Learning Data Manipulation for Augmentation and Weighting
Hu et al.: https://arxiv.org/abs/1910.12795
Code: https://github.com/tanyuqian/learning-data-manipulation
#ArtificialIntelligence #DeepLearning #NeurIPS2019

Continual Unsupervised Representation Learning
Rao et al.: https://arxiv.org/abs/1910.14481
Code: https://github.com/deepmind/deepmind-research/tree/master/curl
#ArtificialIntelligence #DeepLearning #NeurIPS2019

From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction

Tanaka et al.: https://papers.nips.cc/paper/9060-from-deep-learning-to-mechanistic-understanding-in-neuroscience-the-structure-of-retinal-prediction

#DeepLearning #Neuroscience #NeurIPS2019

Bayesian Deep Learning - NeurIPS 2019 Workshop
Friday, December 13, 2019 — Vancouver Convention Center, Vancouver, Canada : http://bayesiandeeplearning.org
#bayesian #deeplearning #neurips2019

We just released our #NeurIPS2019 Multimodal Model-Agnostic Meta-Learning (MMAML) code for learning few-shot image classification, which extends MAML to multimodal task distributions (e.g. learning from multiple datasets). The code contains #PyTorch implementations of our model and two baselines (MAML and Multi-MAML) as well as the scripts to evaluate these models to five popular few-shot learning datasets: Omniglot, Mini-ImageNet, FC100 (CIFAR100), CUB-200-2011, and FGVC-Aircraft.

Code: https://github.com/shaohua0116/MMAML-Classification

Paper: https://arxiv.org/abs/1910.13616

#NeurIPS #MachineLearning #ML #code

Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes
Greg Yang : https://arxiv.org/abs/1910.12478
#ArtificialIntelligence #DeepLearning #NeurIPS2019

A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models
Kuznetsov et al.: https://arxiv.org/abs/1910.13148
#MachineLearning #NeurIPS #NeurIPS2019

NeurIPS 2019 Paper Awards
Neural Information Processing Systems Conference : https://medium.com/@NeurIPSConf/neurips-2019-paper-awards-807e41d0c1e
#ArtificialIntelligence #NeurIPS #NeurIPS2019

"Interpretable comparison of distributions and models"
Arthur Gretton, Dougal Sutherland, Wittawat Jitkrittum
Slides :
Part 1: http://gatsby.ucl.ac.uk/~gretton/papers/neurips19_1.pdf
Part 2: http://gatsby.ucl.ac.uk/~gretton/papers/neurips19_2.pdf
Part 3: http://gatsby.ucl.ac.uk/~gretton/papers/neurips19_3.pdf
#ArtificialIntelligence #MachineLearning #NeurIPS2019

#NeurIPS2019_2019-12-09_19-49-34.xlsx
View an interactive version of this graph (experimental) https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=218538