Abstract page for arXiv paper 1611.00144: Product-based Neural Networks for User Response Prediction

NeurIPS Proceedings Search Charting and Navigating the Space of Solutions for Recurrent Neural Networks Elia Turner, Kabir V Dabholkar, Omri Barak Advances in Neural Information Processing Systems 34 (NeurIPS 2021) Abstract In recent years Recurrent Neural Networks (RNNs) were successfully used to model the way neural activity drives task-related behavior in animals, operating under the implicit assumption that the obtained solutions are universal. Observations in both neuroscience and machine learning chal

Unraveling the Softmax Function A Primer on Its Pivotal Role in Neural Networks. The Softmax function is a fundamental component in neural networks, par

Understand activation functions (ReLU, sigmoid, tanh, GELU) and why non-linearity is essential for deep neural networks to learn complex patterns

# Evaluating Attribution for Graph Neural Networks ## Abstract Interpretability of machine learning models is critical to scientific understanding, AI safety, as well as debugging. Attribution is one approach to interpretability, which highlights input dimensions that are influential to a neural network’s prediction. Evaluation of these methods is largely qualitative for image and text models, because acquiring ground truth attributions requires expensive and unreliable human judgment. Attribution has bee

A Model Extraction Attack on Deep Neural Networks Running on GPUs | Jonah G. O'Brien Weiss | Computer science, Neural networks, nVidia, nVidia Quadro RTX 8000, Security, Tesla T4

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.

--> 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