↓ Skip to main content # PLOS ## Article Metrics # Targeting operational regimes of interest in recurrent neural networks Overview of attention for article published in PLoS Computational Biology, May 2023 Altmetric Badge ## Mentioned by - twitter 10 X users ## Readers on - mendeley 8 Mendeley Summary X Article details Title Targeting operational regimes of interest in recurrent neural networks Published in PLoS Computational Biology, May 2023 DOI 10.1371/journal.pcbi.1011097 Pubmed ID 371
Press "Enter" to skip to content Curated SQL A Fine Slice Of SQL Server open menu Search About Neural Nets On Spark Published 2017-06-28 by Kevin Feasel Nisha Muktewar and Seth Hendrickson show how to use Deeplearning4j to build deep learning models on Hadoop and Spark : Modern convolutional networks can have several hundred million parameters. One of the top-performing neural networks in the Large Scale Visual Recognition Challenge (also known as “ImageNet”), has 140 million parameters to train! These
Abstract page for arXiv paper 2105.10190: AngularGrad: A New Optimization Technique for Angular Convergence of Convolutional Neural Networks
NeurIPS 2019 Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center Paper ID: 9024 Title: Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks This paper proposes a new memory layout for recurrent neural networks that is 1. theoretically grounded 2. allows for orders of magnitude longer memory than traditional approaches with comparable parameter cost The results are also confirmed experimentally. This work is definitely of interest to Neurips community and would
Build a neural network from scratch using NumPy and understand forward propagation, backpropagation, and gradient descent
In my opinion, one of the best applications of neural networks is for generating Halloween costumes. Thanks to a dataset of over 7,100 costumes crowdsourced from readers of this blog, I’ve been able to generate Halloween costumes with progressively more powerful neural networks
The model parameters of convolutional neural networks (CNNs) are determined by backpropagation (BP). In this work, we propose an interpretable feedforward (FF) design without any BP as a reference. The FF design adopts a data-centric approach. It derives network parameters of the current layer based on data statistics from the output of the previous layer in a one-pass manner. To construct convolutional layers, we develop a new signal transform, called the Saab (Subspace Approximation with Adjusted Bias) tr
1. どんなもの？
10. Modern Recurrent Neural Networks search Quick search code Show Source Table Of Contents 1. Introduction 2. Preliminaries 2.1. Data Manipulation 2.2. Data Preprocessing 2.3. Linear Algebra 2.4. Calculus 2.5. Automatic Differentiation 2.6. Probability and Statistics 2.7. Documentation 3. Linear Neural Networks for Regression 3.1. Linear Regression 3.2. Object-Oriented Design for Implementation 3.3. Synthetic Regression Data 3.4. Linear Regression Implementation from Scratch 3.5. Concise Implementation of
#### 論文の概要: RTify: Aligning Deep Neural Networks with Human Behavioral Decisions - arxiv url: http://arxiv.org/abs/2411.03630v2 - Date: Thu, 26 Dec 2024 09:11:08 GMT - ステータス: 翻訳完了 - システム内更新日: 2024-12-30 16:01:35.891479 - Title: RTify: Aligning Deep Neural Networks with Human Behavioral Decisions - Title（参考訳）: RTify:人間の行動決定を伴うディープニューラルネットワークの調整 - Authors: Yu-Ang Cheng, Ivan Felipe Rodriguez, Sixuan