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An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries
List of relevant resources for machine learning from explanatory supervision
Automatic architecture search and hyperparameter optimization for PyTorch
This code package implements the prototypical part network (ProtoPNet) from the paper "This Looks Like That: Deep Learning for Interpretable Image Recognition" (to appear at NeurIPS 2019), by Chaof…
Multilingual Sentence & Image Embeddings with BERT
Model interpretability and understanding for PyTorch
🦋A PyTorch implementation of BigGAN with pretrained weights and conversion scripts.
🦄 State-of-the-Art Conversational AI with Transfer Learning
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Padé Activation Units: End-to-end Learning of Activation Functions in Deep Neural Network
Experiments on the integration of Sum-Product Networks into Deep Neural Networks.
Unofficial PyTorch implementation of the paper titled "Progressive growing of GANs for improved Quality, Stability, and Variation"
pytorch implementation of openai paper "Glow: Generative Flow with Invertible 1×1 Convolutions"
Perform data science on data that remains in someone else's server
Learn and analyse hyperspectral data of leafs with GAN
DeepFill v1/v2 with Contextual Attention and Gated Convolution, CVPR 2018, and ICCV 2019 Oral
Sum Product Flow: An Easy and Extensible Library for Sum-Product Networks
Code for reproducing results in "Glow: Generative Flow with Invertible 1x1 Convolutions"
Lime: Explaining the predictions of any machine learning classifier
Code for Switchable Normalization from "Differentiable Learning-to-Normalize via Switchable Normalization", https://arxiv.org/abs/1806.10779
Code for paper "Which Training Methods for GANs do actually Converge? (ICML 2018)"
A technical report on convolution arithmetic in the context of deep learning
Pytorch implementation of convolutional neural network visualization techniques
Pytorch implementation of convolutional neural network adversarial attack techniques