MartinaCadiz

MartinaCadiz's Stars

• Spandan-Madan/DeepLearningProject

  An in-depth machine learning tutorial introducing readers to a whole machine learning pipeline from scratch.

  Language:HTML4.8k20028637

• cmhungsteve/Awesome-Transformer-Attention

  An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites

  4.7k13030490

• dkozlov/awesome-knowledge-distillation

  Awesome Knowledge Distillation

  3.5k1128495

• stellargraph/stellargraph

  StellarGraph - Machine Learning on Graphs

  Language:Python3k621k432

• 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 Notebook2k15208431

• NX-AI/xlstm

  Official repository of the xLSTM.

  Language:Python1.6k1757115

• ZhuiyiTechnology/roformer

  Rotary Transformer

  Language:Python8508851

• google/edward2

  A simple probabilistic programming language.

  Language:Jupyter Notebook6791811775

• lucidrains/rotary-embedding-torch

  Implementation of Rotary Embeddings, from the Roformer paper, in Pytorch

  Language:Python603113147

• SimonKohl/probabilistic_unet

  A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.

  Language:Jupyter Notebook5482014100

• yandex-research/tab-ddpm

  [ICML 2023] The official implementation of the paper "TabDDPM: Modelling Tabular Data with Diffusion Models"

  Language:Python40964490

• axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation

  A generic Mixture Density Networks (MDN) implementation for distribution and uncertainty estimation by using Keras (TensorFlow)

  Language:Jupyter Notebook340231190

• ds4dm/ecole

  Extensible Combinatorial Optimization Learning Environments

  Language:C++332715768

• stefanknegt/Probabilistic-Unet-Pytorch

  A Probabilistic U-Net for segmentation of ambiguous images implemented in PyTorch

  Language:Python277102867

• alinlab/CSI

  CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances (NeurIPS 2020)

  Language:Python27575463

• team-approx-bayes/dl-with-bayes

  Contains code for the NeurIPS 2019 paper "Practical Deep Learning with Bayesian Principles"

  Language:Python24314923

• Mephisto405/Learning-Loss-for-Active-Learning

  Reproducing experimental results of LL4AL [Yoo et al. 2019 CVPR]

  Language:Python21661950

• EderSantana/gumbel

  Gumbel-Softmax Variational Autoencoder with Keras

  Language:Jupyter Notebook1339526

• coastalcph/hierarchical-transformers

  Hierarchical Attention Transformers (HAT)

  Language:Python46399

• lhirschfeld/ChempropUncertaintyQuantification

  Message Passing Neural Networks for Molecule Property Prediction

  Language:Jupyter Notebook24005

• jayheo/UA

  Language:Python23115

• saif-mahmud/hierarchical-attention-HAR

  [PAKDD-2021] Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity Recognition

  Language:Jupyter Notebook16327

• xiaoyuxin1002/UQ-PLM

  Uncertainty Quantification with Pre-trained Language Models: An Empirical Analysis

  Language:Python14111

• amzn/sto-transformer

  Language:Python13302

• StijnVerdenius/Lat-PFN

  This work introduces LaT-PFN, a novel time series model that combines PFN and JEPA frameworks to generate zero-shot forecasts efficiently, using a versatile latent space that enables adaptable time granularity and superior predictive performance.

  Language:Jupyter Notebook11100

• aritraghsh09/GaMPEN

  ML framework to estimate Bayesian posteriors of galaxy morphological parameters

  Language:Jupyter Notebook9365

• bsantraigi/HIER

  Official Repo for Implementations of Models/Experiments in "Hierarchical Transformer for Task Oriented Dialog Systems" - NAACL 2021 (Long Paper)

  Language:Jupyter Notebook5210

• felipeelorrieta/IAR_Model

  This folder contains R and Python functions to fit unequally spaced time series from the Irregular Autoregressive (IAR). From the functions of this folder we can generate observations for each process, compute the negative of the log likelihood of these process, fit each model to irregularly sampled data, and test the significance of the estimated parameters.

  Language:Python40

• omersan/Practice

  Language:Jupyter Notebook310

• luvalenz/time-series-variability-tree

  Language:Python2101