The Curious Engineer

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Distill

distill.pub

Articles about Machine Learning

AI / ML

’16’17’18’19’20’21’22’23’24’25’26
Posts
51 from 8 Sep 2016 to 2 Sep 2021
Posting
quiet since 2021
Kind
magazine
Feed
https://distill.pub/rss.xml

2021 11

  1. Understanding Convolutions on Graphs
  2. A Gentle Introduction to Graph Neural Networks
  3. Distill Hiatus
  4. Adversarial Reprogramming of Neural Cellular Automata
  5. Weight Banding
  6. Branch Specialization
  7. Multimodal Neurons in Artificial Neural Networks
  8. Self-Organising Textures
  9. Visualizing Weights
  10. Curve Circuits
  11. High-Low Frequency Detectors

2020 12

  1. Naturally Occurring Equivariance in Neural Networks
  2. Understanding RL Vision
  3. Communicating with Interactive Articles
  4. Thread: Differentiable Self-organizing Systems
  5. Self-classifying MNIST Digits
  6. Curve Detectors
  7. Exploring Bayesian Optimization
  8. An Overview of Early Vision in InceptionV1
  9. Visualizing Neural Networks with the Grand Tour
  10. Zoom In: An Introduction to Circuits
  11. Growing Neural Cellular Automata
  12. Visualizing the Impact of Feature Attribution Baselines

2019 15

  1. Computing Receptive Fields of Convolutional Neural Networks
  2. The Paths Perspective on Value Learning
  3. A Discussion of 'Adversarial Examples Are Not Bugs, They Are Features'
  4. A Discussion of 'Adversarial Examples Are Not Bugs, They Are Features': Adversarial Example Researchers Need to Expand What is Meant by 'Robustness'
  5. A Discussion of 'Adversarial Examples Are Not Bugs, They Are Features': Robust Feature Leakage
  6. A Discussion of 'Adversarial Examples Are Not Bugs, They Are Features': Two Examples of Useful, Non-Robust Features
  7. A Discussion of 'Adversarial Examples Are Not Bugs, They Are Features': Adversarially Robust Neural Style Transfer
  8. A Discussion of 'Adversarial Examples Are Not Bugs, They Are Features': Adversarial Examples are Just Bugs, Too
  9. A Discussion of 'Adversarial Examples Are Not Bugs, They Are Features': Learning from Incorrectly Labeled Data
  10. A Discussion of 'Adversarial Examples Are Not Bugs, They Are Features': Discussion and Author Responses
  11. Open Questions about Generative Adversarial Networks
  12. A Visual Exploration of Gaussian Processes
  13. Visualizing memorization in RNNs
  14. Activation Atlas
  15. AI Safety Needs Social Scientists

2018 4

  1. Distill Update 2018
  2. Differentiable Image Parameterizations
  3. Feature-wise transformations
  4. The Building Blocks of Interpretability

2017 5

  1. Using Artificial Intelligence to Augment Human Intelligence
  2. Sequence Modeling with CTC
  3. Feature Visualization
  4. Why Momentum Really Works
  5. Research Debt

2016 4

  1. Experiments in Handwriting with a Neural Network
  2. Deconvolution and Checkerboard Artifacts
  3. How to Use t-SNE Effectively
  4. Attention and Augmented Recurrent Neural Networks