# Speeding Up the Training of Deep Neural Networks Distributed training architectures rely on two concepts: all-reduce or a parameter server . ## BytePS Jiang et al., n.d. BytePS provides a unifying framework for All-reduce and parameter server architectures, showing communication optimality. Intra-machine communication is optimized. It also proposes a “Summation Service”, which accelerates DNN training by running gradient summation on CPUs, while performing parameter updates on GPUs
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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 Implementing Neural Networks in TensorFlow (and PyTorch) Step-by-step code guide on building a Neural Network Shreya Rao Jul 8, 2024 6 min read Share Welcome to the practical implementation guide of our Deep Learning Illustrated series. In this series, we’ll bridge the gap between theory and application, bringing to life
The paper presents a comparative analysis showing Liquid Neural Networks outperform RNNs in accuracy, memory efficiency, and generalization on dynamic tasks
This explores whether neural networks actually run step-by-step procedures the way code does, or whether they only recognize and replay patterns seen in training — and the corpus turns out to have evi
日本語 English Center for Information and Neural Networks 日本語 Center for Information and Neural Networks 〒565-0871 Osaka Prefecture Suita City Yamadaoka 1-4 Center for Information and Neural Networks (CiNet) 2nd floor Computational Social Neuroscience Group My research aims to characterize human social behavior and the underlying neural computations that support it. As social creatures, interactions with other humans command much of our daily life, and are critically related to a number of
mailitics Singular Bayesian Neural Networks Singular Bayesian Neural Networks arXiv:2602.00387v1 Announce Type: new Abstract: Bayesian neural networks promise calibrated uncertainty but require $O(mn)$ parameters for standard mean-field Gaussian posteriors. We argue this cost is often unnecessary, particularly when weight matrices exhibit fast singular value decay. By parameterizing weights as $W = AB^{top}$ with $A in mathbb{R}^{m times r}$, $B in mathbb{R}^{n times r}$, we induce a posterior that is singu
NeurIPS Proceedings Search Dilated Recurrent Neural Networks Shiyu Chang, Yang Zhang, Wei Han, Mo Yu, Xiaoxiao Guo, Wei Tan, Xiaodong Cui, Michael Witbrock, Mark A Hasegawa-Johnson, Thomas S. Huang Advances in Neural Information Processing Systems 30 (NIPS 2017) Abstract Learning with recurrent neural networks (RNNs) on long sequences is a notoriously difficult task. There are three major challenges: 1) complex dependencies, 2) vanishing and exploding gradients, and 3) efficient parallelization. In this pap
# JP's Blog Search - Reviews - Photography - Programming - Maker - Automation - Writing - Research - RSS # FLAIRS 2010 - Augmenting n-gram Based Authorship Attribution With Neural Networks 2010-05-21 - Topics research All Posts Co-authors: Michael Wollowski , and Maki Hirotani Abstract: While using statistical methods to determine authorship attribution is not a new idea and neural networks have been applied to a number of statistical problems, the two have not often been used together. We show tha
Toggle navigation Home English Deutsch Login × Title Click a name to choose. Click to show more. Scalable mechanistic neural networks Chen J, Yao D, Pervez AA, Alistarh D-A, Locatello F. 2025. Scalable mechanistic neural networks. 13th International Conference on Learning Representations. ICLR: International Conference on Learning Representations, 63716–63737. --> Download 2025_ICLR_Chen.pdf 732.75 KB [Published Version] × Request a Copy E-Mail-Adresse Message Send Request Close Conference Paper