We prove the precise scaling, at finite depth and width, for the mean and variance of the neural tangent kernel (NTK) in a randomly initialized ReLU network. The standard deviation is exponential in the ratio of network depth to width. Thus, even in the limit of infinite overparameterization, the NTK is not deterministic if depth and width simultaneously tend to infinity. Moreover, we prove that for such deep and wide networks, the NTK has a non-trivial evolution during training by showing that the mean of
Giacomo Tesio - A decompiler for artificial neural network
A recent paper in Neuron from Kanaka Rajan, Chris Harvey and David Tank sets out to demonstrate how relatively unstructured networks can give rise to highly structured outputs that persist on slow timescales relevant to behaviors like decision-making and working memory. Such unstructured networks seem at first like exactly the wrong thing to support stimulus-driven persistent activity
Abstract page for arXiv paper 1506.02025: Spatial Transformer Networks
Home AI Weirdness Book: You look like a thing About Janelle Subscribe Search Sign in Sign up AI Weirdness: the strange side of machine learning Quote tweet with your favorite neural net By Janelle Shane On August 13, 2021 - 3 min read You know those games that go around on platforms like Twitter, where people share pictures of themselves looking like the leader of an underground resistance, or search google images for their name + fantasy armor or somesuch? I assume that if you successfully start one of the
# ICCV 2019 Open Access Repository Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks With Octave Convolution Yunpeng Chen, Haoqi Fan, Bing Xu, Zhicheng Yan, Yannis Kalantidis, Marcus Rohrbach, Shuicheng Yan, Jiashi Feng; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 3435-3444 Abstract In natural images, information is conveyed at different frequencies where higher frequencies are usually encoded with fine details and lower frequencie
Skip to content Jack Terwilliger Menu Category: cognitive science 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 net
jarxiv Japanese arxiv コンテンツへスキップ ホーム ← Early-Cycle Internal Impedance Enables ML-Based Battery Cycle Life Predictions Across Manufacturers Contrastive Normalizing Flows for Uncertainty-Aware Parameter Estimation → Wilsonian Renormalization of Neural Network Gaussian Processes 投稿日: 2025年5月14日 作成者: jarxiv 要約 関連する情報と無関係な情報を分離することは、モデリングプロセスまたは科学的調査の鍵です。 理論物理学は
A short post playing with the idea of using a Recurrent Neural Network to automatically generate text from James Joyce's Finnegans Wake
A neural architecture that dynamically weights parts of the input to capture relevant context