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Found 4 papers, 2 papers with code
Accelerating Exact Combinatorial Optimization via RL-based Initialization -- A Case Study in Scheduling
The traditional exact methods are limited by runtime complexity, while reinforcement learning (RL) and heuristic-based approaches struggle with determinism and solution quality.
RESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUs
Deep neural networks (DNNs) have substantial computational and memory requirements, and the compilation of its computational graphs has a great impact on the performance of resource-constrained (e. g., computation, I/O, and memory-bound) edge computing systems.
Hateful Memes Challenge: An Enhanced Multimodal Framework
Hateful Meme Challenge proposed by Facebook AI has attracted contestants around the world.
Combinatorial Reinforcement Learning Based Scheduling for DNN Execution on Edge
The past half-decade has seen unprecedented growth in machine learning with deep neural networks (DNNs) that represent state-of-the-art in many real-world applications.
