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Found 4 papers, 1 papers with code
Easy as ABCs: Unifying Boltzmann Q-Learning and Counterfactual Regret Minimization
We propose ABCs (Adaptive Branching through Child stationarity), a best-of-both-worlds algorithm combining Boltzmann Q-learning (BQL), a classic reinforcement learning algorithm for single-agent domains, and counterfactual regret minimization (CFR), a central algorithm for learning in multi-agent domains.
American Stories: A Large-Scale Structured Text Dataset of Historical U.S. Newspapers
The resulting American Stories dataset provides high quality data that could be used for pre-training a large language model to achieve better understanding of historical English and historical world knowledge.
Understanding and Mitigating Extrapolation Failures in Physics-Informed Neural Networks
Physics-informed Neural Networks (PINNs) have recently gained popularity due to their effective approximation of partial differential equations (PDEs) using deep neural networks (DNNs).
Noise-Robust De-Duplication at Scale
Identifying near duplicates within large, noisy text corpora has a myriad of applications that range from de-duplicating training datasets, reducing privacy risk, and evaluating test set leakage, to identifying reproduced news articles and literature within large corpora.
