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Independent component analysis: algorithms and applications
Extracing city blocks from a graph
Computing Receptive Fields of Convolutional Neural Networks
Jyouhou/SceneTextPapers: Tracking the latest progress in Scene Text Detection and Recognition: Must-read papers well organized
attrs 20.3.0 documentation
Seminar 9: Surya Ganguli - Statistical Physics of Deep Learning
[2006.02359] From Probability to Consilience: How Explanatory Values Implement Bayesian Reasoning
Neural Network Attributions: A Causal Perspective
[1706.05394] A Closer Look at Memorization in Deep Networks
[1703.11008] Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data
On the Spectral Bias of Neural Networks
[1711.04623] Three Factors Influencing Minima in SGD
[1704.05310] Unsupervised Learning by Predicting Noise
[1806.07937] A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning
[1901.06523] Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks
Does learning require memorization? a short tale about a long tail
brain of mat kelcey...
Group theory 1: Introduction
Understanding and Visualizing Data Iteration in Machine Learning
SEER: The start of a more powerful, flexible, and accessible era for computer vision
TorchDrift: drift detection for PyTorch — TorchDrift documentation
philadias/freelabel: FreeLabel: A Publicly Available Annotation Tool based on Freehand Traces - Code used for our manuscript presented at Winter Conference on Applications of Computer Vision (WACV), 2019
[2103.06255] Involution: Inverting the Inherence of Convolution for Visual Recognition
PyTorch Lightning V1.2.0- DeepSpeed, Pruning, Quantization, SWA
Interpretability vs Neuroscience [rough note]
[2006.13915] Hierarchically Local Tasks and Deep Convolutional Networks
[2103.03386] Clusterability in Neural Networks
How we can make machine learning algorithms tunable
How screen scraping and TinyML can turn any dial into an API « Pete Warden's blog
Deep Implicit Attention: A Mean-Field Theory Perspective on Attention Mechanisms | mcbal
fields
Create a context manager to enable InferenceMode in python frontend · Issue #56608 · pytorch/pytorch · GitHub
[2105.12221] Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances
[2107.02367] Discrete-Valued Neural Communication
Contrastive Representation Learning
Green's Functions in Physics | Brilliant Math & Science Wiki
einops/3-einmix-layer.ipynb at master · arogozhnikov/einops
Frontier Questions in AI Explainability Research | by Alon Jacovi | Feb, 2022 | Medium
Complex and semantic figure composition — Matplotlib 3.5.1 documentation
[2201.12926] Compositionality as Lexical Symmetry
[2201.11239] Diagnosing AI Explanation Methods with Folk Concepts of Behavior
[2202.05808] Investigating Power laws in Deep Representation Learning
[2201.12091] Linear Adversarial Concept Erasure
C.S. Lewis Was a Modern Man Who Breathed Medieval Air... | Christianity Today
Transformers for software engineers - Made of Bugs
May I Quote?
What Makes a Great Opening Line? ‹ Literary Hub
Radio Garden – Explore live radio by rotating the globe
Compute Funds and Pre-trained Models | GovAI Blog
Measuring Goodhart’s Law