actually many, because in 2020 i have enough time to read more
Ask HN: What's the best paper you've read in 2020?
171–180 of 197 posts
Re: Ask HN: What's the best paper you've read in 2020?
#172What's Wrong with Computational Notebooks? Pain Points, Needs, and Design Opportunities
https://web.eecs.utk.edu/~azh/pubs/Chattopadhyay2020CHI_Note...
Re: Ask HN: What's the best paper you've read in 2020?
#173https://web.stanford.edu/group/dlab/media/papers/chenNBT2020... 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 s…
Very interesting but only on/off stimulation is possible, there is no spatial resolution.
7mm is crazy deep.
Re: Ask HN: What's the best paper you've read in 2020?
#174Three papers stick out for me in the IML / participatory machine learning space this year: 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 ma…
Thank you! We have very similar interests! Especially the first one and the interactive sound recognition one. Any other work in IML you'd recommend?
Alot of IML seems to focus on building interfaces, so this one was pretty good:
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
Re: Ask HN: What's the best paper you've read in 2020?
#175The 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…
Some more links provided at a NeurIPS tutorial this week
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
Re: Ask HN: What's the best paper you've read in 2020?
#176This Nature paper, 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 mu…
So we could, in principle, selectively demethylate those regions of DNA and cure some cancers?
Cancer isn't caused by abnormal methylation patterns.
The ctDNA are just fragments of DNA which have been shed by the tumor and are now circulating in the blood stream.
Re: Ask HN: What's the best paper you've read in 2020?
#177For 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…
Erasure coding was already used in storage tapes in 1985. > some clever information theory tricks is mind bending to me It's a pretty trivial first-degree linear function, y = ax + b
And why all the downvotes?
Re: Ask HN: What's the best paper you've read in 2020?
#178MDMA Increases Cooperation and Recruitment of Social Brain Areas When Playing Trustworthy Players in an Iterated Prisoner's Dilemma
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.
Re: Ask HN: What's the best paper you've read in 2020?
#179Monte Carlo Geometry Processing:
A Grid-Free Approach to PDE-Based Methods on Volumetric Domains
https://www.cs.cmu.edu/~kmcrane/Projects/MonteCarloGeometryP...
Re: Ask HN: What's the best paper you've read in 2020?
#180Three papers stick out for me in the IML / participatory machine learning space this year: 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 ma…
These do seem interesting, thanks for sharing. 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 think "participatory" means something similar here within an ML context. It favors building community-based algorithmic systems and focuses on lowering the barrier to participation, so that non-expert users can be involved during the machine learning development cycle.
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/