NeurIPS 2025
neurips-2025-02e26460·2 events·first seen 1mo agoAliases: NeurIPS 2025
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NeurIPS 2025 E2LM Competition: Early Training Evaluation of Language Models
TII UAE and collaborators are announcing the Early Training Evaluation of Language Models (E2LM) competition at NeurIPS 2025. The competition focuses on predicting or evaluating language model capabilities from early training checkpoints, addressing the challenge of forecasting final model performance without completing full training runs. This is relevant to evaluation methodology and training efficiency research in the AI/ML community.
Information-Driven Design of Imaging Systems
Researchers from Berkeley present a framework for evaluating and optimizing imaging systems based on mutual information content rather than traditional metrics like resolution or SNR, published at NeurIPS 2025. The method estimates mutual information directly from noisy measurements using known noise physics and learned probabilistic models (including transformers and PixelCNN), avoiding the need for task-specific decoders. Validated across four domains—color photography, radio astronomy, lensless imaging, and microscopy—the information metric predicts downstream decoder performance and enables hardware optimization with less compute and memory than end-to-end neural approaches.