PatrickSchrML

PatrickSchrML's Stars

• ml-research/semantic-image-editing

  Language:Python20711

• ml-research/NeSyXIL

  Language:Python322

• EleutherAI/gpt-neox

  An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries

  Language:Python7k1k

• stefanoteso/awesome-explanatory-supervision

  List of relevant resources for machine learning from explanatory supervision

  Language:TeX15516

• automl/Auto-PyTorch

  Automatic architecture search and hyperparameter optimization for PyTorch

  Language:Python2.4k292

• alejandromolinaml/activation_functions

  Language:Cuda53

• cfchen-duke/ProtoPNet

  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 Chaofan Chen* (Duke University), Oscar Li* (Duke University), Chaofan Tao (Duke University), Alina Jade Barnett (Duke University), Jonathan Su (MIT Lincoln Laboratory), and Cynthia Rudin (Duke University) (* denotes equal contribution).

  Language:Python354130

• UKPLab/sentence-transformers

  State-of-the-Art Text Embeddings

  Language:Python15.8k2.5k

• pytorch/captum

  Model interpretability and understanding for PyTorch

  Language:Python5k507

• ml-research/moral-choice-machine

  Language:Python5

• huggingface/pytorch-pretrained-BigGAN

  🦋A PyTorch implementation of BigGAN with pretrained weights and conversion scripts.

  Language:Python1k179

• huggingface/transfer-learning-conv-ai

  🦄 State-of-the-Art Conversational AI with Transfer Learning

  Language:Python1.7k432

• huggingface/transformers

  🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

  Language:Python138k27.6k

• ml-research/pau

  Padé Activation Units: End-to-end Learning of Activation Functions in Deep Neural Network

  Language:Python639

• braun-steven/spn-pytorch-experiments

  Experiments on the integration of Sum-Product Networks into Deep Neural Networks.

  Language:Python82

• akanimax/pro_gan_pytorch

  Unofficial PyTorch implementation of the paper titled "Progressive growing of GANs for improved Quality, Stability, and Variation"

  Language:Python53799

• chaiyujin/glow-pytorch

  pytorch implementation of openai paper "Glow: Generative Flow with Invertible 1×1 Convolutions"

  Language:Python50980

• OpenMined/PySyft

  Perform data science on data that remains in someone else's server

  Language:Python9.6k2k

• PatrickSchrML/hyperspectral_phenotyping_gan

  Learn and analyse hyperspectral data of leafs with GAN

  Language:Python112

• JiahuiYu/generative_inpainting

  DeepFill v1/v2 with Contextual Attention and Gated Convolution, CVPR 2018, and ICCV 2019 Oral

  Language:Python3.3k789

• SPFlow/SPFlow

  Sum Product Flow: An Easy and Extensible Library for Sum-Product Networks

  Language:Python29181

• pclucas14/pytorch-glow

  Pytorch Implementation of OpenAI's GLOW

  Language:Python9217

• thanard/causal-infogan

  Language:Python8516

• openai/glow

  Code for reproducing results in "Glow: Generative Flow with Invertible 1x1 Convolutions"

  Language:Python3.1k518

• marcotcr/lime

  Lime: Explaining the predictions of any machine learning classifier

  Language:JavaScript11.7k1.8k

• switchablenorms/Switchable-Normalization

  Code for Switchable Normalization from "Differentiable Learning-to-Normalize via Switchable Normalization", https://arxiv.org/abs/1806.10779

  Language:HTML866130

• LMescheder/GAN_stability

  Code for paper "Which Training Methods for GANs do actually Converge? (ICML 2018)"

  Language:Jupyter Notebook918114

• vdumoulin/conv_arithmetic

  A technical report on convolution arithmetic in the context of deep learning

  Language:TeX14.2k2.3k

• utkuozbulak/pytorch-cnn-visualizations

  Pytorch implementation of convolutional neural network visualization techniques

  Language:Python7.9k1.5k

• utkuozbulak/pytorch-cnn-adversarial-attacks

  Pytorch implementation of convolutional neural network adversarial attack techniques

  Language:Python35461