Value Iteration Networks Tamar et al., NIPS 2016 ‘Value Iteration Networks’ won a best paper award at NIPS 2016. It tackles two of the hot issues in reinforcement learning at the moment: incorporating longer range planning into the learned strategies, and improving transfer learning from one problem to another. It’s two for the price of
Learn how the backpropagation algorithm trains neural networks. Complete guide with step-by-step explanation, examples, and mathematical foundations
Skip to main content Back to top Ctrl+K Choose version Choose version Collapse Sidebar Expand Sidebar Section Navigation - 1. Introduction - 2. Simulators - 3. Data Processing: Adapters - 4. Approximators - 5. Summary Networks - 6. Inference Networks - 7. Workflows - 8. Saving & Loading Models - 9. Diagnostics and Visualizations - 10. Using Datasets in BayesFlow - User Guide - 5. Summary Networks # 5. Summary Networks # Many scientific simulators produce observations that cannot be cleanly flatten
Units navigate_next Basic Convolutional Networks search Quick search code Show Source STAT 157, Spring 19 Table Of Contents 1. Ensuring Quality Conversations in Online Forums 2. Image attribute classification using disentangled embeddings on multimodal data 3. Deep Learning with NLP (Tacotron) 4. Image captioning 5. Explainable Electrocardiogram Classifications using Neural Networks 7. Deep fitting room 8. Bot controlled accounts 9. Predicting Next Day Stock Returns After Earnings Reports Using Deep Learnin
Date: Aug 29, 2019 Disclaimer: I wrote this article a long time ago. I’ve since learned much more about Deep Learning. Hey everyone, this is my attempt to explain how a Neural Network works
New research deciphers the geometric shape of neural representations to explain how our brains efficiently learn and apply knowledge across different tasks
A neural processing unit (NPU) is a microprocessor that specializes in the acceleration of machine learning algorithms. Examples include TPU by Google, NVDLA by Nvidia, EyeQ by Intel, Inferentia by Amazon, Ali-NPU by Alibaba, Kunlun by Baidu, Sophon by Bitmain, MLU by Cambricon, IPU by Graphcore
Abstract page for arXiv paper 2209.13971v1: 3D Neural Sculpting (3DNS): Editing Neural Signed Distance Functions
# On the Inductive Bias of Neural Tangent Kernels ## Abstract State-of-the-art neural networks are heavily over-parameterized, making the optimization algorithm a crucial ingredient for learning predictive models with good generalization properties. A recent line of work has shown that in a certain over-parameterized regime, the learning dynamics of gradient descent are governed by a certain kernel obtained at initialization, called the neural tangent kernel. We study the inductive bias of learning in suc
This study analyzes deep neural network dynamics using the Neural Tangent Hierarchy, capturing NTK evolution and bridging finite and infinite width predictions