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Appendix Contents 1 Introduction 1 2 Related Work 3 3 The D
Participants are not allowed to modify the training procedure.
bc218a0c656e49d4b086975a9c785f47-Supplemental-Datasets_and_Benchmarks.pdf
Emerging ethical approaches have attempted to filter pretraining material, but such approaches have been ad hoc and failed to take context into account. We offer an approach to filtering grounded in law, which has directly addressed the tradeoffs in filtering material.
AutomatedDiscoveryofAdaptiveAttackson AdversarialDefenses
Common modifications include:(i)tuning attack parameters (e.g., number ofsteps),(ii)replacing network components to simplify the attack (e.g., removing randomization or non-differentiable components), and(iii) replacing the loss function optimized by the attack.
ae07d152c51ea2ddae65aa7192eb5ff7-Supplemental-Conference.pdf
Recent work has shown that a much simpler model, simple graph convolution (SGC) (Wu et al., 2019),iscompetitivewithGCNs incommon graph machine learning benchmarks.
A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems Yi Ma
To address this problem, existing methods partition the overall DPDP into fixed-size sub-problems by caching online generated orders and solve each sub-problem, or on this basis to utilize the predicted future orders to optimize each sub-problem further. However, the solution quality and efficiency of these methods are unsatisfactory, especially when the problem scale is very large.