danielshaps's repositories
bold
Dataset associated with "BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation" paper
danielshaps.github.io
Website URL
edos
Public repository for SemEval 2023 - Task 10 - Explainable Detection of Online Sexism (EDOS)
edos_data
Original data here: https://github.com/rewire-online/edos
edward2
A simple probabilistic programming language.
entropy_estimators
Estimators for the entropy and other information theoretic quantities of continuous distributions
evoclearn_optccv_2022
Articulatory synthesis samples for Interspeech 2022
LibriTTSLabel
Alignment files of LibriTTS.
loss-landscape
Code for visualizing the loss landscape of neural nets
PAC2BAYES
This repository contains the Python code to reproduce all the figures and experiments presented in the paper: Masegosa, Andrés. R., Learning under Model Misspecification: Applications to Variational and Ensemble methods.
pals0039
Introduction to deep learning for speech and language processing
PEEK-Dataset
To make the peek dataset available
python_speech_features
This library provides common speech features for ASR including MFCCs and filterbank energies.
robustness
Corruption and Perturbation Robustness (ICLR 2019)
shennong
A Python toolbox for speech features extraction
swa
Stochastic Weight Averaging in PyTorch
swa_gaussian
Code repo for "A Simple Baseline for Bayesian Uncertainty in Deep Learning"
tensorflow_addons
Useful extra functionality for TensorFlow 2.x maintained by SIG-addons
Transformer-MM-Explainability
[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
uncertainty-baselines
High-quality implementations of standard and SOTA methods on a variety of tasks.
variational-nn-pytorch
Variational Neural Networks Pytorch implementation
VariationalInformationPursuit
Official Implementation for Variational Information Pursuit for Interpretable Predictions (ICLR 2023)
vit-explain
Explainability for Vision Transformers