Search Results for author:
Found 7 papers, 3 papers with code
CARFF: Conditional Auto-encoded Radiance Field for 3D Scene Forecasting
We demonstrate the utility of our method in realistic scenarios using the CARLA driving simulator, where CARFF can be used to enable efficient trajectory and contingency planning in complex multi-agent autonomous driving scenarios involving visual occlusions.
MemGPT: Towards LLMs as Operating Systems
Large language models (LLMs) have revolutionized AI, but are constrained by limited context windows, hindering their utility in tasks like extended conversations and document analysis.
Hindsight Task Relabelling: Experience Replay for Sparse Reward Meta-RL
Meta-reinforcement learning (meta-RL) has proven to be a successful framework for leveraging experience from prior tasks to rapidly learn new related tasks, however, current meta-RL approaches struggle to learn in sparse reward environments.
Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models
Humans have a remarkable ability to make decisions by accurately reasoning about future events, including the future behaviors and states of mind of other agents.
Assessing Generalization in Deep Reinforcement Learning
Our aim is to catalyze community-wide progress on generalization in deep RL.
Out-of-Distribution Generalization
reinforcement-learning
+1
Visually-Aware Personalized Recommendation using Interpretable Image Representations
Visually-aware recommender systems use visual signals present in the underlying data to model the visual characteristics of items and users' preferences towards them.
Learning Compatibility Across Categories for Heterogeneous Item Recommendation
Identifying relationships between items is a key task of an online recommender system, in order to help users discover items that are functionally complementary or visually compatible.
