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Found 9 papers, 2 papers with code
Reinforcement Learning from Delayed Observations via World Models
In standard Reinforcement Learning settings, agents typically assume immediate feedback about the effects of their actions after taking them.
Persuasion in Veto Bargaining
We consider the classic veto bargaining model but allow the agenda setter to engage in persuasion to convince the veto player to approve her proposal.
Selective Perception: Optimizing State Descriptions with Reinforcement Learning for Language Model Actors
Large language models (LLMs) are being applied as actors for sequential decision making tasks in domains such as robotics and games, utilizing their general world knowledge and planning abilities.
Relation-Aware Language-Graph Transformer for Question Answering
To address these issues, we propose Question Answering Transformer (QAT), which is designed to jointly reason over language and graphs with respect to entity relations in a unified manner.
Meta-node: A Concise Approach to Effectively Learn Complex Relationships in Heterogeneous Graphs
To tackle this challenge, we propose a novel concept of meta-node for message passing that can learn enriched relational knowledge from complex heterogeneous graphs without any meta-paths and meta-graphs by explicitly modeling the relations among the same type of nodes.
An Investigation on Hardware-Aware Vision Transformer Scaling
In particular, when transferred to PiT, our scaling strategies lead to a boosted ImageNet top-1 accuracy of from $74. 6\%$ to $76. 7\%$ ($\uparrow2. 1\%$) under the same 0. 7G FLOPs; and when transferred to the COCO object detection task, the average precision is boosted by $\uparrow0. 7\%$ under a similar throughput on a V100 GPU.
Winning the CVPR'2021 Kinetics-GEBD Challenge: Contrastive Learning Approach
Generic Event Boundary Detection (GEBD) is a newly introduced task that aims to detect "general" event boundaries that correspond to natural human perception.
CAG-QIL: Context-Aware Actionness Grouping via Q Imitation Learning for Online Temporal Action Localization
Temporal action localization has been one of the most popular tasks in video understanding, due to the importance of detecting action instances in videos.
Keeping the Listener Engaged: a Dynamic Model of Bayesian Persuasion
We consider a dynamic model of Bayesian persuasion in which information takes time and is costly for the sender to generate and for the receiver to process, and neither player can commit to their future actions.
