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Home - colah's blog
Recent Exciting Things!
Multimodal Neurons
On Distill
Circuits
On Distill
Neural Networks (General)
Neural Networks, Manifolds, and Topology
Deep Learning, NLP, and Representations
Calculus on Computational Graphs: Backpropagation
Neural Networks, Types, and Functional Programming
Recurrent Neural Networks
Understanding LSTM Networks
Attention and Augmented Recurrent Neural Networks
On Distill
Convolutional Neural Networks
Conv Nets
A Modular Perspective
Understanding Convolutions
Groups & Group Convolutions
Deconvolution and Checkerboard Artifacts
On Distill
Visualizing Neural Networks
Visualizing MNIST
An Exploration of Dimensionality Reduction
Visualizing Representations
Deep Learning and Human Beings
Inceptionism
Going Deeper into Neural Networks
On the Google Research Blog
Four Experiments in Handwriting with a Neural Network
On Distill
Feature Visualization
How neural networks build up their understanding of images
On Distill
The Building Blocks of Interpretability
On Distill
Differentiable Image Parameterizations
On Distill
Activation Atlases
On Distill
Circuits
On Distill
Understanding RL Vision
On Distill
Multimodal Neurons
On Distill
Transformer Circuits
Individual Circuits Articles
Zoom In: An Introduction to Circuits
On Distill
An Overview of Early Vision in InceptionV1
On Distill
Curve Detectors
On Distill
Naturally Occurring Equivariance in Neural Networks
On Distill
High-Low Frequency Detectors
On Distill
Curve Circuits
On Distill
Visualizing Weights
On Distill
Miscellaneous
Fanfiction, Graphs, and PageRank
Data.List Recursion Illustrated
Visual Information Theory
Research Debt
On Distill
The Paths Perspective on Value Learning
On Distill
Rough Notes
Writing rough notes allows me share more content, since polishing takes lots of time. While I hope it's useful, it's likely lower quality and less carefully considered than my usual articles.
- Research Taste Exercises
Five exercises for building research taste (and three failure modes). - Interpretability vs Neuroscience
Six major advantages which make artificial neural networks easier to study than biological ones. - Analogies between Biology and Deep Learning
Unusual analogies between biology and deep learning research.
Personal Writing
- Micromarriages
A unit for romantic success (mostly humor). - Collaboration & Credit Principles
How can we be good stewards of collaborative trust? - Do I Need to Go to University?
Instead of asking “Is university good?”, ask “Do I have something more compelling to do?” - Notes on Habbema et al. (2015): "Realizing a Desired Family"
- Matchmaking Party
A small amount of time matchmaking might be life changing for our friends. - Dating Info
Normal online dating seems pretty suboptimal, so I just wrote a public dating profile.
Selected Twitter Threads
Sometimes I write twitter threads as a low-effort way to express something I'd have written an essay about if I had more time.
- On Wanting Kids
I really deeply want to have kids, to an extent I rarely see men talk about. - The Elegance of ML is the Elegance of
Biology
One of the main aesthetics driving my research. - The Wonders of Atmospheric
Dynamics
Like bio/chem/physics, meteorology has beautiful simple theories. - Research Intimacy
Deeply internalizing knowledge and ways of thinking related to a research topic. - Why is fantasy so emotionally compelling
to many of us?
Magic as a metaphor for the humane and intimate aspects missing in technology. - Reflections on Gilead
Thoughts on one of my favorite novels. Kindness, loneliness, and blessing. - Winter in San Francisco
San Francisco has seasons. The winter is breathtakingly beautiful. - Integrity and Relationships Based on Values vs
Beliefs
Can we reconcile wanting to make those close to us proud with moral independence? - Against Polarization in the “Do Neural Networks
Understand” Debate
A lack of nuanced intermediate positions makes this conversation confusing.
Traditional Papers
- Document embedding with paragraph vectors
- TensorFlow: Large-scale machine learning on heterogeneous systems
- Concrete Problems in AI Safety
- Conditional Image Synthesis with Auxiliary Classifier GANs
- Changing Model Behavior at Test-Time Using Reinforcement Learning
- Is Generator Conditioning Causally Related to GAN Performance?