Out of topic, but could you suggest a good resource for papers? I am interested in software mostly
Here are a couple:
- Papers We Love: https://paperswelove.org/
- The Morning Paper: https://blog.acolyer.org/
[edit: formatting]
Ask HN: What's the best paper you've read in 2020?
81–90 of 197 posts
Re: Ask HN: What's the best paper you've read in 2020?
#82Here's a wonderful one I read a little over a year ago: "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 tha…
Is it similar to the german tank problem?
https://www.eadan.net/blog/german-tank-problem/
With python code.
Re: Ask HN: What's the best paper you've read in 2020?
#83Scott Aaron son’s paper “The Busy Beaver Frontier”. https://www.scottaaronson.com/papers/bb.pdf
It’s fairly accessible to anyone who vaguely remembers their CS theory, and quite fun!
Re: Ask HN: What's the best paper you've read in 2020?
#84I recently made public a personal project https://42papers.com to surface the top trending papers to read.
Re: Ask HN: What's the best paper you've read in 2020?
#85Some 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 represe…
also on neurolinguistics:
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.
Re: Ask HN: What's the best paper you've read in 2020?
#86Network topology design at 27,000 km/hour (2017) by
Debopam Bhattacherjee, Ankit Singla
Re: Ask HN: What's the best paper you've read in 2020?
#87Attention Is All You Need 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 detec…
It's clearly important but I found that paper hard to follow. The discussion in AIMA 4th edition was clearer. (Is there an even better explanation somewhere?)
I found it difficult to read too. Here's an annotated version, with code, which helps:
Re: Ask HN: What's the best paper you've read in 2020?
#88A paper that profoundly influenced my language design: “Programming with Polymorphic Variants” https://caml.inria.fr/pub/papers/garrigue-polymorphic_varian...
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 : case v of ‘Just 0 -> ...
[open; note the row var v] v :
[closed]
Re: Ask HN: What's the best paper you've read in 2020?
#89It's an old paper by now and probably niche but was very useful to me this year.
Re: Ask HN: What's the best paper you've read in 2020?
#90Attention Is All You Need 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 detec…
“it can detect patterns among an O(n) input size without requiring an O(n^2) size neural net” 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.
n^2 time yes, but not n^2 weights in the net.