Recommended this week
- Malware Env for OpenAI Gym — create an AI that bypasses machine learning static-analysis malware detection.
- OpenPose — A Real-Time Multi-Person Keypoint Detection And Multi-Threading C++ Library.
- Texture — an open science manuscript editor. As open as LaTeX and as simple as a classic word processor.
- Densely Connected Convolutional Networks implemented in Keras, PyTorch, MXNet, TF and more.
- Tensorflow implementation of Enhanced Deep Residual Networks for Single Image Super-Resolution.
- Tiny Face Detector. CVPR 2017.
- “Annotating Object Instances with a Polygon-RNN”. Given a bounding box, automatically predicts the polygon outlining the object instance inside the box. CVPR 2017.
- “A correspondence between thermodynamics and inference”. Proposes an approach that unifies the Bayesian, frequentist and information-based paradigms of statistics by achieving coherent inference between them.
- “Learning from Simulated and Unsupervised Images through Adversarial Training”. Paper by Apple Researches that earned “Best Paper Prize” CVPR 2017.
- “When Neurons Fail”. Proves a tight bounds on the number of neurons that can fail without harming the result of a computation.
- “Mimicking Word Embeddings using Subword RNNs”. Paper by Yuval Pinter infers out-of-vocabulary(OOV) word embeddings from pre-trained models, without the originating corpus.
- “Going beyond average for reinforcement learning” by Marc G. Bellemare, DeepMind.
- “A Tutorial on Thompson Sampling” by DeepMind Daniel Russo
- “Learning Cross-modal Embeddings for Cooking Recipes and Food Images”. CVPR 2017.
- “PyData Seattle 2017” and “PyData Berlin 2017” playlists.
- Analyzing eye-movement and pupil-size data with Python DataMatrix.
- A collection of papers and books for Getting Started with Genetic Analysis. From Prof. John Storey, Princeton University.
- “Deep Learning — The Straight Dope”. An interactive book on deep learning, in concept and in MXNet by Zack Chase Lipton.
- “How to train your own Object Detector with TensorFlow’s Object Detector API” by Dat Tran.
- “Bayesian Neural Networks with Random Inputs for Model Based Reinforcement Learning” by José Miguel Hernández Lobato.
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