Skip to content Publish AI, ML & data-science insights to a global community of data professionals. Sign in Submit an Article Toggle Mobile Navigation Toggle Search Search Deep Learning Hybrid Neuro-Symbolic Fraud Detection: Guiding Neural Networks with Domain Rules Injecting analyst intuition into the loss function as a differentiable constraint — and what rigorous multi-seed evaluation reveals about model comparison on imbalanced data Emmimal P Alexander Mar 10, 2026 14 min read Share Image by the
Register now for Spiking neural networks as universal function approximators on crowdcast, scheduled to go live on August 31, 2020, 01:00 PM GMT+1
An attention block lets neural networks dynamically weight what matters in a sequence, powering GPT, BERT, and vision transformers
Quantum neural networks exhibit unique dynamics different from classical kernel regression, showing sublinear convergence and highlighting the importance of measurement
Sleep plays an important role in incremental learning and consolidation of memories in biological systems. Motivated by the processes that are known to be involved in sleep generation in biological networks, we developed an algorithm that implements a sleep-like phase in artificial neural networks (ANNs). After initial training phase, we convert the ANN to a spiking neural network (SNN) and simulate an offline sleep-like phase using spike-timing dependent plasticity rules to modify synaptic weights. The SNN
jarxiv Japanese arxiv コンテンツへスキップ ホーム ← TempEE: Temporal-Spatial Parallel Transformer for Radar Echo Extrapolation Beyond Auto-Regression The Structurally Complex with Additive Parent Causality (SCARY) Dataset → Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey 投稿日: 2023年4月28日 作成者: jarxiv 要約 タイトル：都市計算の予測学習のための時空間グラフニューラルネットワーク：サーベイ 要約
Deep Sparse Rectifier Neural NetworksXavier Glorot, Antoine Bordes, Yoshua BengioWhile logistic sigmoid neurons are more biologically plausible tha
Summary: 1. Classic settings, i.e. deep networks with convolutional layers and large batch sizes, almost always have backward-forward FLOP ratios cl
The ICANN 2019 proceedings deal with artificial neural networks and machine learning in general, focusing on theoretical neural computation; deep learning; image processing; text and time series. The last volume includes contributions of the reservoir computing workshop and special sessions
# CSC321 Winter 2015: Introduction to Neural Networks Lecture notes Here are some notes to supplement the Coursera videos. Slides from the in-class meetings can be found in the calendar . Thanks to Tijmen Tieleman for the original version of these notes. ### Lecture A - Why do we need machine learning? and What are neural networks? - These videos introduce the motivation and general philosophy of ML. - Don’t worry if you don’t understand all of the technicalities of e.g. the story about speech recognit