random seed
Temperature Balancing, Layer-wise Weight Analysis, and Neural Network Training Yefan Zhou
However, such a global learning rate schedule does not take into account the structural characteristics of neural networks (NNs).
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Robust Data Valuation with Weighted Banzhaf Values
We summarize our contributions as follows: 1.
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Active Observing in Continuous-time Control
The decision-maker's observing policy must be timely
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Simple random search of static linear policies is competitive for reinforcement learning
Horia Mania, Aurelia Guy, Benjamin Recht
Model-free reinforcement learning aims to offer off-the-shelf solutions for controlling dynamical systems without requiring models of the system dynamics.
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Supplementary Material 481 We provide details omitted in the main paper
To achieve this, auxiliary information (e.g., texts or attributes) is usually needed
Appendix for " Preference-grounded Token-level Guidance for 657 Language Model Fine-tuning " 658 Table of Contents
F.3 Sparse Reward with KL Penalty . . . . . . . . . . . . . . . . . . . . . . . . . . .
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