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Adobe Research
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Forget reinforcement learning; this algorithm learns in real-time without any feedback at all.
Unlock face recognition with just one labeled example and a flood of unlabeled data, achieving state-of-the-art accuracy in a practical authentication scenario.
Learn user preferences across thousands of items from just tens of node evaluations by exploiting graph smoothness in a new spectral bandit framework.
LLMs are revolutionizing conversational AI research, and this survey offers a structured guide to navigating the rapidly evolving landscape of LLM-powered user simulation.
Forget direct prompt editing: this agentic planning framework, powered by offline RL and synthetic data, masters complex image styling by breaking it down into interpretable tool sequences.
Eye-tracking data can boost click prediction in carousel interfaces, but surprisingly, better click prediction doesn't always mean a better model of user behavior.
Spotting unusual labels in your data just got easier with a new method that avoids the pitfalls of flagging isolated or boundary cases as anomalies.
