A visual tour of the long reigning champions of computer vision.
# Nonlinear Models of Neural and Genetic Network Dynamics: Natural Transformations of Łukasiewicz Logic LM-Algebras in a Łukasiewicz-Topos as Representations of Neural Network Development and Neoplastic Transformations Baianu, Professor I.C. (2011) Nonlinear Models of Neural and Genetic Network Dynamics: Natural Transformations of Łukasiewicz Logic LM-Algebras in a Łukasiewicz-Topos as Representations of Neural Network Development and Neoplastic Transformations. [Conference Paper] (In Press) This is the
and reverse engineering neural networks
Deep Learning Specialization week-3 Hey, I’ve watched the batch normalization video three times now, but I still don’t get why it has a regularization effect. Can you explain it to me in a simple way?
Tabular data prediction (TDP) is one of the most popular industrial applications, and various methods have been designed to improve the prediction performance. However, existing works mainly focus on feature interactions and ignore sample relations, e.g., users with the same education level might have a similar ability to repay the debt. In this work, by explicitly and systematically modeling sample relations, we propose a novel framework TabGNN based on recently popular graph neural networks (GNN). Specifi
Publications Google Scholar Code - Unless stated otherwise in the github repo, the code is released under the following license . Neural GPUs learn algorithms, Lukasz Kaiser and Ilya Sutskever, ICLR 2016 [ code ] Reinforcement Learning Neural Turing Machines, Wojciceh Zaremba and Ilya Sutskever, arXiv 2015 [ code ] Training Recurrent Neural Networks with Hessian-Free Optimization, James Martens and Ilya Sutskever, ICML 2011. [ code ] Generating Text with Recurrent Neural Networks, Ilya Sutskeve
Training deep learning models with 3D numerical simulations as input via Neural Concept — store data efficiently and improve the training speed
Master backpropagation — the chain rule applied to computational graphs, gradient flow through layers, and why it enables deep learning.
The unified framework behind GCN, GAT, GraphSAGE, and GIN. Learn aggregation, update functions, and attention mechanisms in GNNs.
NeurIPS Proceedings Search Variational Learning for Recurrent Spiking Networks Danilo J. Rezende, Daan Wierstra, Wulfram Gerstner Advances in Neural Information Processing Systems 24 (NIPS 2011) Abstract We derive a plausible learning rule updating the synaptic efficacies for feedforward, feedback and lateral connections between observed and latent neurons. Operating in the context of a generative model for distributions of spike sequences, the learning mechanism is derived from variational inference princi