AnFreTh's Stars
qile2000/LAMDA-TALENT
A comprehensive toolkit and benchmark for tabular data learning, featuring 30 deep methods, more than 10 classical methods, and 300 diverse tabular datasets.
basf/mamba-tabular
Mambular is a Python package that brings the power of Mamba architectures to tabular data, offering a suite of deep learning models for regression, classification, and distributional regression tasks. This includes models like Mambular, FT-Transformer, TabTransformer and tabular ResNets.
yandex-research/rtdl-revisiting-models
(NeurIPS 2021) Revisiting Deep Learning Models for Tabular Data
yandex-research/tabm
TabM: Advancing Tabular Deep Learning With Parameter-Efficient Ensembling
yandex-research/tabred
TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks
IntelLabs/bayesian-torch
A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch
MinishLab/model2vec
The Fastest State-of-the-Art Static Embeddings in the World
mcrts/ACL2024-SymmetricAttentionBert
mkumar73/stream_topic_data
Dataset for stream_topic python package
AnFreTh/OKPSPS
AnFreTh/STREAM
A versatile Python package engineered for seamless topic modeling, topic evaluation, and topic visualization. Ideal for text analysis, natural language processing (NLP), and research in the social sciences, STREAM simplifies the extraction, interpretation, and visualization of topics from large, complex datasets.
ftrtz/spotify-listening-habits
AnFreTh/NodeGAMLSS
Code for deep distributional learning using Node-GAMLSS
AnFreTh/NAMpy
alxndrTL/mamba.py
A simple and efficient Mamba implementation in pure PyTorch and MLX.
liesel-devs/liesel
A probabilistic programming framework
StatMixedML/XGBoostLSS
An extension of XGBoost to probabilistic modelling
LMU-Seminar-LLMs/TopicGPT
TopicGPT allows to integrate the benefits of LLMs into Topic Modelling
ArikReuter/TopicGPT
TopicGPT allows to integrate the benefits of LLMs into Topic Modelling
ArikReuter/TNTM
This repository contains the code for the Transformer-Representation Neural Topic Model (TNTM) based on the paper "Probabilistic Topic Modelling with Transformer Representations" by Arik Reuter, Anton Thielmann, Christoph Weisser, Benjamin Säfken and Thomas Kneib
JENScoding/Lets-play-poker
Python (Jupyter) script to play poker with up to 8 players.