Deep Learning
ExPT: Synthetic Pretraining for Few-Shot Experimental Design
In this work, we address the more challenging yet realistic setting of few-shot experimental design, where only a few labeled data points of input designs and their corresponding values are available.
Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy Cameron Allen UC Berkeley Aaron Kirtland Brown University Ruo Y u Tao
These authors contributed equally and are ordered alphabetically.
Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine
However, developing effective foundation models for electronic health record (EHR) data in Emergency Medicine requires addressing several challenges.
Pre-RMSNorm and Pre-CRMSNorm Transformers: Equivalent and Efficient Pre-LN Transformers Zixuan Jiang, Jiaqi Gu, Hanqing Zhu, David Z. Pan Chandra Department of Electrical and Computer Engineering
Transformers have achieved great success in machine learning applications.
SelfCodeAlign: Self-Alignment for Code Generation Y uxiang Wei
SelfCodeAlign employs the same base model for inference throughout the data generation process. It first extracts diverse coding concepts from high-quality seed snippets to generate new tasks.
