KaHyPar (Karlsruhe Hypergraph Partitioning) is a multilevel hypergraph partitioning framework providing direct k-way and recursive bisection based partitioning algorithms that compute solutions of very high quality.

Multi-Task Learning package built with tensorflow 2 (Multi-Gate Mixture of Experts, Cross-Stitch, Ucertainty Weighting)

Mixed-curvature Variational Autoencoders (ICLR 2020)

An archive for NILM papers with source code and other supplemental material

A paper list of state-of-the-art weakly supervised object detection or localization.

IMS Machine Learning Lab publications.

A python implementation of the paper "Scalable Recognition with a Vocabulary Tree, D. Nister, H. Stewenius, 2006"

This repository contains the jupyter notebooks for the custom-built DenseNet Model build on Tiny ImageNet dataset

Supplemental material on comparability and performance evaluation in NILM

Tensorflow code , pre-trained models, and papers for Natural Language Processing

Hybrid Girvan Newman. Code for "A Distributed Hybrid Community Detection Methodology for Social Networks."

A Go Command Line App to get all the source URLS from an arxiv pdf.

This is a python JAX implementation of the paper: Rainbow: Combining improvements in deep reinforcement learning, by M. Hessel et al. In Thirty-Second AAAI Conference on Artificial Intelligence.

Code for "Groove2Groove: One-Shot Music Style Transfer with Supervision from Synthetic Data"

Detecting Bot Behaviour in Social Media using Digital DNA Compression

A python implementation of the concepts in the book "Reinforcement Learning: An Introduction" by R.S. Sutton and A. G. Barto.

A python JAX implementation of the paper: Kaiser, L., Babaeizadeh, M., Milos, P., Osinski, B., Campbell, R.H., Czechowski, K., Erhan, D., Finn, C., Kozakowski, P., Levine, S. and Mohiuddin, A., 2019. Model-based reinforcement learning for atari. arXiv preprint arXiv:1903.00374.

A python haiku implementation of: Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G. and Petersen, S., 2015. Human-level control through deep reinforcement learning. nature, 518(7540), pp.529-533.

Extract source code urls from a PDF file.