Abstract page for arXiv paper 2111.10657: Generalizing Graph Neural Networks on Out-Of-Distribution Graphs
NIPS 2018 Sun Dec 2nd through Sat the 8th, 2018 at Palais des Congrès de Montréal Paper ID: 1911 Title: Learning sparse neural networks via sensitivity-driven regularization Reviewer 1 This paper studies the sensitivity-based regularization and pruning of neural networks. Authors have introduced a new update rule based on the sensitivity of parameters and derived an overall regularization term based on this novel update rule. The main idea of this paper is indeed novel and interesting. The paper is
Announcing the release of TensorFlow GNN 1.0, a production-tested library for building GNNs at Google scale, supporting both modeling and training.
sign in Search: Search T. Sun (Tao) and S.M. Bohte (Sander) 2025-11-26 Uncertainty-aware spiking neural networks for regression Publication Publication Presented at the 2025 International Conference on Neuromorphic Systems (ICONS) (July 2025), Seattle, USA Uncertainty estimation is a key component for quantifying the reliability of modern deep learning models, and is crucial for many real-world applications. However, efficient methods for uncertainty estimation in spiking neural networks (SNNs), particularl
David MacKay Information Theory, Pattern Recognition and Neural Networks Prerequisites Summary · Synopsis « · Bibliography Videos Slides 2012 Supervisions The Book Software Any questions? Search : Information Theory, Pattern Recognition and Neural Networks Minor Option [16 lecture synopsis] (from 2006, the course is reduced to 12 lectures) Lecturer: David MacKay Introduction to information theory [1] The possibility of reliable communication over unreliable channels. The (7,4) Hamming code and repetition
baguette::bagger() creates a collection of neural networks forming an ensemble. All trees in the ensemble are combined to produce a final prediction
By Nick McCullum Machine learning, and especially deep learning, are two technologies that are changing the world. After a long "AI winter" that spanned 30 years, computing power and data sets have finally caught up to the artificial intelligence alg...
Statistics and Data Science Statistics and Data Science About the course Probability Probability Topics Random Variables Conditonal Probability Bayes’ Theorem Independence Empirical Distribution Expectation Covariance and Correlation Simple data exploration Visualizing joint and marginal distributions Quantifying statistical dependence How do distributions transform under a change of variables ? Change of variables with autodiff Transformation of likelihood with change of random variable Transformation prop
# ImageNet Classification with Deep Convolutional Neural Networks ## Abstract We trained a large, deep convolutional neural network to classify the 1.3 million high-resolution images in the LSVRC-2010 ImageNet training set into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 39.7\% and 18.9\% which is considerably better than the previous state-of-the-art results. The neural network, which has 60 million parameters and 500,000 neurons, consists of five convolutiona
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