7 ms·
Gradients are not all you need
- unlikelymordant 4y agoMy one wish is that machine learning papers would use paper titles that actually described what the paper was about. I suppose there is a certain 'evolutionary pressure' where clever titles 'outcompete' dryer, more descriptive titles (or it seems that way). But i don't like it.
- optimalsolver 4y agoI'd like to imagine quirky titles make them harder for other researchers to find, leading to lower citation counts, but that may be wishful thinking.
- bundie 4y agoI think this trend is happening because of the entire buzz around OPENAI LLMs and that 2017 Google Research Paper
- rsfern 4y agoIronically, attention is what you need to get high citation counts Whether quirky titles help or hurt with that i think depends on name recognition and the publication venue
- skybrian 4y agoI don’t think it makes much difference. If anything, it might help? It’s easier to search for a paper if you remember its name. If not, you can search on author or words in the abstract. The problem isn’t quirky titles, it’s with websites like Hacker News that only display a headline and not the abstract.
- hikingsimulator 4y agoThere is a lot of jargon in ML, an example is found in the object detection literature where you will often find sentences like such: "we present a novel simple non-hierarchical feature network backbone with window-shifted attention based learning accommodating neck+regression head fine-tuning or masked cascade RCNN second stage." I'm half joking. Surveys are often a godsend.
- alephxyz 4y agoYou forgot to toss in a few "state-of-the-art"
- chas 4y agoThe major ML conferences all have pretty tight page limits, so more expository sentences usually get cut. This also means that papers usually only explain how their work is different from previous work, so they assume you are familiar with the papers they cite or are willing to read the cited papers. This means that people who have an up-to-date knowledge of a given subfield can quickly get a lot out of a new papers. Unfortunately, it also means that it usually takes a pretty decent stack of papers to get up to speed on a new subfield since you have to read the important segments of the commonly cited papers in order to gain the common knowledge that papers are being diffed against. Traditionally, this issue is solved by textbooks, since the base set of ideas in a given field or subfield is pretty stable. ML has been moving pretty fast in recent years, so there is still a sizable gap between the base knowledge required for productive paper reading and what you can get out of a textbook. For example, Goodfellow et al [1] is a great intro to the core ideas of deep learning, but it was published before transformers were invented, so it doesn’t mention them at all. [1] https://www.deeplearningbook.org/ https://www.deeplearningbook.org/
- visarga 4y ago> "Understanding Limitations and Chaos-Based Failures in Gradient-Based Optimization Methods." (gpt4) Fixed with ML.
- xpe 4y agoI’ve provided the PDF URL to ChatGPT 4.0 and asked it to summarize the article with alternative titles, but for some reason it keeps getting the original title, authors, abstract, and body wrong. What prompt did you use?
- visarga 4y agoI asked a bunch of things. So initially I posted the abstract and title, then prompted just: "Explain". Then I asked "Give me background knowledge on spectrum of the Jacobian" and "Explain the title of the paper" and in the end "Reformulate the title in a more explicit manner". Maybe you can skip directly to the last prompt.
- wcoenen 4y agoThat's because chatgpt doesn't have the ability to retrieve PDFs from the internet. (Unless maybe if you have early access to the version with plugins?) Bing chat does have the ability to read opened PDFs when used from the Edge side bar.
- satvikpendem 4y agoThe clever titles are more brandable. See the citations for "Attention is all you need" or "Chinchilla limit" versus more mundane titles.
- rolisz 4y agoI'm pretty sure Attention is all you need would have gotten a lot of citations even if it had a "boring" title. It was a groundbreaking paper with lots of good ideas.
- cubefox 4y agoThe Chinchilla paper was called "Training Compute-Optimal Large Language Models", which is exactly on point.
- dragonwriter 4y agoI mean, it would be slightly more accurate if it had been “Compute-Optimal Training of Large Language Models”, since the models so-trained aren’t the thing that is compute-optimal, the training is compute-optimal. But… yeah, its hardly a title chosen for marketing rather than description.
- sroussey 4y agoBut we all know what you mean when you say “the Chinchilla paper”
- moritzwarhier 4y agoStupid question, is training considered a part of the model? Or is this only common parlance for GPT (the P).
- cubefox 4y agoInterestingly, the models themselves also have compute cost associated with them, namely the cost of running them (inference). The smaller a model is, the less compute it needs for inference. Which is an interesting trade-off, because you can overtrain (rather than undertrain, as in the past) a model, i.e. use more tokens, fewer parameters, and ultimately more compute than optimal during training, to get lower inference cost. https://www.harmdevries.com/post/model-size-vs-compute-overhead/ https://www.harmdevries.com/post/model-size-vs-compute-overh... This website has an interesting graph which visualizes this: > For example, the compute overhead for 75% of the optimal model size is only 2.8%, whereas for half of the optimal model size, the overhead rises to 20%. As we move towards smaller models, we observe an asymptotic trend, and at 25% of the compute-optimal model size, the compute overhead increases rapidly to 188%. So if you train your model you shouldn't just look at compute-optimal training but try to anticipate how much the model will probably be used for inference, to minimize total compute cost. Basically, the less inference you expect to do with your model, the closer the training should be to Chinchilla-optimality.
- sho_hn 4y agoRemember the days after Rowhammer and Heartbleed, when every new security vulnerability needed its own catchy name and domain name website? This is the science version of that. Branding is eating the world.
- preommr 4y ago> Branding is eating the world. Not realizing that it always has is why it's still effective. History is written by the victors and victors tend to have the best marketting.
- deleted 4y ago[deleted]
- mumblemumble 4y agoIt's inescapable. 40 years ago, when articles went into print publications, you'd just get your paper into a key print journal and then trust that everyone who gets it would at least look through the article headlines and read the abstracts of articles that seemed relevant to them. And it was manageable because you'd only have a few new issues rolling in per month. But arXiv had an average of 167 CS papers being submitted per day in 2021. An academic who wants to keep their career alive needs to resort to every trick in the book to be heard above that din.
- V__ 4y agoIsn't this more a problem of not good enough curation? There are more papers now than ever, but the signal is probably getting worse and worse.
- mumblemumble 4y agoHow does one, practically speaking, curate article publication in a world with arXiv? Curation kind of requires gatekeepers, and good gatekeeping is a full-time job that needs to be paid for. In the days of print journals, there was both a motivation, because you can only print so many articles per month due to materials costs, and a steady revenue stream to pay for it, because everyone has to pay for the journals to get any access to articles at all. The Internet's been eroding both of those factors for a good quarter century now.
- dauertewigkeit 4y agoAt some point you run into the problem where titles become useless because there are 100 papers on the same exact topic with very slight variation in the title. At this point people use surnames and date to cite papers. But then the title can become something catchy that will give you more visibility.
- scrubs 4y agoAgree -- owl talk of Winnie-the-pooh childhood fame --- sucks.
- xpe 4y agoGetting cited is probably the largest individual incentive. To various degrees, authors also want to get their ideas and reputation “out there”. To that end, authors want their work to be (i) skimmed (the abstract at least); (ii) shared; (iii) read. A catchy title almost always helps (right?); there doesn’t seem to be significant downside to catchy titles. So how do we get more desirable system behavior? It seems we have a collective action problem.
- tough 4y agoI asked GPT > https://arxiv.org/abs/2111.05803 https://arxiv.org/abs/2111.05803 make a good useful title that's not silly for this paper and wht's about I dunno if it even read the article lols ==== Title: “Deep Reinforcement Learning for Multi-Agent Navigation in Complex Environments” This paper proposes a deep reinforcement learning approach for multi-agent navigation in complex environments. The proposed method uses a centralized critic and decentralized actor architecture, where each agent has its own policy network and learns from its local observations, while the critic network takes in the global state information to provide a centralized value estimate. The method is evaluated on a variety of benchmark environments and achieves state-of-the-art performance compared to existing methods.
- ajb 4y agoThere's quite a long history of these titles, even before ML: "Sometime’ is Sometimes ‘Not Never’" - Lamport "Reflections on trusting trust" - Thompson "On the cruelty of really teaching computer science" - Dijkstra I'm sure there are more
- abhgh 4y agoYour comment reminded me this talk [1] by David Duvenaud where he discusses his work on Neural ODEs, introduced in the paper [2] titled "Neural Ordinary Differential Equations" (this was one of the Best Papers in Neurips 2018). Apparently the original name of the paper was "Training Infinitesimally-Layered Neural Networks by Backpropagating through Black-box ODE Solvers". I found that to be much better description for the paper. It seems he thinks so too, but eventually the catch-phrase-y title won out. In the video [1], this bit is at 00:48-01:22. PS: [1] is a good talk in general where he discusses some of the limitations of the paper and things that could have been done better. [1] https://www.youtube.com/watch?v=YZ-_E7A3V2w https://www.youtube.com/watch?v=YZ-_E7A3V2w [2] https://papers.nips.cc/paper_files/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf https://papers.nips.cc/paper_files/paper/2018/file/69386f6bb...
- nextaccountic 4y agoThis also happens with security vulnerabilities as well (Heartbleed, POODLE, GHOST, Shellshock, FREAK, CRIME, SMACK)
- 0xBABAD00C 4y ago> Gradients Are Not All You Need Sometimes you need to peek at the Hessian. Seriously though, what is intelligence if not creative unrolling of the first few terms of the Taylor expansion?
- esafak 4y agoOf what?
- mnky9800n 4y ago`a + ax + ax^2+...`
- esafak 4y agoThat's the expansion. What does it represent?
- cgio 4y agoYes. (And it goes down all the way)
- mnky9800n 4y agoNo that's turtles.
- mnky9800n 4y agoGiven the context of the original statement, the Taylor series expansion represents the statistical learning process of any topic anywhere.
- esafak 4y agoI think you mean loss function, not process. And I'm failing to make any connection between intelligence and polynomial approximations to loss functions.
- modeless 4y agoSeems to me like the whole history of neural nets is basically crafting models with well-behaved gradients to make gradient descent work well. That, and models that can achieve high utilization of available hardware. The surprising thing is that models exist where the gradients are so well-behaved that we can learn GPT-4 level stuff.
- smonn_ 4y agoThere's plenty of interesting neural network designs out there but they're being overshadowed by transformers due to their recent success. I personally thing that the main reason transformers work so well is because they actually step away from the multi layer perceptron stuff and introduce some structure and in a way sparsity.
- mumblemumble 4y agoAlso, multi-head attention strikes me as being about as close to how language semantics seems to actually work in human brains as I've seen. Lots of caveats there, of course. First off, I don't know much about the neurology, I just have an amateur interest in second language acquisition research that sometimes brings me into contact with this sort of thing. On the ANN side, which is closer to my actual wheelhouse, we definitely don't actually have any way of knowing if the actual mechanism is all that close, and I'm guessing it probably isn't even close since ANN's don't actually work that similarly to brains. Nor does it need to be, but, intuitively, there's still something promising about an ANN architecture that's vaguely capable of mimicking the behavior of modules in an existing system (human brains) that's well known to be capable of doing the job. I'm not super wild about the bidirectional recurrent layers, either, because they impose some restrictions that clearly aren't great, such as the hard limit on input size. et cetera. But it still strikes me as another big step in a good direction.
- smonn_ 4y agoI'm currently working on a variation of a spiking neural network that learns by making and purging connections between neurons, which so far has been pretty interesting, though I am having a hard time getting it to output anything more than just the patterns it recognised. I did play around with adding its outputs to the input list, making it sort of recurrent but its practically impossible to decode anything thats going on inside of the network. Im thinking of tracking the inputs around to see what its doing right now, might be interesting to see it generate some sort of tree-like structure.
- asdfman123 4y ago> chaos based failure mode I studied this in undergrad, but it’s not the same thing the paper is talking about
- Der_Einzige 4y agoGlobal optimization techniques which don't rely on gradients seems theoretically superior in all instances, except that we haven't found super fast ways to run these kinds of optimizers. The cartpoll demo famously tripped up derivative based reinforcement learning for awhile.
- wenc 4y ago> Global optimization techniques which don't rely on gradients seems theoretically superior in all instances, except that we haven't found super fast ways to run these kinds of optimizers. Did you mean "Global optimization techniques which do rely on gradients..."? Because exact gradient-based global optimization (GBD or branch-and-bound based) methods for general nonconvex problems are theoretically superior (bounding with McCormick relaxations etc.) but also more challenging to practically deploy than say stochastic methods or metaheuristics like local search.
- thatcherc 4y agoCould you point me toward any more info or keywords on "the cartpoll demo famously tripped up derivative based reinforcement learning for awhile"? It sounds like an interesting bit of history but my google searches are only bringing up python tutorials.
- tangjurine 4y agoChatgpt: What does this refer to: cartpoll demo famously tripped up derivative based reinforcement learning The phrase "cartpole demo famously tripped up derivative-based reinforcement learning" is likely referring to a classic problem in the field of reinforcement learning, which involves balancing a pole on a cart. The pole is attached to the cart via a hinge, and the goal is to keep the pole upright by moving the cart left or right in response to its angle. This problem is often used as a benchmark for testing reinforcement learning algorithms. The phrase suggests that derivative-based reinforcement learning algorithms, which rely on computing gradients of a function with respect to its parameters, were not successful at solving this problem. This could be due to the fact that the problem is highly non-linear and requires precise control, which may be difficult to achieve with gradient-based methods. Edit: bard got it too, with more detail, which is surprising
- msackmann 4y agoInteresting paper, thanks for bringing this up! I have been working on methods for trajectory optimization using both, analytic gradient computations and black box stochastic gradient approximations (proximal policy optimization). I was always wondering about a question that is touched in the paper: despite the analytic gradient computation being intuitively more efficient and mathematically correct, it is much harder to learn a policy with it than with the “brute force trial-and-error” black box methods. This paper brings many new perspectives on why.