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Articole: 15

  1. The Future of Software

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    The world of software is undergoing a shift not seen since the advent of compilers in the 1970s.

  2. Deep Learning is Powerful Because It Makes Hard Things Easy - Reflections 10 Years On

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    Ten years ago this week, I wrote a provocative and bold post that blew up, made it to top spot on HackerNews.

  3. Discrete Diffusion: Continuous-Time Markov Chains

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    This post, intended to be the first in a series related to discrete diffusion models, has been sitting in my drafts for months.

  4. We may finally crack Maths. But should we?

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    Automating mathematical theorem proving has been a long standing goal of artificial intelligence and indeed computer science.

  5. Mortal Komputation: On Hinton's argument for superhuman AI.

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    Last week in Cambridge was Hinton bonanza.

  6. Autoregressive Models, OOD Prompts and the Interpolation Regime

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    A few years ago I was very much into maximum likelihood-based generative modeling and autoregressive models (see this, this or this).

  7. We May be Surprised Again: Why I take LLMs seriously.

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    "Deep Learning is Easy, Learn something Harder" - I proclaimed in one of my early and provocative blog posts from 2016.

  8. Implicit Bayesian Inference in Large Language Models

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    This intriguing paper kept me thinking long enough for me to I decide it's time to resurrect my blogging (I started writing this during ICLR review period, and realised it might be a good idea to wait until that's concluded) Sang Michael Xie, Aditi Raghun…

  9. Eastern European Guide to Writing Reference Letters

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    Excruciating.

  10. Causal inference 4: Causal Diagrams, Markov Factorization, Structural Equation Models

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    This post is written with my PhD student and now guest author Patrik Reizinger and is part 4 of a series of posts on causal inference: Part 1: Intro to causal inference and do-calculus Part 2: Illustrating Interventions with a Toy Example Part 3: Counterf…

  11. On Information Theoretic Bounds for SGD

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    Few days ago we had a talk by Gergely Neu, who presented his recent work: Gergely Neu Information-Theoretic Generalization Bounds for Stochastic Gradient Descent I'm writing this post mostly to annoy him, by presenting this work using super hand-wavy intu…

  12. Notes on the Origin of Implicit Regularization in SGD

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    I wanted to highlight an intriguing paper I presented at a journal club recently: Samuel L Smith, Benoit Dherin, David Barrett, Soham De (2021) On the Origin of Implicit Regularization in Stochastic Gradient Descent There's actually a related paper that c…

  13. An information maximization view on the $\beta$-VAE objective

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    guest post with Dóra Jámbor This is a half-guest-post written jointly with Dóra, a fellow participant in a reading group where we recently discussed the original paper on $\beta$-VAEs: Irina Higgins et al (ICLR 2017): $\beta$-VAE: Learning Basic Visual Co…

  14. Some Intuition on the Neural Tangent Kernel

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    Neural tangent kernels are a useful tool for understanding neural network training and implicit regularization in gradient descent.

  15. Notes on Causally Correct Partial Models

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    I recently encountered this cool paper in a reading group presentation: Rezende et al (2020) Rezende Causally Correct Partial Models for Reinforcement Learning It's frankly taken me a long time to understand what was going on, and it took me weeks to write