4 accepted papers
Multi-view learning fuses complementary views to improve perception, but real-world deployments often suffer from Test-time Noisy Correspondence (TNC) -- cross-view misalignment caused by asynchronous sampling, transient network congestion, or other disturbances. Such misalignment introduces semanti
World models enable learning policies via latent imagination, offering benefits such as history compression and sample efficiency. The primary challenge in applying world models to multi-agent tasks is that modeling multi-agent dynamics in latent space requires integrating information from different…
Retrosynthesis, which aims to find a route to synthesize a target molecule from commercially available starting materials, is a critical task in drug discovery and materials design. Recently, the combination of ML-based single-step reaction predictors with multi-step planners has led to promising re…
Utilizing messages from teammates is crucial in cooperative multi-agent tasks due to the partially observable nature of the environment. Naively asking messages from all teammates without pruning may confuse individual agents, hindering the learning process and impairing the whole system's performan…