DAVE: Diagnostic benchmark for Audio Visual Evaluation

The official PyTorch implementation of Google's Gemma models

A collection of papers on the topic of ``Computer Vision in the Wild (CVinW)''

Optimization with JDTLoss and Evaluation with Fine-grained mIoUs for Semantic Segmentation

Algorithms for abstention, calibration and domain adaptation to label shift.

The official source code to: Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition (AISTATS'23)

Calibration of Convolutional Neural Networks

A new markup-based typesetting system that is powerful and easy to learn.

The simplest, fastest repository for training/finetuning medium-sized GPTs.

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.

[MICCAI 2021 (Oral)] Official code repository for "Variational Topic Inference for Chest X-Ray Report Generation"

A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation

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

It is my belief that you, the postgraduate students and job-seekers for whom the book is primarily meant will benefit from reading it; however, it is my hope that even the most experienced research…

This repository open sources some of the code and trained models belonging to the public datasets used in the corresponding articles.

Codebase for "Decoding language spatial relations to 2D spatial arrangements" (Findings of EMNLP 2020).

Codebase for "Revisiting spatio-temporal layouts for compositional action recognition" (Oral at BMVC 2021).

Code for the Lovász-Softmax loss (CVPR 2018)

Calibration library and code for the paper: Verified Uncertainty Calibration. Ananya Kumar, Percy Liang, Tengyu Ma. NeurIPS 2019 (Spotlight).

Python code for "Probabilistic Machine learning" book by Kevin Murphy

Code for the paper "Calibrating Deep Neural Networks using Focal Loss"

Summaries of machine learning papers

A scikit-learn-compatible library for estimating prediction intervals and controlling risks, based on conformal predictions.

A simple way to calibrate your neural network.