From Sparse to Dense: Toddler–inspired Reward Transition in Goal–Oriented Reinforcement Learning
Abstract: Reinforcement learning (RL) agents face fundamental challenges in balancing exploration and exploitation, particularly when sparse or dense rewards bias learning toward sub–optimal behaviors ...
Abstract: Deepfake detection remains a pressing challenge due to the rapid evolution of forgery techniques and the demand for robust, generalizable, and interpretable solutions. We present a sparse ...
Analog compute-in-memory combines compute and storage using crossbar arrays of non-volatile memory, thus promising to reduce the energy demand for artificial intelligence workloads. Yet, significant ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results