Search Results for author:
Found 8 papers, 6 papers with code
MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification
The objective of search result diversification (SRD) is to ensure that selected documents cover as many different subtopics as possible.
What's documented in AI? Systematic Analysis of 32K AI Model Cards
To evaluate the impact of model cards, we conducted an intervention study by adding detailed model cards to 42 popular models which had no or sparse model cards previously.
PDiT: Interleaving Perception and Decision-making Transformers for Deep Reinforcement Learning
Designing better deep networks and better reinforcement learning (RL) algorithms are both important for deep RL.
Enhancing Your Trained DETRs with Box Refinement
We hope our work will bring the attention of the detection community to the localization bottleneck of current DETR-like models and highlight the potential of the RefineBox framework.
Transformer in Transformer as Backbone for Deep Reinforcement Learning
Recent methods combine the Transformer with these modules for better performance.
DATE: Dual Assignment for End-to-End Fully Convolutional Object Detection
In this paper, we revisit these two assignment methods and find that bringing one-to-many assignment back to end-to-end fully convolutional detectors helps with model convergence.
PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning
Furthermore, we introduce a novel paradigm named Personalized Training with Distilled Execution (PTDE), wherein agent-personalized global information is distilled into the agent's local information.
Improving Fine-tuning of Self-supervised Models with Contrastive Initialization
Self-supervised learning (SSL) has achieved remarkable performance in pretraining the models that can be further used in downstream tasks via fine-tuning.
