yc14600

yc14600's Stars

• jessevig/bertviz

  BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)

  Language:Python7k790

• thuml/Time-Series-Library

  A Library for Advanced Deep Time Series Models.

  Language:Python7.4k1.2k

• thuml/Autoformer

  About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008

  Language:Jupyter Notebook2k430

• som-shahlab/femr

  FEMR (Framework for Electronic Medical Records) provides tooling for large-scale, self-supervised learning using electronic health records

  Language:Python11814

• neotree/neotree

  Overview of Neotree project and codebase

  3

• asaini/Apriori

  Python Implementation of Apriori Algorithm for finding Frequent sets and Association Rules

  Language:Python771435

• mlmed/torchxrayvision

  TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.

  Language:Jupyter Notebook943222

• dataabc/weiboSpider

  新浪微博爬虫，用python爬取新浪微博数据

  Language:Python8.5k2k

• HazyResearch/hippo-code

  Language:Python16931

• jambo6/neuralRDEs

  Code for: "Neural Rough Differential Equations for Long Time Series", (ICML 2021)

  Language:Jupyter Notebook11514

• tatsu-lab/stanford_alpaca

  Code and documentation to train Stanford's Alpaca models, and generate the data.

  Language:Python29.7k4.1k

• nliulab/mimic4ed-benchmark

  Python workflow for generating benchmark datasets and machine learning models from the MIMIC-IV-ED database.

  Language:Python6927

• jambo6/online-neural-cdes

  Code for: "Neural Controlled Differential Equations for Online Prediction Tasks"

  Language:Python376

• JonathanCrabbe/Simplex

  This repository contains the implementation of SimplEx, a method to explain the latent representations of black-box models with the help of a corpus of examples. For more details, please read our NeurIPS 2021 paper: 'Explaining Latent Representations with a Corpus of Examples'.

  Language:Python248

• medspacy/medspacy

  Library for clinical NLP with spaCy.

  Language:Jupyter Notebook53892

• PBarnaghi/ML4NS

  Machine Learning for Neuroscience Module, Spring 2023 (https://www.imperial.ac.uk/study/courses/postgraduate-taught/translational-neuroscience/)

  Language:Jupyter Notebook291

• NLPatVCU/medaCy

  :hospital: Medical Text Mining and Information Extraction with spaCy

  Language:Python43291

• garyongguanjie/Ordinal-Classifier

  Classifying classes with ordinality

  Language:Python102

• shap/shap

  A game theoretic approach to explain the output of any machine learning model.

  Language:Jupyter Notebook23.1k3.3k

• patrick-kidger/NeuralCDE

  Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)

  Language:Python63270

• msurtsukov/neural-ode

  Jupyter notebook with Pytorch implementation of Neural Ordinary Differential Equations

  Language:Jupyter Notebook702120

• patrick-kidger/torchcde

  Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.

  Language:Python42344

• raminmh/CfC

  Closed-form Continuous-time Neural Networks

  Language:Python906147

• google-research/torchsde

  Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

  Language:Python1.6k203

• optimass/continual_learning_papers

  Relevant papers in Continual Learning

  Language:TeX72482

• esoreq/dcarte

  Dcarte code

  Language:Python98

• pengzhiliang/MAE-pytorch

  Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners

  Language:Python2.6k342

• vanderschaarlab/hyperimpute

  A framework for prototyping and benchmarking imputation methods

  Language:Python16514

• mmalekzadeh/motion-sense

  MotionSense Dataset for Human Activity and Attribute Recognition ( time-series data generated by smartphone's sensors: accelerometer and gyroscope) (PMC Journal) (IoTDI'19)

  Language:Jupyter Notebook327105

• ContinualAI/continual-learning-baselines

  Continual learning baselines and strategies from popular papers, using Avalanche. We include EWC, SI, GEM, AGEM, LwF, iCarl, GDumb, and other strategies.

  Language:Python26738