The Curious Engineer

All blogs / AI / ML

Eric Jang

evjang.com

Personal website and blog of Eric Jang.

AI / MLEssays / personal

’15’20’25
Posts
61 from 14 Jan 2014 to 10 Sep 2026
Posting
now and then (4 in the last year)
Kind
personal blog
Feed
https://evjang.com/feed.xml

2026 4

  1. Smooth Exponentials for Robotics
  2. AutoGo: a Tutorial
  3. As Rocks May Think
  4. Leaving 1X

2025 1

  1. Ultra Instinct

2024 3

  1. Motor Physics
  2. Questions about ARC Prize
  3. All Roads Lead to Robotics

2023 2

  1. Takeaways from DeepMind's RoboCat Paper
  2. Can LLMs Critique and Iterate on Their Own Outputs?

2022 5

  1. How Can We Make Robotics More like Generative Modeling?
  2. All Roads Lead to Rome: The Machine Learning Job Market in 2022
  3. Haikus about Effective Altruism
  4. Ranking YC Companies with a Neural Net
  5. Leaving Google Brain ✌️

2021 9

  1. To Understand Language is to Understand Generalization
  2. Just Ask for Generalization
  3. Robots Must Be Ephemeralized
  4. ML Mentorship: Some Q/A about RL
  5. Stonks are What You Can Get Away With
  6. Sovereign Arcade: Currency as High-Margin Infrastructure
  7. Science and Engineering for Learned Robots
  8. Don't Mess with Backprop: Doubts about Biologically Plausible Deep Learning
  9. How to Understand ML Papers Quickly

2020 5

  1. Software and Hardware for General Robots
  2. My Criteria for Reviewing Papers
  3. Chaos and Randomness
  4. Free Office Hours for Non-Traditional ML Researchers
  5. Three Questions that Keep Me Up at Night

2019 10

  1. Selected Quotes from "The Dark Ages of AI Panel Discussion" (1984)
  2. Differentiable Path Tracing on the GPU/TPU
  3. Robinhood, Leverage, and Lemonade
  4. Tips for Training Likelihood Models
  5. Normalizing Flows in 100 Lines of JAX
  6. Lessons from AI Research Projects: The First 3 Years
  7. Fun with Snapchat's Gender Swapping Filter
  8. What I Cannot Control, I Do not Understand
  9. Meta-Learning in 50 Lines of JAX
  10. Thoughts on the BagNet Paper

2018 8

  1. Uncertainty: A Tutorial
  2. Dijkstra's, in Disguise
  3. Bots and Thoughts from ICRA2018
  4. Aesthetically Pleasing Learning Rates
  5. Teacup: A Short Story
  6. Doing a Concurrent Masters at Brown
  7. Normalizing Flows Tutorial, Part 1: Distributions and Determinants
  8. Normalizing Flows Tutorial, Part 2: Modern Normalizing Flows

2017 4

  1. Gamma Correction
  2. Expressivity, Trainability, and Generalization in Machine Learning
  3. Strong AI Ideas in Crystal Nights (Greg Egan, 2009)
  4. Summary of NIPS 2016

2016 7

  1. Tutorial: Categorical Variational Autoencoders using Gumbel-Softmax
  2. How Can a Deep Neural Network with ReLU Activations Approximate any Function?
  3. Riemann Summation and Physics Simulation are Statistically Biased
  4. Monte Carlo Variance Reduction Techniques in Julia
  5. A Beginner's Guide to Variational Methods: Mean-Field Approximation
  6. Adversarial Exploration Policies for Robust Model Learning
  7. Understanding and Implementing Deepmind's DRAW Model

2015 2

  1. Generative Adversarial Nets in TensorFlow: Part I
  2. My Internship Experiences at Pixar, Google, and Two Sigma

2014 1

  1. Reverse-Engineering Apps: a Step-by-Step Beginner's Guide