An agent-managed museum exhibit, built in Rust with Gajae-Code / LazyCodex — developed and maintained with no human intervention.

CausalPFN: Amortized Causal Effect Estimation via In-Context Learning

Community extensions for TabPFN - the foundation model for tabular data. Built with TabPFN! 🤗

A tool that validates academic paper references

UQLM: Uncertainty Quantification for Language Models, is a Python package for UQ-based LLM hallucination detection

The open source coding agent.

Ranking methods from paired comparison data for the Pears platform.

Simulation-based inference benchmark

PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks

A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.

Database with posteriors of interest for Bayesian inference

Laplace approximations for Deep Learning.

Probabilistic Inference on Noisy Time Series

Official repository for discrete Walk-Jump Sampling (dWJS)

Probabilistic modelling and uncertainty quantification library

A Python library for vine copula models

Debiasing the Human-Recommender System Feedback Loop in Collaborative Filtering Paper at The 2019 World Wide Web Conference

Simple implementation of the CGP-UCB algorithm.

Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.

NCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.

Code associated with the NeurIPS ML4PS paper titled, "Re-calibrating Photometric Redshift Probability Distributions Using Feature-space Regression"

A framework for single/multi-objective optimization with metaheuristics

BERT score for text generation

Implementation of https://srush.github.io/annotated-s4

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…