5 ms·

Yes seconding this. If you want a broad view of ML IMHO the best places to look at are conference proceedings. The typical review process is imperfect so that s

by abhgh 2y ago

Yes seconding this. If you want a broad view of ML IMHO the best places to look at are conference proceedings. The typical review process is imperfect so that still doesn't show you all the interesting work out there (which you mention), but it is still a start wrt diversity of research. I follow LLMs closely but then going through proceedings means I come across exciting research like these [1],[2],[3]. References: [1] A grad.-based way to optimize axis-parallel and oblique decision trees: the Tree Alternating Optimization (TAO) algorithm https://proceedings.neurips.cc/paper_files/paper/2018/file/185c29dc24325934ee377cfda20e414c-Paper.pdf https://proceedings.neurips.cc/paper_files/paper/2018/file/1.... An extension was the softmax tree https://aclanthology.org/2021.emnlp-main.838/ https://aclanthology.org/2021.emnlp-main.838/. [2] XAI explains models, but can you recommend corrective actions? FACE: feasible and Actionable Counterfactual Explanations https://arxiv.org/pdf/1909.09369 https://arxiv.org/pdf/1909.09369, Algorithmic Recourse: from Counterfactual Explanations to Interventions https://arxiv.org/pdf/2002.06278 https://arxiv.org/pdf/2002.06278 [3] OBOE: Collaborative Filtering for AutoML Model Selection https://arxiv.org/abs/1808.03233 https://arxiv.org/abs/1808.03233

godelski 2y ago
Honestly, these days I just rely on arxiv. The conferences are so noisy that it is hard to really tell what's useful and what's crap. Twitter is a bit better but still a crap shoot. So as far as it seems to me, there's no real good signal to use to differentiate. And what's the point of journals/conferences if not to provide some reasonable signal? If it is a slot machine, it is useless. And I feel like we're far too dismissive of instances we see where good papers get rejected. We're too dismissive of the collusion rings. What am I putting in all this time to write and all this time to review (and be an emergency reviewer) if we aren't going to take some basic steps forward? Fuck, I've saved a Welling paper from rejection from two reviewers who admitted to not knowing PDEs, and this was a workshop (should have been accepted into the main conference). I think review works for those already successful, who can p̶a̶y̶ "perform more experiments when requested" their way out of review hell, but we're ignoring a lot of good work simply for lack of m̶o̶n̶e̶y̶ compute. It slows down our progress to reach AGI.
- abhgh 2y ago
  Yes arxiv is a good first source too. I mentioned conferences as a way to get exposed to diversity, but not necessarily (sadly) merit. It has been my experience as an author and reviewer both that review quality has plummeted over the years for the most part. As a reviewer I had to struggle with the ills of "commission and omission" both, i.e., (a) convince other reviewers to see an idea (from a trendy area such as in-context learning) as not novel (because it has been done before, even in the area of LLMs), and (b) see an idea as novel, which wouldn't haven't seemed so initially because some reviewers weren't aware of the background or impact of anything non-LLM, or god forbid, non-DL. As an author this has personally affected me because I had to work on my PhD remotely, so I didn't have access to a bunch of compute and I deliberately picked a non-DL area, and I had to pay the price for that in terms of multiple rejections, reviewer ghosting, journals not responding for years (yes, years).
  - godelski 2y ago
    I've stopped considering novelty at all. The only thing I now consider is if the precise technique has been done before. If not, well I've seen pretty small things change results dramatically. The pattern I've seen that scares me more is that when authors do find simple but effective changes, they end up convoluting the ideas because simplicity and clarity is often confused with novelty. And honestly, revisiting ideas is useful as our environments change. So I don't want to discourage this type of work. Personally, this has affected me as a late PhD student. Late in the literal sense as I'm not getting my work pushed out (even some SOTA stuff) because of factors like these and my department insists something is wrong with me but will not read my papers, the reviews, or suggest what I need to do besides "publish more." (Literally told to me, "try publishing 5 papers a year, one should get in.") You'll laugh at this, I pushed a paper into a workshop and a major complaint was that I didn't give enough background on StyleGAN because "not everyone would be familiar with the architecture." (while I can understand the comment, 8 pages is not much room when you gotta show pictures on several datasets. My appendix was quite lengthy and included all requested information). We just used a GAN as a proxy because diffusion is much more expensive to train (most common complaints are "not enough datasets" and "how's it scale"). I think this is the reason so many universities use pretrained networks instead of training things from scratch, which just railroads research. (I also got a paper double desk rejected. First because it was "already published." Took a 2 months for them to realize it was arxiv only. Then they fixed that and rejected again because "didn't cite relevant works" with no mention of what those works were... I've obviously lost all faith in the review process)

by abhgh 2y ago

godelski 2y ago

Honestly, these days I just rely on arxiv. The conferences are so noisy that it is hard to really tell what's useful and what's crap. Twitter is a bit better but still a crap shoot. So as far as it seems to me, there's no real good signal to use to differentiate. And what's the point of journals/conferences if not to provide some reasonable signal? If it is a slot machine, it is useless. And I feel like we're far too dismissive of instances we see where good papers get rejected. We're too dismissive of the collusion rings. What am I putting in all this time to write and all this time to review (and be an emergency reviewer) if we aren't going to take some basic steps forward? Fuck, I've saved a Welling paper from rejection from two reviewers who admitted to not knowing PDEs, and this was a workshop (should have been accepted into the main conference). I think review works for those already successful, who can p̶a̶y̶ "perform more experiments when requested" their way out of review hell, but we're ignoring a lot of good work simply for lack of m̶o̶n̶e̶y̶ compute. It slows down our progress to reach AGI.

abhgh 2y ago
Yes arxiv is a good first source too. I mentioned conferences as a way to get exposed to diversity, but not necessarily (sadly) merit. It has been my experience as an author and reviewer both that review quality has plummeted over the years for the most part. As a reviewer I had to struggle with the ills of "commission and omission" both, i.e., (a) convince other reviewers to see an idea (from a trendy area such as in-context learning) as not novel (because it has been done before, even in the area of LLMs), and (b) see an idea as novel, which wouldn't haven't seemed so initially because some reviewers weren't aware of the background or impact of anything non-LLM, or god forbid, non-DL. As an author this has personally affected me because I had to work on my PhD remotely, so I didn't have access to a bunch of compute and I deliberately picked a non-DL area, and I had to pay the price for that in terms of multiple rejections, reviewer ghosting, journals not responding for years (yes, years).
- godelski 2y ago
  I've stopped considering novelty at all. The only thing I now consider is if the precise technique has been done before. If not, well I've seen pretty small things change results dramatically. The pattern I've seen that scares me more is that when authors do find simple but effective changes, they end up convoluting the ideas because simplicity and clarity is often confused with novelty. And honestly, revisiting ideas is useful as our environments change. So I don't want to discourage this type of work. Personally, this has affected me as a late PhD student. Late in the literal sense as I'm not getting my work pushed out (even some SOTA stuff) because of factors like these and my department insists something is wrong with me but will not read my papers, the reviews, or suggest what I need to do besides "publish more." (Literally told to me, "try publishing 5 papers a year, one should get in.") You'll laugh at this, I pushed a paper into a workshop and a major complaint was that I didn't give enough background on StyleGAN because "not everyone would be familiar with the architecture." (while I can understand the comment, 8 pages is not much room when you gotta show pictures on several datasets. My appendix was quite lengthy and included all requested information). We just used a GAN as a proxy because diffusion is much more expensive to train (most common complaints are "not enough datasets" and "how's it scale"). I think this is the reason so many universities use pretrained networks instead of training things from scratch, which just railroads research. (I also got a paper double desk rejected. First because it was "already published." Took a 2 months for them to realize it was arxiv only. Then they fixed that and rejected again because "didn't cite relevant works" with no mention of what those works were... I've obviously lost all faith in the review process)