Posts
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As Rocks May Think
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Leaving 1X
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Ultra Instinct
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Motor Physics
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Questions about ARC Prize
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All Roads Lead to Robotics
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Takeaways from DeepMind's RoboCat Paper
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Can LLMs Critique and Iterate on Their Own Outputs?
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How Can We Make Robotics More like Generative Modeling?
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All Roads Lead to Rome: The Machine Learning Job Market in 2022
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Haikus about Effective Altruism
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Ranking YC Companies with a Neural Net
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Leaving Google Brain ✌️
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To Understand Language is to Understand Generalization
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Just Ask for Generalization
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Robots Must Be Ephemeralized
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ML Mentorship: Some Q/A about RL
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Stonks are What You Can Get Away With
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Sovereign Arcade: Currency as High-Margin Infrastructure
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Science and Engineering for Learned Robots
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Don't Mess with Backprop: Doubts about Biologically Plausible Deep Learning
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How to Understand ML Papers Quickly
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Software and Hardware for General Robots
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My Criteria for Reviewing Papers
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Chaos and Randomness
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Free Office Hours for Non-Traditional ML Researchers
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Three Questions that Keep Me Up at Night
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Selected Quotes from "The Dark Ages of AI Panel Discussion" (1984)
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Differentiable Path Tracing on the GPU/TPU
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Robinhood, Leverage, and Lemonade
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Tips for Training Likelihood Models
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Normalizing Flows in 100 Lines of JAX
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Lessons from AI Research Projects: The First 3 Years
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Fun with Snapchat's Gender Swapping Filter
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What I Cannot Control, I Do not Understand
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Meta-Learning in 50 Lines of JAX
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Thoughts on the BagNet Paper
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Uncertainty: A Tutorial
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Dijkstra's, in Disguise
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Bots and Thoughts from ICRA2018
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Aesthetically Pleasing Learning Rates
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Teacup: A Short Story
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Doing a Concurrent Masters at Brown
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Normalizing Flows Tutorial, Part 2: Modern Normalizing Flows
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Normalizing Flows Tutorial, Part 1: Distributions and Determinants
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Gamma Correction
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Expressivity, Trainability, and Generalization in Machine Learning
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Strong AI Ideas in Crystal Nights (Greg Egan, 2009)
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Summary of NIPS 2016
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Tutorial: Categorical Variational Autoencoders using Gumbel-Softmax
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How Can a Deep Neural Network with ReLU Activations Approximate any Function?
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Riemann Summation and Physics Simulation are Statistically Biased
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Monte Carlo Variance Reduction Techniques in Julia
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A Beginner's Guide to Variational Methods: Mean-Field Approximation
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Adversarial Exploration Policies for Robust Model Learning
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Understanding and Implementing Deepmind's DRAW Model
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Generative Adversarial Nets in TensorFlow: Part I
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My Internship Experiences at Pixar, Google, and Two Sigma
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Reverse-Engineering Apps: a Step-by-Step Beginner's Guide
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