Dataset And Benchmark Neurips 2025 Model. NeurIPS Poster Beyond Realworld Benchmark Datasets An Empirical Study of Node Classification We introduce DrivingDojo, the first dataset tailor-made for training interactive world models with complex driving dynamics Best Paper Award committee for dataset and benchmark track: Yulia Gel, Ludwig.
Two Papers accepted for NeuRIPS 2025 Datasets and Benchmarks Track Universität Mannheim from www.uni-mannheim.de
In this paper, we present UniVG, a diffusion based model that unifies diverse image generation tasks within a single framework Abstract Deadline: Feb 17, 2025; Paper Deadline: Feb 24, 2025; Notification: May 16, 2024; Camera-ready: TBD; All deadlines are end-of-day in the Anywhere on Earth (AoE) time.
Two Papers accepted for NeuRIPS 2025 Datasets and Benchmarks Track Universität Mannheim
Best Paper Award committee for dataset and benchmark track: Yulia Gel, Ludwig. NeurIPS Datasets & Benchmarks: Raising the Bar for Dataset Submissions. Our dataset features video clips with a complete set of driving maneuvers, diverse multi-agent interplay, and rich open-world driving knowledge, laying a stepping stone for future world model development.
Two Papers accepted for NeuRIPS 2025 Datasets and Benchmarks Track Universität Mannheim. If you are willing to self-nominate to serve as an AC for NeurIPS 2025, please fill in this form A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs" by Julia Gastinger and others
NeurIPS Poster An NLP Benchmark Dataset for Assessing Corporate Climate Policy Engagement. Abstract Deadline: Feb 17, 2025; Paper Deadline: Feb 24, 2025; Notification: May 16, 2024; Camera-ready: TBD; All deadlines are end-of-day in the Anywhere on Earth (AoE) time. NeurIPS 2025 Datasets & Benchmarks Track Call for Papers The NeurIPS Datasets and Benchmarks track serves as a venue for high-quality publications on highly valuable machine learning datasets and benchmarks crucial for the development and continuous improvement of machine learning methods