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Found 6 papers, 0 papers with code
Inductive Bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters
The problem of scheduling of workloads onto heterogeneous processors (e. g., CPUs, GPUs, FPGAs) is of fundamental importance in modern datacenters.
$\widetilde{O}(T^{-1})$ Convergence to (Coarse) Correlated Equilibria in Full-Information General-Sum Markov Games
No-regret learning has a long history of being closely connected to game theory.
Watch Out for the Safety-Threatening Actors: Proactively Mitigating Safety Hazards
In this work, we propose a safety threat indicator (STI) using counterfactual reasoning to estimate the importance of each actor on the road with respect to its influence on the AV's safety.
Watch out for the risky actors: Assessing risk in dynamic environments for safe driving
Driving in a dynamic environment that consists of other actors is inherently a risky task as each actor influences the driving decision and may significantly limit the number of choices in terms of navigation and safety plan.
Application-aware Congestion Mitigation for High-Performance Computing Systems
Netscope has a lower training cost and accurately estimates the impact of congestion on application runtime with a correlation between 0. 7and 0. 9 for common scientific applications.
Distributed, Parallel, and Cluster Computing Networking and Internet Architecture
ML-driven Malware that Targets AV Safety
Ensuring the safety of autonomous vehicles (AVs) is critical for their mass deployment and public adoption.
