"Estimating the number of unseen species: A bird in the hand is worth log(n) in the bush" https://arxiv.org/abs/1511.07428 https://www.pnas.org/content/113/47/13283

It deals with the classic, and wonderful, question of "If I go and catch 100 birds, and they're from 20 different species, how many species are left uncaught?" There's more one can say about that than it might first appear and it has plenty of applications. But mostly I just love the name. Apparently PNAS had them change it for the final publication, sadly.

Big game from an organization with that acronym.

https://imgur.com/oRF0OD3 https://imgur.com/UtCZL5A

Set a team of experts to find bugs independently for some specified amount of time. Then look at how many of the same bugs were found by multiple experts.

If most of the bugs were found by multiple experts, then there are probably not that many more bugs than the total number that they found. If most of the bugs were found by only one expert, then there are probably a lot more than the total that they found.

With some math you can pin down the 'probablies' to numeric ranges.

Just wondering because this rule of thumb sounds intuitively wrong to me. Depending on the difficulty of the bugs and the skill levels of the experts, it seems possible for them to find every "easy" bug at least twice while having none of them finding the hardest bug even once. (real world example would be some obscure zero-day security bug)

The actual guarantee from the result is not that the number of unobserved "species" is small, but that the total population of all unobserved species is small. If you go back to the birds example, then you could say something like "at most 0.1% of all birds are from species that we haven't identified" but maybe those 0.1% of birds are from a million different species each with incredibly tiny populations. In the code bug example, the very rare species would be the bugs that are very unlikely to be found, i.e. it's more about estimating how many more bugs you will find if you continue to analyze it than how many are really there.

With python code.

https://www.cambridge.org/core/services/aop-cambridge-core/c...

I've always hated build systems. Stuff cobbled together that barely works, yet a necessary step towards working software. This paper showed me there's hope. If we take build systems seriously, we can come up with something much better than most systems out there.

I've recently started learning C++ and have had to grapple with the complexities of CMake. I'm only a few pages into the paper but it's already done a great job at distilling the problem domain and the core components of a build system.

I also found the beginning to be a great introduction to `make` and the build dependency tree.

Non-invasive early detection of cancer four years before conventional diagnosis using a blood test

https://www.nature.com/articles/s41467-020-17316-z

Major breakthrough in cell-free diagnostics. The methylation pattern of DNA can be used to identify early-stage cancer, i.e. circulating tumor DNA (ctDNA) has a distinct methylation pattern.

The results are based on data from a ten year study which must have cost a fortune to run.

The ctDNA are just fragments of DNA which have been shed by the tumor and are now circulating in the blood stream.

So now after we know that, what are the treatment options at that stage ?

My understanding is that the prognosis is much better the earlier one begins treatment.

1.Attention is not explanation (https://arxiv.org/abs/1902.10186)

2.Attention is not not Explanation (https://arxiv.org/abs/1908.04626)

Goes to show the complete lack of agreement between researchers in the explainability space. Most popular packages (allen NLP, google LIT, Captum) use saliency based methods (Integrated gradients) or Attention. The community has fundamental disagreements on whether they capture anything equivalent to importance as humans would understand it.

An entire community of fairness, ethics and Computational social science is built on top of conclusions using these methods. It is a shame that so much money is poured into these fields, but there does not seem to be as strong a thrust to explore the most fundamental questions themselves.

(my 2 cents: I like SHAP and the stuff coming out of Bin Yu and Tommi Jakkola's labs better..but my opinion too is based in intuition without any real rigor)

Explanation: why the model (the deep neural net, that is) is doing what it's doing.

Attention: a particular technique used in certain deep net models, invented a few years ago, that originally showed remarkable performance improvements in NLP (natural language processing) tasks, but has recently been applied in vision and elsewhere.

https://explainml-tutorial.github.io/papers

I saw the first paper mentioned in the tutorial chat.

What is your impression of anchor? https://github.com/marcotcr/anchor

[1] [PDF] https://papers.nips.cc/paper/2018/file/b495ce63ede0f4efc9eec...

There are lots of good bits, such as: 'On the practical side, not only is there no age at which humans are performing at peak on all cognitive tasks, there may not be an age at which humans perform at peak on most cognitive tasks. Studies that compare the young or elderly to “normal adults” must carefully select the “normal” population.' (italics in original)

This seems to me to comport with the research suggesting that most or all of the variance in IQ across the life span can be accounted for by controlling for mental processing speed; i.e., you are generally faster when you are younger, but you are not more correct when you are younger.

As far as I can tell, this section might be one of those facts that people try not to think about too much. I don't worry about it, but I end up thinking about it a lot.

There was recently some headline news about an older mathematician that made a significant breakthrough. Other than that one outlier, have there been many important contributions made by people after the age of, say, 45?

--

"I had better say something here about this question of age, since it is particularly important for mathematicians. No mathematician should ever allow himself to forget that mathematics, more than any other art or science, is a young man's game. To take a simple illustration at a comparatively humble level, the average age of election to the Royal Society is lowest in mathematics.

We can naturally find much more striking illustrations. We may consider, for example, the career of a man who was certainly one of the world's three greatest mathematicians. Newton gave up mathematics at fifty, and had lost his enthusiasm long before; he had recognized no doubt by the time that he was forty that his great creative days were over. His greatest ideas of all, fluxions and the law of gravitation, came to him about 1666, when he was twenty-four—'in those days I was in the prime of my age for invention, and minded mathematics and philosophy more than at any time since'. He made big discoveries until he was nearly forty (the 'elliptic orbit' at thirty-seven), but after that he did little but polish and perfect.

Galois died at twenty-one, Abel at twenty-seven, Ramanujan at thirty-three, Riemann at forty. There have been men who have done great work a good deal later; Gauss's great memoir on differential geometry was published when he was fifty (though he had had the fundamental ideas ten years before). I do not know an instance of a major mathematical advance initiated by a man past fifty. If a man of mature age loses interest in and abandons mathematics, the loss is not likely to be very serious either for mathematics or for himself.

On the other hand the gain is no more likely to be substantial; the later records of mathematicians who have left mathematics are not particularly encouraging. Newton made a quite competent Master of the Mint (when he was not quarrelling with anybody). Painlevé was a not very successful Premier of France. Laplace's political career was highly discreditable, but he is hardly a fair instance, since he was dishonest rather than incompetent, and never really 'gave up' mathematics. It is very hard to find an instance of a first-rate mathematician who has abandoned mathematics and attained first-rate distinction in any other field.1 There may have been young men who would have been first-rate mathematicians if they had stuck to mathematics, but I have never heard of a really plausible example. And all this is fully borne out by my own very limited experience. Every young mathematician of real talent whom I have known has been faithful to mathematics, and not from lack of ambition but from abundance of it; they have all recognized that there, if anywhere, lay the road to a life of any distinction.

1 Pascal seems the best."

However, as of 2011[1] the mean age of physics Nobel winners at the time of their achievements across the entire period of the award was 37.2 and since 1985 the mean age was 50.3.

According to the same paper, by the year 2000, Nobel-level achievement in physics before age 40 was only 19% of cases. It also appears that awards in chemistry and medicine are similarly increasing in mean age.

Is this dispositive? Certainly not. Maybe the Nobel committee prefers to award old scientists because of some unknown bias?

However, it does indicate that high achievement is both possible and normal in middle age and beyond.

[1] https://www.pnas.org/content/108/47/18910.full

This is one of the reasons that I'm personally so excited about (and working on) the potential of spatially immersive media like VR to understand complex concepts. Taking a step back, tools like a graph plot enabled humans to understand complex concepts like differentials and projectile motion at a much younger age. Could a breakthrough with new ways of understanding human knowledge effectively do the same with knowledge that is today considered complex (eg, quantum mechanics)? If such a breakthrough happens, could we bring the average age of significant contribution in subjects like physics back down?

I don't know, but I hope so. :)

Edit. I also remember reading thoughts by either Michael Nielsen on the increasing age of Nobel-worthy contributions in physics, but I can't find it in my current sleep deprived state. I shall tomorrow if somebody else hasn't pointed to that article by then.

I've pondered on this problem a bit before. To solve it, I reached your same conclusion, that we need some breakthrough with new ways of understanding human knowledge simplifying knowledge that is today considered complex, or in other words, we need at least try to build some sort of elevator.

IMHO, the most promising possible breaktroughs I could find were:

(I) a reform in math education, with early introduction of schoolchildren to computer algebra system (CAS) software, shifting curriculum away from tedious manual computations and trick learning. When in university, for example, I learned lots of integration tricks, and forgot most of them a few years later. Would my time had better invested just learning SymPy instead of all those tricks? This idea is pushed by Conrad Wolfram. For example. See his talk at https://youtu.be/jE9lU4E52Vg

(II). a reform of physics education to replace vector algebra with proper geometric algebra, as advocated by David Hestenes. Vector algebra as taught in physics today is actually a hack pushed by Gibbs, that only works well in 3D and demands a lot of shoehorning to work in problems with higher dimensionality. Geometric algebra scales well in any number of dimensions, and many problems become easier. The four Maxwell equations, for example, became one. See the discussion in https://physics.stackexchange.com/a/62822 :

"Now, the contention is that Clifford algebra is under-utilized in basic physics. Every problem in rigid-body dynamics is at least as easy when using Clifford algebra as anything else — and most are far easier — which is why you see quaternions being used so frequently. Orbital dynamics (especially eccentricity!) is practically trivial. Relativistic dynamics is simple. Moreover, once you've gotten practice with Clifford algebra in the basics, extending to electrodynamics and the Dirac equation are really simple steps. So I think there's a strong case to be made that this would be a useful approach for undergrads. This could all be done using different tools, of course — that's how most of us learned them. But maybe we could do it better, and more consistently. No one is claiming that Clifford algebra is fundamentally new; just that it could be bundled into a neater package, making for easier learning. Try teaching a kid who is struggling with the direction of the curl vector that s/he should really be thinking in terms of the algebra generated by the (recently introduced) vector space, subject only to the condition that the product of a vector with itself is equal to the quadratic form. Or a kid who can't understand Euler angles that this rotation is better understood as a transformation generated (under a two-fold covering) by the even subalgebra of Cl3,0(R). No one here is arguing that that should happen. GA is just a name for a pedagogical approach that makes these lessons a whole lot easier than they would be if you sent the student off to read Bourbaki. Starting off with GA may be slightly harder at the beginning, but pays enormous dividends once you get to harder problems. And once teachers and textbooks get good at explaining GA, even the introduction will be easier."

There is one other effect beyond ability: people over 45 rarely take up new interests. There are plenty of cases of people who continue working in the same field through their 40s and 50s and continue to make advances, but there are many fewer cases of an individual beginning to work in a field after 40 and going on to make a major discovery. In pure math, possibly the most aesthetic-driven technical field (it is impractical by definition), this effect might be especially strong.

Richard Hamilton (not to be confused with William Rowan Hamilton of action-principle fame) initiated the application of the Ricci flow to the geometrization conjecture in 1982 at 39 and continued his work through the '90s, being credited by Perelman as making crucial contributions to the final solution of the Poincare conjecture in three dimensions. He's probably the most prominent example.

>It is very hard to find an instance of a first-rate mathematician who has abandoned mathematics and attained first-rate distinction in any other field.

Chomsky? Szilard? Maybe even Wolfram?

The idea that you can achieve the same practical effect of a 3x replication factor in a distributed system, but only increasing the cost of data storage by 1.6x, by leveraging some clever information theory tricks is mind bending to me.

If you're operating a large Ceph cluster, or you're Google/Amazon/Microsoft and you're running GCS/S3/ABS, if you needed 50PB HDDs before, you only need 27PB now (if implementing this).

The cost savings, and environmental impact reduction that this allows for are truly enormous, I'm surprised how little attention this paper has gotten in the wild.

[0] https://www.microsoft.com/en-us/research/wp-content/uploads/...

(there are also effects on the tail latency of both read and write, because in a replicated encoding you are less likely to be affected by a single slow drive).

(also, for insane performance which is sometimes needed you can mlock() things into RAM; the per-byte cost of RAM is ~100x the cost of HDD and ~10x the cost of SSD).

However for cold-data, there really hasn't been (or at least I am unaware of) any system that can achieve the combined durability of 1.5x Reed-Solomon codes + 3x replication, with such a small penalty to storage costs.

Like you said though, it's definitely not the thing you'd be doing for things that prioritize performance as aggressively as the use-cases you've suggested.

Also, these days the storage of the data doesn't have to be at the same machine that processes the data. A lot of datacenter setups have basically zero transfer cost (or, alternatively, all the within-DC transfer cost is in the CAPEX required to build the DC in the first place), ultra low latency, and essentially unlimited bandwidth for any within-datacenter communication. This doesn't hold for dc1->dc2 communication, in particular it is very very far from the truth in long distance lines.

One way to think about the above is that datacenters have become the new supercomputers of the IBM era - it's free and really fast to exchange data within a single DC.

Also2, this is completely independent of consistency guarantees. At best it relates to durability guarantees, but that I want from all storage solutions. And yes, properly done reed solomon has the same durability guarantees as plain old replicated setup.

Also to the above also2, single-DC solutions are never really durable as the DC can simply burn down or meet some other tragic end, you need geographic replication if your data cannot be accidentally lost without serious consequences (a lot of data actually can be lost, in particular if it is some kind of intermediate data that can be regenerated from the "source" with some engineering effort). This is not just a theoretical concern, I've seen "acts of God" destroy single-DC setups data, ay least partially. It is pretty rare, though.

> ~1.5x reed solomon is the default these days, again, unless you need read throughput performance

I'm not surprised that Reed-Solomon is the "default these days" given that it exists since the 1960's, and that the most widely available and deployed open-source distributed filesystem is HDFS (which uses Reed-Solomon). However I don't see how that is to be taken as a blind endorsement for it, especially given that the paper in reference explicitly compares itself to Reed-Solomon based systems, including concerns regarding reconstruction costs, performance, and reliability.

> Also, these days the storage of the data doesn't have to be at the same machine that processes the data

Even though what you said here is correct, I don't see how that's relevant to the referenced paper, nor do I think I implied that I hold a contrary belief in any way from what I said.

> Also2, this is completely independent of consistency guarantees

My comment about consistency referred only to the fact that you cannot "simply" spin up more replicas to increase read throughput, because consistent reads often have to aqcuire a lock on systems that enforce stronger consistency, so your comments regarding throughput are not universally true, given that there are many systems where reads cannot be made faster this way, as they are bottle-necked by design.

> Properly done Reed-Solomon has the same durability guarantees as plain old replicated setup

This is not true unless the fragments themselves are being replicated across failure domains, which you seem to address with your next comment with "you need geographic replication if your data cannot be accidentally lost without serious consequences". All of this, however, is directly addressed in the paper as well:

> The advantage of erasure coding over simple replication is that it can achieve much higher reliability with the same storage, or it requires much lower storage for the same reliability. The existing systems, however, do not explore alternative erasure coding designs other than Reed-Solomon codes. In this work, we show that, under the same reliability requirement, LRC allows a much more efficient cost and performance tradeoff than Reed-Solomon.

[1] Clay Codes - https://www.usenix.org/conference/fast18/presentation/vajha . This paper was also implemented on Ceph and the results are shown in the paper.

and, [2] HeART: improving storage efficiency by exploiting disk-reliability heterogeneity - https://www.usenix.org/conference/fast19/presentation/kadeko... . This paper talks about how just one erasure code is not enough and employing code conversions over the disk-reliability we can get up to 30% savings!

https://static.googleusercontent.com/media/research.google.c...

I actually found out about this paper because it was referenced in a slide presentation from Google about Colossus (which is the successor to GFS). GFS indeed uses erasure coding with a 1.5x factor, but erasure coding alone does not guarantee durability, and thus needs to be combined with replication to satisfy that requirement, and erasure coding is not the same thing as replication.

The innovation here is explicitly the combination of a new erasure coding algorithm (LRC) AND replication, with a combined storage amplification that is much lower than the previous SOTA.

The paper explicitly compares the new algorithm (LRC) with GFS and other alternatives, and explains why it's better, so this is really not something that is comparable to the 2003 GFS paper in any way (or to any other prior art really), as this is not just a trivial application of erasure coding in a storage system.

There's also this paper [0] from 2001 which digs a bit deeper into the Erasure Codes vs Replication idea that I can recommend if you're interested

[0] http://fireless.cs.cornell.edu/publications/erasure_iptps.pd...

This is a combination of erasure coding AND replication, whose combined storage amplification is dramatically lower than previous SOTA.

I gave a longer explanation in a sibing comment to yours [1]

[0] http://fireless.cs.cornell.edu/publications/erasure_iptps.pd...

[1] https://news.ycombinator.com/item?id=25351678

By the way, not at all trying to say this paper isn't interesting. I keep it in my filing cabinet to show my colleagues when I need to describe this technique, since Google hasn't ever bothered to describe Colossus in a way I can reference.

https://imgur.com/a/gi2Xdl0

> some clever information theory tricks is mind bending to me

It's a pretty trivial first-degree linear function, y = ax + b

And the earlier paper “A Polymorphic Type System for Extensible Records and Variants” https://web.cecs.pdx.edu/~mpj/pubs/96-3.pdf

Row types are magically good: they serve either records or variants (aka sum types aka enums) equally well and both polymorphically. They’re duals. Here’s a diagram.

              Construction                Inspection

    Records   {x:1} : {x:Int}             r.x — r : {x:Int|r}
              [closed]                    [open; note the row variable r]
    
    Variants  ‘Just 1 : <Just Int|v>      case v of ‘Just 0 -> ...
              [open; note the row var v]  v : <Just Int>
                                          [closed]

https://www.unisonweb.org/ ?

https://arxiv.org/abs/1706.03762

It's from 2017 but I first read it this year. This is the paper that defined the "transformer" architecture for deep neural nets. Over the past few years, transformers have become a more and more common architecture, most notably with GPT-3 but also in other domains besides text generation. The fundamental principle behind the transformer is that it can detect patterns among an O(n) input size without requiring an O(n^2) size neural net.

If you are interested in GPT-3 and want to read something beyond the GPT-3 paper itself, I think this is the best paper to read to get an understanding of this transformer architecture.

This might be misleading, the amount of computation for processing a sequence size N with a vanilla transformer is still N^2. There has been recent work however which has tried to make them scale better.

[1] https://arxiv.org/abs/2009.14794

https://nlp.seas.harvard.edu/2018/04/03/attention.html

I start to understand what you always hear from older ICs about having to work to keep up, or else every undergrad coming out will know things you don't.

Recurrent neural network size is also independent of input sequence length.

The successful removal of inductive bias is really what differentiates this from previous sequence-to-sequence neural networks.

1) Michael, C. J., Acklin, D., & Scheuerman, J. (2020). On interactive machine learning and the potential of cognitive feedback. ArXiv:2003.10365 [Cs]. http://arxiv.org/abs/2003.10365

2) Denton, E., Hanna, A., Amironesei, R., Smart, A., Nicole, H., & Scheuerman, M. K. (2020). Bringing the people back in: Contesting benchmark machine learning datasets. ArXiv:2007.07399 [Cs]. http://arxiv.org/abs/2007.07399

3) Jo, E. S., & Gebru, T. (2020). Lessons from archives: Strategies for collecting sociocultural data in machine learning. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 306–316. https://doi.org/10.1145/3351095.3372829

Also a great read related to IML tooling for audio recognition:

1) Ishibashi, T., Nakao, Y., & Sugano, Y. (2020). Investigating audio data visualization for interactive sound recognition. Proceedings of the 25th International Conference on Intelligent User Interfaces, 67–77. https://doi.org/10.1145/3377325.3377483

Also, what do you mean by "participatory" in the context of machine learning? Is there a seminal paper that defines it?

I ask as in HCI and other fields, participatory had a VERY defined meaning that in short, I'd about equal power, democracy, and inclusivity. I can't understand how that applies to ML and would like to learn more, hence asking you.

I'm not aware of any seminal papers per-say, although here are a few that I've read recently... first one is something I maintain at $DAYJOB:

1) Halfaker, A., & Geiger, R. S. (2020). Ores: Lowering barriers with participatory machine learning in Wikipedia. ArXiv:1909.05189 [Cs]. http://arxiv.org/abs/1909.05189

2) Martin Jr. , D., Prabhakaran, V., Kuhlberg, J., Smart, A., & Isaac, W. S. (2020). Participatory problem formulation for fairer machine learning through community based system dynamics. ArXiv:2005.07572 [Cs, Stat]. http://arxiv.org/abs/2005.07572

Also checkout PAIR: https://research.google/teams/brain/pair/

1) Dudley, J. J., & Kristensson, P. O. (2018). A review of user interface design for interactive machine learning. ACM Transactions on Interactive Intelligent Systems, 8(2), 1–37. https://doi.org/10.1145/3185517

http://www.pnas.org/lookup/doi/10.1073/pnas.1915006117

You might think that it's possible to use machine learning to predict whether people will be successful using established socio-demographic, psychological, and educational metrics. It turns out that it's very hard and simple regression models outperform the fanciest machine learning ideas for this problem.

The way this study was done is also interesting and paves the way for new kinds of collaborative scientific projects that take on big questions. It draws on communities like Kaggle, but applies it to scientific questions not just pure prediction problems.

This reminds me of a classic paper: "Improper linear models are those in which the weights of the predictor variables are obtained by some nonoptimal method; for example, they may be obtained on the basis of intuition, derived from simulating a clinical judge's predictions, or set to be equal. This article presents evidence that even such improper linear models are superior to clinical intuition when predicting a numerical criterion from numerical predictors."

Dawes, R. M. (1979). The robust beauty of improper linear models in decision making. American psychologist, 34(7), 571.

https://core.ac.uk/download/pdf/190386677.pdf

Genetics and net worth can be blown away by a good or bad group of friends. And unfortunately you start having friends before you are concious enough to realize the impact.

"Temporal difference (TD) error is a powerful teaching signal in machine learning"

Fellow HNer seems to have liked a lot of ML paper, this is not breaking the trend. This is a great meta paper questioning the goal of the field itself, and proposing ways to formally evaluate intelligence in a computational sense. Chollet is even ambitious enough to propose a proof of concept benchmark! [2] I also like some out of the box methods people tried to get closer to a solution, like this one combining cellular automata and ML [3]

[1] https://arxiv.org/abs/1911.01547 [2] https://github.com/fchollet/ARC [3] https://www.kaggle.com/arsenynerinovsky/cellular-automata-as...

Also a big fan of Hutter prize. Good AGI is lossless compression.

A good incremental improvement in service level indicator measurements for large-scale cloud services.

Obligatory The Morning Paper post: https://blog.acolyer.org/2020/02/26/meaningful-availability/

At one startup we worked at we had availability problems for some time, with the service going down in a semi-predictable manner ~2 times a day (and the proper bugfix a few weeks away). Because once a day the service went down was in the middle of the night with no one on call, pure availability was 80-90%. Given that it was a single country app with no one trying to do any business during the night, meaningful availability was ~99%. Knowing that gave us peace of mind and made tackling the problem a much more relaxed ordeal than the crunch time for a few weeks I've seen at other companies in similar situations.

In computing theory, when do you actually need coordination to get consistency? They partition the space into two kinds of algorithm, and show that only one kinds needs coordination.

CACM, 9/2020. https://cacm.acm.org/magazines/2020/9/246941-keeping-calm/fu...

Highly recommended reading.

Some pretty mind blowing insights - ex: if you replace one layer's weights in a trained classification network with the initialisation weights for the layer (or some intermediate checkpoint as well), many networks show relatively unaffected performance for certain layers ... which is seen as a generalisation since it amounts to parameter reduction. However, if you replace with fresh random weights (although initialisation state is itself another set of random weights), the loss is high! Some layers are more sensitive to this than others in different network architectures.

I recently summarised this to a friend who asked "what's the most important insight in deep learning?" - to which I said - "in a sufficiently high dimensional parameter space, there is always a direction in which you can move to reduce loss". I'm eager to hear other answers to that question here.

Anyways, one of the papers by Yoshua that was really influential on my master's thesis was published in 2009, has received 8956 citations to date on Google Scholar, called "Learning Deep Architectures for AI". For many young researchers, even though this paper pre-dates much of the hyped architectures of the current era, I would still recommend it for its timeless views on deep representations as equivalent to architectural units of learning and knowledge, including its breakdown of deep networks as compositions of functions.

"Learning Deep Architectures for AI" by Yoshua Bengio (2009) - https://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf

The reason for Bengio demonstrating this on a complete layer is obviously to demonstrate that this is NOT due to redundantly routing information to the next layer (think holographically, for robustness). And using non-ancestor random weights illustrates that the ecosystem fitness suffers if redundant / holographic routing is prevented while also using non ancestral cells / weights...

2) Snowflake and its tiered storage, among other things http://pages.cs.wisc.edu/~yxy/cs839-s20/papers/snowflake.pdf

https://arogozhnikov.github.io/2016/12/19/markov_chain_monte...

Gradualizing the Calculus of Inductive Constructions (https://hal.archives-ouvertes.fr/hal-02896776/)

I'm not sure if this is precisely the direction things should go in order to improve the utilisation of specification within software development but it's a very important contribution. As yet my favourite development style has been with F-star but F-star also leaves me a bit in a lurch when the automatic system isn't able to find the answer. Too much hinting in the case of hard proofs.

Eventually there will be a system that lets you turn the crank up on specification late in the game, allows lots of the assertions to be discharged automatically, and then finally saddles you with the remaining proof obligations in a powerful proof assistant.

Chen, Y.W.; Yiu, C.B.; Wong, K.Y. Prediction of the SARS-CoV-2 (2019-nCoV) 3C-like protease (3CL (pro)) structure: Virtual screening reveals velpatasvir, ledipasvir, and other drug repurposing candidates. F1000Research 2020, 9, 129.

This paper (based on a machine learning-driven open source drug docking tool from Scripps Institute) from Feb/Mar formed the basis for the agriceutical venture I started for supporting pandemic management in Africa. We’re in late stage trialing talks with research institutes here in East Africa.

https://www.emske-phytochem.com

Thought the Naiad project is really cool!

https://cs.stanford.edu/~matei/courses/2015/6.S897/readings/...

An interesting (no pun intended) paper on what makes papers (or anything in general) interesting.

https://www.nature.com/articles/s41587-020-0711-0

It’s fairly accessible to anyone who vaguely remembers their CS theory, and quite fun!

https://www.cs.utah.edu/~regehr/papers/pldi11-preprint.pdf

Automerge [2] implements a variant of this.

[1] https://arxiv.org/abs/1805.04263

[2] https://github.com/automerge/automerge

Which is actually great because it gives me something to read on subjects im not familar with.

It's nearly impossible to have a scientific paper on anything with abstraction. At best you can create some "after the fact" optimizations using time studies and statistics.

Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures [2] applies DFA, an approach to training neural nets without backprop, to modern architectures like the Transformer. It does surprisingly well and is a step in the right direction for biologically plausible neural nets as well as potentially significant efficiency gains.

Hopfield Networks is All You Need [3] analyzes the Transformer architecture as the classical Hopfield Network. This one got a lot of buzz on HN so I won't talk about it too much, but it's part of a slew of other analyses of the Transformer that basically show how generalizable the attention mechanism is. It also sorta confirms many researchers' inkling that Transformers are likely just memorizing patterns in their training corpus.

Edit: Adding a few interesting older NLP papers that I came across this year.

StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding [4]

Do Syntax Trees Help Pre-trained Transformers Extract Information? [5]

Learning to Compose Neural Networks for Question Answering [6]

Parsing with Compositional Vector Grammars [7]

[1] https://arxiv.org/abs/2006.11287

[2] https://arxiv.org/abs/2006.12878

[3] https://arxiv.org/abs/2008.02217

[4] https://arxiv.org/abs/1908.04577

[5] https://arxiv.org/abs/2008.09084

[6] https://arxiv.org/abs/1601.01705

[7] https://www.aclweb.org/anthology/P13-1045/

https://deepmind.com/research/publications/Mastering-Atari-G...

It felt like a baby step towards general intelligence.

https://research.google/pubs/pub48630/

https://www.nationalgeographic.com/science/2020/04/first-spi...

- No-setup collaborative notebooks: near real-time editing on the same notebook. Large Docker images.

- Long-running notebook scheduling: you can schedule a notebook right from the JupyterLab interface and continue to work on your notebook without context switch. The notebook will run and you can view its state even if you close your browser or get disconnected, and you can view it without opening the JupyterLab interface, even on your mobile phone. https://iko.ai/docs/notebook/#long-running-notebooks

- Automatic experiment tracking: iko.ai automatically detects parameters, metrics, and models and saves them. Users don't have to remember or know how to write boilerplate code or experiment tracking code.

- One click parametrization: you can publish an AppBook to enable other people to run your automatically parametrized notebook without being overwhelmed by the interface. You don't have to use cell tags or metadata to specify parameters. You click a button and an application is created from your notebook. The runs of this application are also logged as experiments in case they generate a better model. https://iko.ai/docs/appbook/

- Easy deployment: people can look at the leaderboard, and click on a button to deploy the model they choose into a "REST API" endpoint. They'll be able to invoke it with a POST request or from a form where they simply upload or enter data and get predictions.

We haven't focused on the stylesheets given that in our previous projects with actual paying enterprise customers, it wasn't CSS that held us back.

Here's an invite link that's valid multiple times: https://iko.ai/invite/lEMzE_hKwJ2SUbfLdnK7SbZb1c3zUCOAQexakL...

Note that it has been published on arXiv just yesterday; I helped review an earlier draft.

[1]: https://arxiv.org/abs/2012.04400

Erik Hoel in this paper offers an audacious hypothesis: Our brain, during the its evolution, has developed dreams as a way to solve over-fitting

Since we’re learning from a limited samples of data in the real world, chances of overfitting (I call it judgement) goes higher. In ML we inject randomness and noise to avoid overfitting. Hoel theory can explain why our dreams are so sparse & hallucinatory

https://www.jprasurg.com/article/0007-1226(75)90127-7/pdf

Great read. Note if you're not going to read it that you yourself should not eat 35 eggs per day because these patients had calorie requirements of a little under 7000.

"Equality of Opportunity in Supervised Learning" (https://arxiv.org/abs/1610.02413)

It explain the basic concept about fairness in ML. Very practical exemple in my domain knowledge that show the trade-off between fairness of an algo and overall performance (money). Really make you see what may go wrong with bias in ML. It shows, in my opinion, why we will have to regulate ML as corporation aren't really incentivized to deal with fairness. It also shows that there is different notions of fairness. So there will always be something that feel unfair and also doing something can always be interpreted as positive discrimination.

Deep brain optogenetics without intracranial surgery

"Achieving temporally precise, noninvasive control over specific neural cell types in the deep brain would advance the study of nervous system function. Here we use the potent channelrhodopsin ChRmine to achieve transcranial photoactivation of defined neural circuits, including midbrain and brainstem structures, at unprecedented depths of up to 7 mm with millisecond precision. Using systemic viral delivery of ChRmine, we demonstrate behavioral modulation without surgery, enabling implant-free deep brain optogenetics."

https://groups.csail.mit.edu/genesis/papers/radul%202009.pdf

https://www.acpjournals.org/doi/10.7326/M20-6817

https://booksc.xyz/book/68207697/475742

Even if a bit impractical in some regrards, I think an operating system/cloud that you interact with like a database is something we should aspirationally strive for. We're spending too much time gluing things together and not enough time being productive. Databases are great at tracking and describing resources (much better than YAML) and stored procedures that are like Lambdas would be neat.

A killer paper presenting an algorithm capable of inductive learning. ("DreamCoder solves both classic inductive programming tasks and creative tasks such as drawing pictures and building scenes. It rediscovers the basics of modern functional programming, vector algebra and classical physics, including Newton's and Coulomb's laws.")

http://www.heathershrewsbury.com/dreu2010/wp-content/uploads...

I think it's going to be years before we understand this properly, but in 2020 we are beginning to see practical uses.

At the moment I think it's a toss up: it's either going to be a curiosity that people read, have their mind exploded but can't do anything with or else it's a good chance to be the most influential deep learning paper of the decade.

[1] https://arxiv.org/abs/1803.03635

In this paper, they did that with genes. And the 2d space that was left wasn't meaningless at all. It accurately recreated map of Europe.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2735096/

No one knows what attention is (https://link.springer.com/article/10.3758/s13414-019-01846-w)

- Papers We Love: https://paperswelove.org/

- The Morning Paper: https://blog.acolyer.org/

[edit: formatting]

https://github.com/papers-we-love/papers-we-love#other-good-...

The last thing is that I save every paper that i see mentioned as a pdf file in a seperate folder on my PC. I use a combination of rip grep all + recoll to search them.

Molecular repertoire of Deinococcus radiodurans after 1 year of exposure outside the International Space Station within the Tanpopo mission: https://microbiomejournal.biomedcentral.com/articles/10.1186...

I've been reading up on the object capability security model a lot recently, and was pointed to this paper... I was hooked. A really compelling security model almost from first principles.

https://arxiv.org/abs/1907.07283

https://www.jneurosci.org/content/39/2/307

Abstract ran through a text optimizer:

We administered 100 mg MDMA or placebo to 20 male participants in a double-blind, placebo-controlled, crossover study.

Cooperation with trustworthy, but not untrustworthy, opponents was enhanced following MDMA but not placebo.

Specifically, MDMA enhanced recovery from, but not the impact of, breaches in cooperation.

During trial outcome, MDMA increased activation of four clusters incorporating precentral and supramarginal, gyri, superior temporal cortex, central operculum/posterior insula, and supplementary motor area.

MDMA increased cooperative behavior when playing trustworthy opponents.

Our findings highlight the context-specific nature of MDMA's effect on social decision-making.

While breaches of trustworthy behavior have a similar impact following administration of MDMA compared with placebo, MDMA facilitates a greater recovery from these breaches of trust.

https://pdfs.semanticscholar.org/c26b/4d3156b0c526d16c891ce7...

>"three of the four most cited papers in the journals deal with hypoxia [...] yet its routine clinical use is very limited."

https://people.inf.ethz.ch/asingla/papers/conext19.pdf

'What if we treated AI as equals, like other human beings, not as tools or, worse, slaves to their creators?' That's the premise to this paper, which is a wonderful provocation. It's a really important consideration too, when you consider how many of our decisions we're asking machine sentience to make for us. If algorithmic bias were a human judge, they'd be thrown out of court (you'd hope).

[1] https://arxiv.org/abs/1912.04958

https://www.researchgate.net/publication/342317256_A_systema...

-- because it was relatively straightforward to understand and convert to code. So it helped me understand backprop.

https://www.usenix.org/legacy/event/osdi10/tech/full_papers/...

Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion: https://arxiv.org/abs/2008.12228

http://winsh.me/papers/erlang_workshop_2013.pdf

aka SIREN: https://vsitzmann.github.io/siren/

"Erotic Modesty: (Ad)dressing Female Sexuality and Propriety in Open and Closed Drawers, USA, 1800–1930" https://onlinelibrary.wiley.com/doi/abs/10.1111/1468-0424.00...

When JAmes C. Scott wrote about infrapolitcs in his 1990 work "Domination and the Arts of Resistance: Hidden Transcripts" (https://www.jstor.org/stable/j.ctt1np6zz) and described it as a sort of political resistance that never declares itself and remains beneath what the dominant group can properly perceive until the power shift actually starts to happen, he probably didn't think of a case where the undeclared politics is so literally something not meant to be seen. The theme is very much a progression of how slowly women were able to establish even what parts of their body can be sexualized or not sexualized, and culminates in a sudden burst, or power shift, in the 1910s-1930s after centuries of aggregating individual choices and entirely unseen acts. This particular revolution managed to happen almost entirely outside of organization and public view and while it's by no means over, the progress made in the last twenty years covered in the paper really show how productive the aggregation of individual acts of resistance, done without any open plans, can still bring about so much change. It also showed the limits of such movements, particularly whe the dominant group rhas an active interest in preserving that status quo.

"How Qualified Immunity Fails" https://scholarship.law.nd.edu/cgi/viewcontent.cgi?article=4...

and "The Case Against Qualified Immunity" https://scholarship.law.nd.edu/cgi/viewcontent.cgi?article=4...

These two were both written by UCLA Law professor Johanna Schwartz over the course of about a year and half from 2017-2018, and really got a lot of attention this year when a lot of people for the first time asked "why does it seem impossible to actually hold abusive police to some degree of personal responsibility?" Having worked at a public defender's office and then on federal CJA cases (essentially federal defense work when there is more than one codefendant and the federal defenders would have a conflict of interest defending both), the abusive nature of policing was very much something that I saw constantly for years but it's difficult to quantify just how little potential consequence a police officer may actually face because nobody had done the shoeleather work to collect the data, and police departments tend to have opacity written into their contracts. The actual data collected by Schwartz demonstrating how the multiple layers of shielding negotiated into police contracts and just how much indemnification, which is actually illegal in many jurisdictions but universally ignored, pushes any potential liability onto taxpayers directly, creating a situation where victims' taxes are just getting looped back into the settlements they receive. There are a lot of problems in the criminal justice and really any carceral system this country runs, and most of it are poorly documented on a systemic level and difficult to quantify. It's nice to see that someone put in the work to make the picture a little clearer, as practitioners tend to be entirely focused on their clients to do research like this and this is a particularly unglamorous field of research.

https://susam.github.io/tucl/the-unix-command-language.html

MMR vaccine could protect against COVID-19

https://mbio.asm.org/content/11/6/e02628-20?_ga=2.139230451....

Constantinescu, Alexandra O., Jill X. O’Reilly, and Timothy EJ Behrens. "Organizing conceptual knowledge in humans with a gridlike code." Science 352.6292 (2016): 1464-1468.

Kriegeskorte, Nikolaus, and Katherine R. Storrs. "Grid cells for conceptual spaces?." Neuron 92.2 (2016): 280-284.

Klukas, Mirko, Marcus Lewis, and Ila Fiete. "Efficient and flexible representation of higher-dimensional cognitive variables with grid cells." PLOS Computational Biology 16.4 (2020): e1007796.

Moser, May-Britt, David C. Rowland, and Edvard I. Moser. "Place cells, grid cells, and memory." Cold Spring Harbor perspectives in biology 7.2 (2015): a021808.

Quiroga, Rodrigo Quian. "Concept cells: the building blocks of declarative memory functions." Nature Reviews Neuroscience 13.8 (2012): 587-597.

Stachenfeld, Kimberly L., Matthew M. Botvinick, and Samuel J. Gershman. "The hippocampus as a predictive map." Nature neuroscience 20.11 (2017): 1643.

Buzsáki, György, and David Tingley. "Space and time: The hippocampus as a sequence generator." Trends in cognitive sciences 22.10 (2018): 853-869.

Umbach, Gray, et al. "Time cells in the human hippocampus and entorhinal cortex support episodic memory." bioRxiv (2020).

Eichenbaum, Howard. "On the integration of space, time, and memory." Neuron 95.5 (2017): 1007-1018.

Schiller, Daniela, et al. "Memory and space: towards an understanding of the cognitive map." Journal of Neuroscience 35.41 (2015): 13904-13911.

Rolls, Edmund T., and Alessandro Treves. "The neuronal encoding of information in the brain." Progress in neurobiology 95.3 (2011): 448-490.

Fischer, Lukas F., et al. "Representation of visual landmarks in retrosplenial cortex." Elife 9 (2020): e51458.

Hebart, Martin, et al. "Revealing the multidimensional mental representations of natural objects underlying human similarity judgments." (2020).

Ezzyat, Youssef, and Lila Davachi. "Similarity breeds proximity: pattern similarity within and across contexts is related to later mnemonic judgments of temporal proximity." Neuron 81.5 (2014): 1179-1189.

Seger, Carol A., and Earl K. Miller. "Category learning in the brain." Annual review of neuroscience 33 (2010): 203-219.

Neurolinguistics:

Marcus, Gary F. "Evolution, memory, and the nature of syntactic representation." Birdsong, speech, and language: Exploring the evolution of mind and brain 27 (2013).

Dehaene, Stanislas, et al. "The neural representation of sequences: from transition probabilities to algebraic patterns and linguistic trees." Neuron 88.1 (2015): 2-19.

Fujita, Koji. "On the parallel evolution of syntax and lexicon: A Merge-only view." Journal of Neurolinguistics 43 (2017): 178-192.

Here's a cool paper on bird song during lockdown.

Derryberry, Elizabeth P., et al. Singing in a silent spring: Birds respond to a half-century soundscape reversion during the COVID-19 shutdown (2020) (DOI: 10.1126/science.abd5777)

I'm not an expert though, just curious about "mind computations".

Pulvermüller, Friedemann. "Words in the brain's language." Behavioral and brain sciences 22.2 (1999): 253-279.

Pulvermüller, Friedemann. "Brain embodiment of syntax and grammar: Discrete combinatorial mechanisms spelt out in neuronal circuits." Brain and language 112.3 (2010): 167-179.

Buzsáki, György. "Neural syntax: cell assemblies, synapsembles, and readers." Neuron 68.3 (2010): 362-385.

Lau, Ellen F., Colin Phillips, and David Poeppel. "A cortical network for semantics:(de) constructing the N400." Nature Reviews Neuroscience 9.12 (2008): 920-933.

-->On cognitive maps, spacial & abstract navigation:

Bellmund, Jacob LS, et al. "Navigating cognition: Spatial codes for human thinking." Science 362.6415 (2018).

Peer, Michael, et al. "Processing of different spatial scales in the human brain." ELife 8 (2019): e47492.

Kriegeskorte, Nikolaus, and Rogier A. Kievit. "Representational geometry: integrating cognition, computation, and the brain." Trends in cognitive sciences 17.8 (2013): 401-412.

Mok, Robert M., and Bradley C. Love. "A non-spatial account of place and grid cells based on clustering models of concept learning." Nature communications 10.1 (2019): 1-9.

Chrastil, Elizabeth R., and William H. Warren. "From cognitive maps to cognitive graphs." PloS one 9.11 (2014): e112544.

-->On graph navigation and 'rich club' networks:

Watts, Duncan J., and Steven H. Strogatz. "Collective dynamics of ‘small-world’networks." nature 393.6684 (1998): 440-442.

Kleinberg, Jon M. "Navigation in a small world." Nature 406.6798 (2000): 845-845.

Ball, Gareth, et al. "Rich-club organization of the newborn human brain." Proceedings of the National Academy of Sciences 111.20 (2014): 7456-7461.

Malkov, Yury A., and Alexander Ponomarenko. "Growing homophilic networks are natural navigable small worlds." PloS one 11.6 (2016): e0158162.

Givoni, Inmar, Clement Chung, and Brendan J. Frey. "Hierarchical affinity propagation." arXiv preprint arXiv:1202.3722 (2012).

--> On concept formation, memory & generalization

Bowman, Caitlin R., and Dagmar Zeithamova. "Abstract memory representations in the ventromedial prefrontal cortex and hippocampus support concept generalization." Journal of Neuroscience 38.10 (2018): 2605-2614.

Garvert, Mona M., Raymond J. Dolan, and Timothy EJ Behrens. "A map of abstract relational knowledge in the human hippocampal–entorhinal cortex." Elife 6 (2017): e17086.

Collin, Silvy HP, Branka Milivojevic, and Christian F. Doeller. "Hippocampal hierarchical networks for space, time, and memory." Current opinion in behavioral sciences 17 (2017): 71-76.

Kumaran, Dharshan, et al. "Tracking the emergence of conceptual knowledge during human decision making." Neuron 63.6 (2009): 889-901.

DeVito, Loren M., et al. "Prefrontal cortex: role in acquisition of overlapping associations and transitive inference." Learning & Memory 17.3 (2010): 161-167.

Gallistel, Charles Randy, and Louis D. Matzel. "The neuroscience of learning: beyond the Hebbian synapse." Annual review of psychology 64 (2013): 169-200.

Martin, A., and W. K. Simmons. "Structural Basis of semantic memory." Learning and Memory: A Comprehensive Reference. Elsevier, 2007. 113-130.

Zeithamova, Dagmar, Margaret L. Schlichting, and Alison R. Preston. "The hippocampus and inferential reasoning: building memories to navigate future decisions." Frontiers in human neuroscience 6 (2012): 70.

Tenenbaum, Joshua B., and Thomas L. Griffiths. "Generalization, similarity, and Bayesian inference." Behavioral and brain sciences 24.4 (2001): 629.