Sharing some of the latest research, announcements, and resources on deep learning. By Isaac Madan ( email ) Continuing our series of deep learning updates, we pulled together some of the awesome resources that have emerged since our last post. In case you missed it, you can find all past updates . As always, this list is not comprehensive, so if there’s something we should add, or if you’re interested in discussing this area further. If you’re a machine learning practitioner or student, join our Talent Network to get exposed to awesome ML opportunities. here let us know here Research & Announcements by CMU. OpenPose: Real-time multi-person keypoint detection library for body, face, and hands OpenPose is a library for real-time multi-person keypoint detection and multi-threading written in C++ using OpenCV and Caffe. An extension: Openpose from CMU implemented using Tensorflow with Custom Architecture for fast inference . It provides some changes to the network structure for real-time processing on the CPU or low-power embedded devices. by Wen of Purdue Engineering. Youtube video . Original paper . ‘Mind-reading’ AI: Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision et al Researchers have demonstrated how to decode what the human brain is seeing by using artificial intelligence to interpret fMRI scans from people watching videos, representing a sort of mind-reading technology. here here by Hinton of Google Brain. New paper by Geoffrey Hinton. PyTorch implementation of the paper . Dynamic Routing Between Capsules et al A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or object part. We show that a discrimininatively trained, multi-layer capsule system achieves state-of-the-art performance on MNIST and is considerably better than a convolutional net at recognizing highly overlapping digits. here by Pei of Columbia Engineering. DeepXplore: Automated Whitebox Testing of Deep Learning Systems et al Researchers at Columbia and Lehigh universities have come up with a way to automatically error-check the thousands to millions of neurons in a deep learning neural network. Their tool feeds confusing, real-world inputs into the network to expose rare instances of flawed reasoning by clusters of neurons. DeepXplore efficiently finds thousands of incorrect corner case behaviors (e.g., self-driving cars crashing into guard rails and malware masquerading as benign software) in state-of-the-art DL models with thousands of neurons trained on five popular datasets including ImageNet and Udacity self-driving challenge data. by Ahn and Park. GitHub repo. Voice Conversion with Non-Parallel Data (i.e. Speaking like Kate Winslet) Deep neural networks for voice conversion (voice style transfer) in Tensorflow. Website and docs . Uber AI Labs Open Sources Pyro, a Deep Probabilistic Programming Language . here Why probabilistic modeling? To correctly capture uncertainty in models and predictions for unsupervised and semi-supervised learning, and to provide AI systems with declarative prior knowledge. Why (universal) probabilistic programs? To provide a clear and high-level, but complete, language for specifying complex models. Why deep probabilistic models? To learn generative knowledge from data and reify knowledge of how to do inference. Why inference by optimization? To enable scaling to large data and leverage advances in modern optimization and variational inference. Resources, Tutorials & Data by UC Berkeley. _This two-day long bootcamp will teach you the foundations of Deep RL through a mixture of lectures and hands-on lab sessions, so you can go on and build new fascinating applications using these techniques and maybe even push the algorithmic frontier._ Great topics, content, and speakers — the lectures and labs are available online. Deep RL Bootcamp by Deep Systems. Latest Deep Learning OCR with Keras and Supervisely in 15 minutes This tutorial is a gentle introduction to building modern text recognition system using deep learning in 15 minutes. . AMA with the two of the team members at DeepMind who developed AlphaGo AlphaGo Zero uses a quite different approach to deep RL than typical (model-free) algorithms such as policy gradient or Q-learning. By using AlphaGo search we massively improve the policy and self-play outcomes — and then we apply simple, gradient based updates to train the next policy + value network. This appears to be much more stable than incremental, gradient-based policy improvements that can potentially forget previous improvements. by S. Keshav of University of Waterloo. Not newly published, but very useful as the number of new ML papers continues to grow rapidly. BTW: How to Read a Paper By . Isaac is an investor at Venrock ( ). If you’re interested in deep learning or there are resources I should share in a future newsletter, I’d love to hear from you. If you’re a machine learning practitioner or student, join our Talent Network to get exposed to awesome ML opportunities. Isaac Madan email here is a newsletter of entrepreneurial ideas & perspectives by investors, operators, and influencers. Requests for Startups ** **❤” Please tap or click “︎ to help to promote this piece to others.
