A Mathematical Model For Meat Cooking (2019): https://arxiv.org/pdf/1908.10787.pdf
Ask HN: What's the best paper you've read in 2020?
11–20 of 197 posts
Re: Ask HN: What's the best paper you've read in 2020?
#12The pair of these papers: (Don't read them in full.)
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)
Re: Ask HN: What's the best paper you've read in 2020?
#13For me, it was "Erasure Coding in Windows Azure Storage" from Microsoft Research (2016) [0] 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 runn…
The primary reason why you should be using 3x or higher replication is the read throughput (which makes it only really relevant for magnetic storage). If the data is replicated 1.6x then there's only 1.6 magnetic disk heads per each file byte. If you replicate it 6x then there's 6 magnetic disk heads for each byte. At ~15x it becomes cheaper to store in SSD with ~1.5x reed-solomon/erasure code overhead since SSD has ~10x the per-byte cost of HDD.
(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).
Re: Ask HN: What's the best paper you've read in 2020?
#14Some CogSci & Neuro papers I found interesting in 2020:
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.
Re: Ask HN: What's the best paper you've read in 2020?
#15A Mathematical Model For Meat Cooking (2019): https://arxiv.org/pdf/1908.10787.pdf
The tittle reminds me of this http://up.csail.mit.edu/science-of-cooking/home-screen.html
Re: Ask HN: What's the best paper you've read in 2020?
#16For me, it was "Erasure Coding in Windows Azure Storage" from Microsoft Research (2016) [0] 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 runn…
It's not even the reduction in storage costs in this paper that is groundbreaking. They talk about a way to not only reduce storage costs, but optimize for repairs. Repairs are costly at scale and reducing resources where possible: network, cpu, disk reads, etc is ideal.
Re: Ask HN: What's the best paper you've read in 2020?
#17No second thought about it, stylegan2[1] takes the cake.
Re: Ask HN: What's the best paper you've read in 2020?
#18"Hypoxic radiosensitization: Adored and Ignored"
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."
Re: Ask HN: What's the best paper you've read in 2020?
#19The pair of these papers: (Don't read them in full.) 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 ha…
I am following Tommi Jakkola in this space too. Also would recommended Ameet Talwalkar's group, esp the papers with Gregory Plumb like MAPLE [1].
[1] [PDF] https://papers.nips.cc/paper/2018/file/b495ce63ede0f4efc9eec...