InTDS ArchivebySergi Castella i SapéBest of arXiv — Readings for November 2021A monthly selection of ML papers, news and codeNov 3, 20211Nov 3, 20211
InTDS ArchivebySergi Castella i SapéBest of arXiv—Readings for October 2021A monthly selection of ML papersSep 29, 2021Sep 29, 2021
InTDS ArchivebyTDS EditorsLatent Stochastic Differential EquationsDavid Duvenaud | TMLS2019Apr 14, 2020Apr 14, 2020
InTDS ArchivebySergi Castella i SapéICLR 2021 — A selection of 10 papers you shouldn’t missThe International Conference on Learning Representations is already here and it’s packed with content: 860 papers 8 workshops and 8…Apr 30, 20211Apr 30, 20211
InTDS ArchivebyVijay Prakash DwivediGraph Transformer: Generalization of Transformers to GraphsWe generalize Transformers to arbitrary graphs by extending key design aspects of attention and positional encodings from NLP to graphs.Mar 4, 20214Mar 4, 20214
InTDS ArchivebyMichael BronsteinGraph Neural Networks as Neural Diffusion PDEsGraph neural networks are intimately related to partial differential equations governing information diffusion on graphs.Jun 18, 20212Jun 18, 20212
InTDS ArchivebyMichael BronsteinTemporal Graph NetworksA new neural network architecture for dynamic graphsJul 27, 20207Jul 27, 20207
InTDS ArchivebyAjay ArunachalamDesign a neuromorphic predictive network architecture with pytorchDeep Learning with network of spiking neurons — Are spiking neural network the future?Sep 12, 2021Sep 12, 2021
InTDS ArchivebyRobert LangeFour Deep Learning Papers to Read in September 2021From Auto-ML to Vision Transformer Training & Representations and Catastrophic Fisher ExplosionSep 4, 20212Sep 4, 20212
