Search Results for author:
Found 7 papers, 3 papers with code
Review-driven Personalized Preference Reasoning with Large Language Models for Recommendation
Recent advancements in Large Language Models (LLMs) have demonstrated exceptional performance across a wide range of tasks, generating significant interest in their application to recommendation systems.
Active Neural 3D Reconstruction with Colorized Surface Voxel-based View Selection
CSV encapsulates the uncertainty of estimated scene appearance (e. g., color uncertainty) and estimated geometric information (e. g., surface).
Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset
Our experimental results demonstrate that utterances in PEARL include more specific user preferences, show expertise in the target domain, and provide recommendations more relevant to the dialogue context than those in prior datasets.
EXOT: Exit-aware Object Tracker for Safe Robotic Manipulation of Moving Object
We evaluate our method on the first-person video benchmark dataset, TREK-150, and on the custom dataset, RMOT-223, that we collect from the UR5e robot.
Robust Imitation via Mirror Descent Inverse Reinforcement Learning
Recently, adversarial imitation learning has shown a scalable reward acquisition method for inverse reinforcement learning (IRL) problems.
Unbiased learning with State-Conditioned Rewards in Adversarial Imitation Learning
The formulation draws a strong connection between adversarial learning and energy-based reinforcement learning; thus, the architecture is capable of recovering a reward function that induces a multi-modal policy.
Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning
Active learning is widely used to reduce labeling effort and training time by repeatedly querying only the most beneficial samples from unlabeled data.
