I have been very fascinated by how Convolution Neural Networks have been able to, so efficiently, do image classification and image recognition CNN’s have been very successful in in both these tasks. A good paper that explores the workings of a CNN Visualizing and Understanding Convolutional Networks by Matthew D Zeiler and Rob Fergus. They
In the past 10 years, the best-performing artificial-intelligence systems—such as the speech recognizers on smartphones or Google's latest automatic translator—have resulted from a technique called "deep learning."
This paper offers a comprehensive tutorial for deep learning practitioners on designing and implementing Bayesian Neural Networks to quantify uncertainty
How do neural networks work? It's a question that can confuse novices and experts alike. A team from MIT's Computer Science and Artificial Intelligence Lab (CSAIL) says that understanding these representations, as well as
jarxiv Japanese arxiv コンテンツへスキップ ホーム ← ICML 2023 Topological Deep Learning Challenge : Design and Results Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions → Modeling non-linear Effects with Neural Networks in Relational Event Models 投稿日: 2023年12月20日 作成者: jarxiv 要約 動的ネットワークは、リレーショナル システムがどのように進化するかについての洞察を提供します。 ただし
In this post, I discuss L1, L2, elastic net, and group lasso regularization on neural networks. I describe how regularization can help you build models that are more useful and interpretable, and I include Tensorflow code for each type of regularization. Finally, I provide a detailed case study demonstrating the effects of regularization on neural
Abstract page for arXiv paper 2302.06586v3: Stitchable Neural Networks
You may be interested in my new arXiv paper, joint work with Xi Cheng, an undergraduate at UC Davis (now heading to Cornell for grad school); Bohdan Khomtchouk, a post doc in biology at Stanford; and Pete Mohanty, a Science, Engineering & Education Fellow in statistics at Stanford. The paper is of a provocative nature, … Continue reading Neural Networks Are Essentially Polynomial Regression
It’s kind of weird that you are mixing decision making and construction of the output together in conventional artificial neural networks. The ReLU switch decisions are based on boosting of many weak learners in the pri
Proven: Verifying robustness of neural networks with a probabilistic approach for ICML 2019 by Tsui Wei Weng et al