Abstract page for arXiv paper 2206.08427: SATBench: Benchmarking the speed-accuracy tradeoff in object recognition by humans and dynamic neural networks
← Single-seed generation of Brownian paths and integrals for adaptive and high order SDE solvers Uncertainty Quantification Metrics for Deep Regression → # Residual-based Attention Physics-informed Neural Networks for Efficient Spatio-Temporal Lifetime Assessment of Transformers Operated in Renewable Power Plants 変圧器は、電力およびエネルギー システムの信頼性と効率性を高めるための重要な資産です。 これらは
Graph Neural Networks (GNNs) have become a building block in graph data processing, with wide applications in critical domains. The growing needs to deploy GNNs in high-stakes applications necessitate explainability for users in the decision-making processes. A popular paradigm for the explainability of GNNs is to identify explainable subgraphs by comparing their labels with the ones of original graphs. This task is challenging due to the substantial distributional shift from the original graphs in the trai
Do we have reason to be a little less bewildered by so-called grandmother cells in the context of deep neural networks
# Neural Machine Translation with Attention Mechanism This article reviews a paper titled Neural Machine Translation By Jointly Learning To Align And Translate by Dzmitry Bahdanau, KyungHyun Cho, and Yoshua Bengio. In 2014, machine translation using neural networks emerged. Researchers adapted encoder-decoder (or sequence-to-sequence) architectures that encode a sentence in one language into a fixed-length vector and then decode it to another language. However, the approach requires the encoder to compre
Artificial neural networks have revolutionized the field of machine learning in recent years, powering everything from virtual assistants to self-driving cars
How to Standardize Your Data for Neural Networks Standardizing data helps in centering the data around zero with a unit variance
Chomsky Hierarchyにおいて, 各モデルがどのクラスに属するかを実験的に示した 各階層はオートマトンの性質と紐付いている RNNやTransformerは無限ステップにおいてチューリング完全であることが理論的に証明されているが, 有限ステップにおいて各モデルがどのクラスに属するかの研究は未だ発展中 例えば, Transformer
Network engineering has spent the better part of a decade trying to figure out how to fit artificial intelligence into workflows.
↓ Skip to main content PLOS Article Metrics What is this page? Embed badge Share Oscillation, Conduction Delays, and Learning Cooperate to Establish Neural Competition in Recurrent Networks Overview of attention for article published in PLOS ONE, February 2016 Altmetric Badge Mentioned by twitter 1 X user Readers on mendeley 17 Mendeley Summary X Article details Title Oscillation, Conduction Delays, and Learning Cooperate to Establish Neural Competition in Recurrent Networks Published in PLOS ONE