output
20022024
most citedNon-Abelian Anyons and Topological Quantum Computation

7k citations

Showing 2021Show all

89 papers · 1 filter

cs.LG20213 cited

The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle

Fang Kong, Yueran Yang, Wei Chen +1

Thompson sampling (TS) has attracted a lot of interest in the bandit area. It was introduced in the 1930s but has not been theoretically proven until recent years. All of its analy…

cs.CV2021

Bootstrap Your Object Detector via Mixed Training

Mengde Xu, Zheng Zhang, Fangyun Wei +5

We introduce MixTraining, a new training paradigm for object detection that can improve the performance of existing detectors for free. MixTraining enhances data augmentation by ut…

cs.CL2021

Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP

Trapit Bansal, Karthick Gunasekaran, Tong Wang +2

Meta-learning considers the problem of learning an efficient learning process that can leverage its past experience to accurately solve new tasks. However, the efficacy of meta-lea…

cs.MM2021

Distantly Supervised Semantic Text Detection and Recognition for Broadcast Sports Videos Understanding

Avijit Shah, Topojoy Biswas, Sathish Ramadoss +1

Comprehensive understanding of key players and actions in multiplayer sports broadcast videos is a challenging problem. Unlike in news or finance videos, sports videos have limited…

eess.IV202117 cited

Whole Brain Segmentation with Full Volume Neural Network

Yeshu Li, Jonathan Cui, Yilun Sheng +4

Whole brain segmentation is an important neuroimaging task that segments the whole brain volume into anatomically labeled regions-of-interest. Convolutional neural networks have de…

cs.CV20214 cited

SOAT: A Scene- and Object-Aware Transformer for Vision-and-Language Navigation

Abhinav Moudgil, Arjun Majumdar, Harsh Agrawal +2

Natural language instructions for visual navigation often use scene descriptions (e.g., "bedroom") and object references (e.g., "green chairs") to provide a breadcrumb trail to a g…