reproducibility
The ability to reproduce the results of an experiment or model reliably when the same methods and parameters are applied. Reproducibility is crucial for scientific integrity and validating AI research findings.
- Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms
- DAPO: An Open-Source LLM Reinforcement Learning System at Scale
- Gymnasium: A Standard Interface for Reinforcement Learning Environments
- Higher-Order Learning with Graph Neural Networks via Hypergraph Encodings
- LibriBrain: Over 50 Hours of Within-Subject MEG to Improve Speech Decoding Methods at Scale
- Meta-World+: An Improved, Standardized, RL Benchmark
- NerfBaselines: Consistent and Reproducible Evaluation of Novel View Synthesis Methods
- Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model
- OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics
- QoQ-Med: Building Multimodal Clinical Foundation Models with Domain-Aware GRPO Training
- QoQ-Med: Building Multimodal Clinical Foundation Models with Domain-Aware GRPO Training
- SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era.
- Scaling Data-Driven Probabilistic Robustness Analysis for Semantic Segmentation Neural Networks
- Statistically Valid Post-Deployment Monitoring Should Be Standard for AI-Based Digital Health
- TimeEmb: A Lightweight Static-Dynamic Disentanglement Framework for Time Series Forecasting
- Understanding and Mitigating Numerical Sources of Nondeterminism in LLM Inference
- Understanding and Mitigating Numerical Sources of Nondeterminism in LLM Inference
- VITA-Audio: Fast Interleaved Audio-Text Token Generation for Efficient Large Speech-Language Model
- WebGen-Bench: Evaluating LLMs on Generating Interactive and Functional Websites from Scratch