Ravi Kalia

machine learning, aws, generative AI, mlops. Retweets are not endorsements. PhD.

[Ravi Kalia @ravkalia1]

@chipro What tools (packages, frameworks) do you use to make your blog please? Also have you stopped blogging forever, I ask since your content is superb.

[Naval @naval]

When You Truly Work for Yourself

[Tanishq Mathew Abraham, Ph.D. @iScienceLuvr]

A new YouTube video by Welch Labs that gives an awesome walk-through of how multi-latent attention works (one of the innovations of DeepSeek)!

Check it out!

[Carlos Alberto Haro @h1sort]

this is one of the best papers I’ve read about MLOps

[François Chollet @fchollet]

One of the reasons making things is satisfying is that it's a concrete demonstration of the notion that you can have some amount of control over your environment and circumstances

[Bo Wang @BoWang87]

Biology is probably the only place with unlimited fossil fuel for AI 🔥🔥🔥

[Jürgen Schmidhuber @SchmidhuberAI]

Yet another award for plagiarism. Of all the papers that could have won the #NeurIPS2024 Test of Time Award, it had to be the #NeurIPS 2014 paper on "Generative Adversarial Networks" [GAN1]. This is the notorious paper that republished the 1990 principle of Artificial Curiosity [AC90,AC90b,AC20,AC][R2] under a new name without citing it. See references [AC90,AC90b] (1990-91) that were published when compute was about 100,000 times more expensive than in 2014: two neural nets (NNs) fight each other. A control network with adaptive stochastic Gaussian units (a generative model) generates output data. This output is fed into a predictor NN which learns by gradient descent to predict the effects of the outputs. However, in a minimax game, the first NN maximizes the loss minimized by the second NN. See Section "Implementing Dynamic Curiosity and Boredom" of [AC90,AC90b]: it mentions preliminary experiments where (in absence of external reward) the predictor minimizes a linear function of what the generator maximizes.

Even later surveys by the authors failed to cite the original work [DLP].

One year later, in 1991, there was also another 2-network adversarial system called "Predictability Minimization" [PM0,PM1] for creating disentangled representations. The 2014 GAN paper [GAN1] cites it, but wrongly claims that PM is NOT a minimax game. However, PM experiments from 1991 [PM0,PM1] and 1996 [PM2] (with images) are of the minimax type. The 2014 GAN authors have never corrected their 2014 paper [DLP].

There is a peer-reviewed journal publication on this priority dispute [AC20]. (Other early adversarial machine learning settings since 1959 [S59][H90] were very different - they neither involved self-supervised NNs where one NN sees the output of another generative NN and tries to predict its consequences, nor were about modeling data, nor used gradient descent [AC20].)

Of course, it is well known that plagiarism can be either "unintentional" or "intentional or reckless" [PLAG1-6], and the more innocent of the two may very well be partially the case here [NOB][DLP]. But science has a well-established way of dealing with "multiple discovery" and plagiarism - be it unintentional [PLAG1-6][CONN21] or not [FAKE,FAKE2] - based on facts such as time stamps of publications and patents. The deontology of science requires that unintentional plagiarists correct their publications through errata and then credit the original sources properly in the future. The awardees didn't; instead the awardees kept collecting citations for inventions of other researchers [DLP]. Isn't this behaviour intentional plagiarism as it turns even unintentional plagiarism [PLAG1-6] into an intentional form [FAKE2][NOB][DLP]?

Some people have lost their titles or jobs due to plagiarism. Harvard's former president resigned after some drama [PLAG7][NOB]. But how can advisors from the field of Machine Learning now continue to tell their PhD students that they should avoid plagiarism?

REFERENCES

[GAN1] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio. Generative adversarial nets. NIPS 2014, 2672-2680, Dec 2014. A description of GANs that does not cite J. Schmidhuber's original GAN principle of 1990 [AC90,AC90b][AC20][AC][DLP] (also containing wrong claims about J. Schmidhuber's separate adversarial NNs for Predictability Minimization [PM1-2][AC20][DLP]).

[AC90] J. Schmidhuber. Making the world differentiable: On using self-supervised fully recurrent neural networks for dynamic reinforcement learning and planning in non-stationary environments. Technical Report FKI-126-90, TUM, Feb 1990, revised Nov 1990. The first paper on planning with reinforcement learning recurrent neural networks (NNs) and on generative adversarial networks where a generator NN is fighting a predictor NN in a minimax game.

[AC90b] J. Schmidhuber. A possibility for implementing curiosity and boredom in model-building neural controllers. In J. A. Meyer and S. W. Wilson, editors, Proc. of the International Conference on Simulation of Adaptive Behavior: From Animals to Animats, pages 222-227. MIT Press/Bradford Books, 1991. Based on [AC90].

[AC20] J. Schmidhuber. Generative Adversarial Networks are Special Cases of Artificial Curiosity (1990) and also Closely Related to Predictability Minimization (1991). Neural Networks, Volume 127, p 58-66, 2020.

[AC] J. Schmidhuber (AI Blog, 2021). Artificial Curiosity & Creativity Since 1990-91.

[PM0] J. Schmidhuber. Learning factorial codes by predictability minimization. TR CU-CS-565-91, Univ. Colorado at Boulder, 1991.

[PM1] J. Schmidhuber. Learning factorial codes by predictability minimization. Neural Computation, 4(6):863-879, 1992.

[PM2] J. Schmidhuber, M. Eldracher, B. Foltin. Semilinear predictability minimzation produces well-known feature detectors. Neural Computation, 8(4):773-786, 1996.

[R2] Reddit/ML, 2019. J. Schmidhuber really had GANs in 1990.

[S59] A. L. Samuel. Some studies in machine learning using the game of checkers. IBM Journal on Research and Development, 3:210-229, 1959.

[H90] W. D. Hillis. Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D: Nonlinear Phenomena, 42(1-3):228-234, 1990.

[DLP] J. Schmidhuber (2023). How 3 Turing awardees republished key methods and ideas whose creators they failed to credit. Technical Report IDSIA-23-23, Swiss AI Lab IDSIA, 14 Dec 2023.

[NOB] J. Schmidhuber (Dec 2024). A Nobel Prize for Plagiarism. Technical Report IDSIA-24-24. Sadly, the Nobel Prize in Physics 2024 for Hopfield & Hinton is a Nobel Prize for plagiarism. They republished methodologies developed in Ukraine and Japan by Ivakhnenko and Amari in the 1960s & 1970s, as well as other techniques, without citing the original papers. Even in later surveys, they didn't credit the original inventors (thus turning what may have been unintentional plagiarism into a deliberate form). None of the important algorithms for modern Artificial Intelligence were created by Hopfield & Hinton.

[PLAG1] Oxford's guide to types of plagiarism (2021). Quote: "Plagiarism may be intentional or reckless, or unintentional."

[PLAG2] Jackson State Community College (2022). Unintentional Plagiarism.

[PLAG3] R. L. Foster. Avoiding Unintentional Plagiarism. Journal for Specialists in Pediatric Nursing; Hoboken Vol. 12, Iss. 1, 2007.

[PLAG4] N. Das. Intentional or unintentional, it is never alright to plagiarize: A note on how Indian universities are advised to handle plagiarism. Perspect Clin Res 9:56-7, 2018.

[PLAG5] InfoSci-OnDemand (2023). What is Unintentional Plagiarism?

[PLAG6] Copyrighted dot com (2022). How to Avoid Accidental and Unintentional Plagiarism (2023). Quote: "May it be accidental or intentional, plagiarism is still plagiarism."

[PLAG7] Cornell Review, 2024. Harvard president resigns in plagiarism scandal. 6 January 2024.

[FAKE] H. Hopf, A. Krief, G. Mehta, S. A. Matlin. Fake science and the knowledge crisis: ignorance can be fatal. Royal Society Open Science, May 2019. Quote: "Scientists must be willing to speak out when they see false information being presented in social media, traditional print or broadcast press" and "must speak out against false information and fake science in circulation and forcefully contradict public figures who promote it."

[FAKE2] L. Stenflo. Intelligent plagiarists are the most dangerous. Nature, vol. 427, p. 777 (Feb 2004). Quote: "What is worse, in my opinion, ..., are cases where scientists rewrite previous findings in different words, purposely hiding the sources of their ideas, and then during subsequent years forcefully claim that they have discovered new phenomena."

[ℏεsam @Hesamation]

graph neural nets: underrated af

[Ravi Kalia @ravkalia1]

@karpathy Geometric learning?

[Ravi Kalia @ravkalia1]

@fchollet Ensemble?

[Surag Nair @suragnair]

In-silico saturation mutagenesis (ISM) of all human regulatory regions with a long-range (and large) model like Enformer can take many GPU-months. In the updated version of fastISM, we show that ISM for Enformer can be sped up by a factor of ~3x. Check it out!

[Mikhael Dito Manurung @mikhaeldito313]

R-Python interoperability for #singlecell multi-omics is just...painful. Am I missing some handy tool out there?

#scrnaseq

[Kevin Patrick Murphy @sirbayes]

I am delighted to announce that my new book, “Probabilistic Machine Learning: An Introduction”, is finally available in print format! You can order it from https://t.co/fx92WBQvk3, or from Amazon. Also available at https://t.co/dSlKkwYpLr 1/4

[Lior Pachter @lpachter]

If single-cell RNA-seq was taking the class for grades the parents would be called in. h/t @sinabooeshaghi https://t.co/lpyhTiknSA

[Iannis Aifantis @iannisaifantis1]

Rahul and I hope to see you all in Cancun this May! The meeting is fantastic...considering that we all lost two years of our lives ...imagine the advances that took place in the single cell biology field during these two years! @nyuniversity #Aifantislab @satijalab

[Sahil Chhabra @_sahilchhabra]

I haven’t enjoyed reading something this much in a long time as much as I did the white paper on the topic of “Geometric Deep Learning” by @mmbronstein @joanbruna @TacoCohen and @PetarV_93. Here are a few reasons and takeaways from my perspective: 1/16
https://t.co/FNyrlXYkJJ

[Michael Bronstein @mmbronstein]

In a new paper with @AnaMariaCretu5 @frederickmonti Stefano Marrone @epomqo @yvesalexandre published today in @NatureComms we show how interaction data can be used to uniquely identify individuals over long periods of time--strong implications for data anonymization and privacy

[Kevin K. Yang 楊凱筌 @KevinKaichuang]

Use a pretrained protein language model + trRosettaX for improved protein structure prediction from single sequences.

Wenkai Wang, Zhenling Peng, Jianyi Yan

https://t.co/90koNV75yZ

[Rahul Satija @satijalab]

Interested in single cell genomics but need help getting started? Check out my lab's Single Cell Genomics Day on March 4. Talks will feature recent exciting computational and experimental advances and will be livestreamed at https://t.co/zG98gkckMU. Please RT/spread the word!