Skip to content Jack Terwilliger Menu Tag: neuroscience Attractor Networks, (A bit of) Computational Neuroscience Part III Posted on September 5, 2018September 25, 2018 by Jack Terwilliger Brains are comprised of networks of neurons connected by synapses, and these networks have greater computational properties than the neurons and synapses themselves. In this post, I am going to talk about a class of neural networks which I think are fascinating: attractor networks. These are recurrent neural networks with
Deep Learning Course Lecture 01: Introduction to Neural Networks Lecture 02: Training Neural Networks Lecture 03: Architecture Lecture 04: Tuning Lecture 05: CNN Section 1: Convolutional Neural Networks (CNN) & Transfer Learning Lecture 06: NLP and Representation Learning Section 1: Representation Learning and Text Representations Lecture 07: Recurrent Neural Networks Section 1: Recurrent Neural Networks Lecture 08: Attention and Transformers Lecture 09: Large Language Models Lecture 10: Marketing I Section
This Friday, I’m headed over to SysML to present “Toward Scalable Verification for Safety-Critical Deep Networks”, co-authored with Guy Katz, Justin Gottschlich, Kyle Julian, Clark Barrett, and Mykel Kochenderfer. Here’s our poster
The importance of achieving fairness in machine learning models cannot be overstated. Recent research has pointed out that fairness should be examined from a causal perspective, and several fairness notions based on the on Pearl's causal framework have been proposed. In this paper, we construct a reweighting scheme of datasets to address causal fairness. Our approach aims at mitigating bias by considering the causal relationships among variables and incorporating them into the reweighting process. The propo
Introduction
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Intel dumps its Nervana neural network processors for Habana's AI chips - SiliconANGLE
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A Unified Neural Scaling Law is presented that accurately models and extrapolates deep neural network scaling behaviors across multiple simultaneous dimensions including