Separating the hype from what actually moves the needle
Neural module networks (NMNs) are a popular approach for modeling compositionality: they achieve high accuracy when applied to problems in language and vision, while reflecting the compositional structure of the problem in the network architecture. However, prior work implicitly assumed that the structure of the network modules, describing the abstract reasoning process, provides a faithful explanation of the model's reasoning; that is, that all modules perform their intended behaviour. In this work, we pro
The news site Slashdot (“news for nerds, stuff that matters”) is celebrating its 20 year anniversary this October. What could be geekier than celebrating with the help of an open-source neural network? Neural networks are a type of machine learning program that learn by example, rather than by a human programmer feeding them rules. Whatever the headlines contain, whatever common words and rhythms, a neural network will do its best to imitate. I’ve trained an open-source neural network called ch
Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation Keywords: imagenet Abstract: Spiking Neural Networks (SNNs) operate with asynchronous discrete events (or spikes) which can potentially lead to higher energy-efficiency in neuromorphic hardware implementations. Many works have shown that an SNN for inference can be formed by copying the weights from a trained Artificial Neural Network (ANN) and setting the firing threshold for each layer as the maximum in
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Abstract page for arXiv paper 2401.15299: SupplyGraph: A Benchmark Dataset for Supply Chain Planning using Graph Neural Networks
9. Recurrent Neural Networks navigate_next 9.1. Working with Sequences 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 Scrat
jarxiv Japanese arxiv コンテンツへスキップ ホーム ← ICU-TSB: A Benchmark for Temporal Patient Representation Learning for Unsupervised Stratification into Patient Cohorts Antithetic Noise in Diffusion Models → Physics-Informed Neural Networks for Control of Single-Phase Flow Systems Governed by Partial Differential Equations 投稿日: 2025年6月9日 作成者: jarxiv 要約 部分微分方程式（PDE
Artificial Neural Connection From GM-RKB An Artificial Neural Connection is a Graph Edge that connects pairs of Artificial Neurons ( nodes ) in Artificial Neural Network . AKA: ANN Connection , ANN Edge . Context: It is analog to a Neural Synapsis in a Biological Neural Network . It is quantified by a Neural Network Weight value [math]\displaystyle{ w_{ji} }[/math] where [math]\displaystyle{ i }[/math] the index of artificial neuron in an initial neural network layer and [math]\displaystyle{ j }[/math] is i
Biological neurons transmit information with stereotyped electrical impulses called ``spikes'', sensitive to coincident timings. Spiking Neural Networks (SNNs), introduced in the nineties, have gained popularity in AI for their energy efficiency and competitive performance with deep learning. Amo