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Found 13 papers, 5 papers with code
Make the Pertinent Salient: Task-Relevant Reconstruction for Visual Control with Distractions
Training in the presence of visual distractions is particularly difficult due to the high variation they introduce to representation learning.
Realizable Continuous-Space Shields for Safe Reinforcement Learning
For real-world robotic domains, it is essential to define safety specifications over continuous state and action spaces to accurately account for system dynamics and compute new actions that minimally deviate from the agent's original decision.
Rethinking Data Augmentation for Robust LiDAR Semantic Segmentation in Adverse Weather
Motivated by this issue, we identified key factors of adverse weather and conducted a toy experiment to pinpoint the main causes of performance degradation: (1) Geometric perturbation due to refraction caused by fog or droplets in the air and (2) Point drop due to energy absorption and occlusions.
Ranked #1 on
LIDAR Semantic Segmentation
on SemanticSTF
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.
Selective Generation for Controllable Language Models
$\texttt{SGen}^{\texttt{Sup}}$, a direct modification of the selective prediction, is a supervised learning algorithm which exploits entailment-labeled data, annotated by humans.
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.
