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20162023
most citedInstructUIE: Multi-task Instruction Tuning for Unified Information Extraction

48 citations · 58 across the 16 of their papers we have counts for

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Showing 2023Show all

12 papers · 1 filter

cs.CV2023

Prompt-based Ingredient-Oriented All-in-One Image Restoration

Hu Gao, Depeng Dang

Image restoration aims to recover the high-quality images from their degraded observations. Since most existing methods have been dedicated into single degradation removal, they ma…

physics.optics2023

Switchable polarization manipulation, optical logical gates and conveyor belt based on U-shaped $\ce{VO2}$ nanoholes

Xiao-Yu Ouyang, Jing Yang

Based on U-shaped plasmonic nanoholes in an $\ce{Au-VO2-Au}$ film, we propose to achieve several switchable functions at the telecom wavelength by transition from the $\ce{VO2}$ se…

cs.LG2023

Provably Efficient Algorithm for Nonstationary Low-Rank MDPs

Yuan Cheng, Jing Yang, Yingbin Liang

Reinforcement learning (RL) under changing environment models many real-world applications via nonstationary Markov Decision Processes (MDPs), and hence gains considerable interest…

cs.LG2023

Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games

Songtao Feng, Ming Yin, Yu-Xiang Wang +2

The problem of two-player zero-sum Markov games has recently attracted increasing interests in theoretical studies of multi-agent reinforcement learning (RL). In particular, for fi…

cs.HC2023

TimePool: Visually Answer "Which and When" Questions On Univariate Time Series

Tinghao Feng, Yueqi Hu, Jing Yang +4

When exploring time series datasets, analysts often pose "which and when" questions. For example, with world life expectancy data over one hundred years, they may inquire about the…

cs.LG2023

Provably Efficient UCB-type Algorithms For Learning Predictive State Representations

Ruiquan Huang, Yingbin Liang, Jing Yang

The general sequential decision-making problem, which includes Markov decision processes (MDPs) and partially observable MDPs (POMDPs) as special cases, aims at maximizing a cumula…