Code of paper: Do LLMs Really Adapt to Domains? An Ontology Learning Perspective (ISWC 2024)

Code and resources for our CODI paper "MuLMS-AZ: An Argumentative Zoning Dataset for the Materials Science Domain"

Experimental resources for our paper "QUITE: Quantifying Uncertainty in Natural Language Text in Bayesian Reasoning Scenarios" (EMNLP'24)

Official implementation of "Accurate Training Data for Occupancy Map Prediction in Automated Driving Using Evidence Theory"

Code and dataset for NAACL 2024 paper FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering

[CVPR 2024] Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationships

Official implementation of "Adversarial Supervision Makes Layout-to-Image Diffusion Models Thrive" (ICLR 2024)

Bosch solution to CHAMPS Kaggle competition

Code for our TMLR paper "AutoCLIP: Auto-tuning Zero-Shot Classifiers for Vision-Language Models"

This is the code accompanying our ICML 2024 paper "Eureka-Moments in Transformers: Multi-Step Tasks Reveal Softmax Induced Optimization Problems".

Code and resources for our WIESP paper "MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain"

Implementation and Knowledge Graphs of the ICCV 2023 workshop paper "nuScenes Knowledge Graph - A comprehensive semantic representation of traffic scenes for trajectory prediction"

Code and resources for the NAACL 2024 paper "Rehearsal-Free Modular and Compositional Continual Learning for Language Models"

Companion code for the research paper "CoAug: Combining Augmentation of Labels and Labeling Rules"

Official implementation of "Adaptive Certified Training: Towards Better Accuracy-Robustness Tradeoffs" (ECML-PKDD 2024)

Baseline code for DCASE 2024 Task10 and ICASSP 2024 paper

Blackbox optimization algorithms with a common interface, along with useful helpers like parallel optimization loops, analysis and visualization scripts.

Code for the ICLR 2024 paper Federated Text-driven Prompt Generation for Vision-Language Models (https://openreview.net/forum?id=NW31gAylIm)