jarxiv Japanese arxiv コンテンツへスキップ ホーム ← Make Every Example Count: On Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets Epicurus at SemEval-2023 Task 4: Improving Prediction of Human Values behind Arguments by Leveraging Their Definitions → SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks 投稿日: 2023年2月28日 作成者: jarxiv 要約
Feedforward neural networks use one-way layers with nonlinear activations and backpropagation for robust supervised learning, statistical inference, and model selection
How thinking machines implement one of the most important functions of cognition.
Abstract page for arXiv paper 2306.15403v1: Verifying Safety of Neural Networks from Topological Perspectives
--> Dropout: A Simple Way to Prevent Neural Networks from Overfitting Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov. Year: 2014, Volume: 15 , Issue: 56, Pages: 1929−1958 Abstract Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different
I’m a bandit Random topics in optimization, probability, and statistics. By Sébastien Bubeck Guest post by Julien Mairal: A Kernel Point of View on Convolutional Neural Networks, part I Posted on July 10, 2019 by Sebastien Bubeck I (n.b., Julien Mairal ) have been interested in drawing links between neural networks and kernel methods for some time, and I am grateful to Sebastien for giving me the opportunity to say a few words about it on his blog. My initial motivation was not to provide another “why
Recurrent Neural Network (RNN) articles from The Batch, DeepLearning.AI's weekly AI newsletter
Presentation about an "Achitectural Zoo" of different applications and architectures of CNNs. Presented at Machine Learning Meetup in Porto Alegre yesterday. Video (there are english subtitles available):
Learn what neural networks are, how layers of interconnected nodes process information, and why they are the foundation of modern AI systems from language models to computer vision
Jump to content Main menu Main menu move to sidebar hide Navigation Contribute Search Search Appearance Personal tools Contents move to sidebar hide (Top) 1 History Toggle History subsection 1.1 Mathematical foundations 1.2 Perceptrons 1.3 Historical foundations and the Dartmouth proposal 1.4 1960s and 1970s 1.5 Backpropagation 1.6 Convolutional neural networks 1.7 Recurrent neural networks 1.8 Modern deep learning 1.9 Transformers 2 Elements Toggle Elements subsection 2.1 Neuron 2.2 Network 3 Learning Togg