pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

Due to inappropriate sample selection and limited training data, a distribution shift often exists between the training and test sets. This shift can adversely affect the test performance of Graph Neural Networks (GNNs). Existing approaches mitigate this issue by either enhancing the robustness of GNNs to distribution shift or reducing the shift itself. However, both approaches necessitate retraining the model, which becomes unfeasible when the model structure and parameters are inaccessible. To address thi

Independent Subnet Training decomposes fully connected networks into narrow, same-depth subnets trained independently on partitioned data, periodically exchanging parameters. It enables

What are Generative Adversarial Networks and how do they work? Learn about GANs architecture and model training, and explore the most popular generative models variants and their limitations

Encoder, decoder and encoder-decoder transformers are the most popular neural network in NLP. Understand the differences and how to use them

artificial neural network that mimics real neurons

Home AI Weirdness Book: You look like a thing About Janelle Subscribe Search Sign in Sign up AI Weirdness: the strange side of machine learning Pokemon generated by neural network By Janelle Shane On July 22, 2016 - 1 min read I’ve been playing around with char-rnn, an open-source torch add-on for character-based neural networks by Andrej Karpathy , using it to generate everything from cookbook recipes to superhero names to a Lovecraft/cookbook mashup . I decided to train the neural network to randomly

Image recognition technology may be sophisticated, but it is also easily duped. Researchers have fooled algorithms into confusing two skiers for a dog, a

Energy-based models for associative memory and generative learning: Hopfield networks, Boltzmann machines, RBMs, and contrastive divergence

My talk at ICSI-the International Computer Science Institute at UC Berkeley. ICSI is a leading independent, nonprofit center for research in computer science. Why Deep Learning Works: Self Regularization in Neural Networks Presented Thursday, December 13, 2018 https://www.youtube.com/watch?v=6Zgul4oygMc The slides are available on my slideshare. The supporting tool, WeightWatcher, can be installed using