Search Results for author:
Found 3 papers, 1 papers with code
Scaling Up Quantization-Aware Neural Architecture Search for Efficient Deep Learning on the Edge
Since models are typically quantized for edge deployment, recent work has investigated quantization-aware NAS (QA-NAS) to search for highly accurate and efficient quantized models.
Off-Policy Action Anticipation in Multi-Agent Reinforcement Learning
Learning anticipation in Multi-Agent Reinforcement Learning (MARL) is a reasoning paradigm where agents anticipate the learning steps of other agents to improve cooperation among themselves.
Deep Adaptive Multi-Intention Inverse Reinforcement Learning
This paper presents a deep Inverse Reinforcement Learning (IRL) framework that can learn an a priori unknown number of nonlinear reward functions from unlabeled experts' demonstrations.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
