Search Results for author: Qichao Wang

Found 6 papers, 1 papers with code

Recent Advances in Speech Language Models: A Survey

no code implementations1 Oct 2024 Wenqian Cui, Dianzhi Yu, Xiaoqi Jiao, Ziqiao Meng, Guangyan Zhang, Qichao Wang, Yiwen Guo, Irwin King

To address these issues, Speech Language Models (SpeechLMs) -- end-to-end models that generate speech without converting from text -- have emerged as a promising alternative.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Language Agents for Detecting Implicit Stereotypes in Text-to-image Models at Scale

no code implementations18 Oct 2023 Qichao Wang, Tian Bian, Yian Yin, Tingyang Xu, Hong Cheng, Helen M. Meng, Zibin Zheng, Liang Chen, Bingzhe Wu

The recent surge in the research of diffusion models has accelerated the adoption of text-to-image models in various Artificial Intelligence Generated Content (AIGC) commercial products.

A Machine Learning Method for Predicting Traffic Signal Timing from Probe Vehicle Data

no code implementations4 Aug 2023 Juliette Ugirumurera, Joseph Severino, Erik A. Bensen, Qichao Wang, Jane Macfarlane

In this paper, we present a machine learning (ML) method for estimating traffic signal timing information from vehicle probe data.

Management

Attention Paper: How Generative AI Reshapes Digital Shadow Industry?

no code implementations26 May 2023 Qichao Wang, Huan Ma, WenTao Wei, Hangyu Li, Liang Chen, Peilin Zhao, Binwen Zhao, Bo Hu, Shu Zhang, Zibin Zheng, Bingzhe Wu

The rapid development of digital economy has led to the emergence of various black and shadow internet industries, which pose potential risks that can be identified and managed through digital risk management (DRM) that uses different techniques such as machine learning and deep learning.

Management

Prioritized Guidance for Efficient Multi-Agent Reinforcement Learning Exploration

no code implementations18 Jul 2019 Qisheng Wang, Qichao Wang

Exploration efficiency is a challenging problem in multi-agent reinforcement learning (MARL), as the policy learned by confederate MARL depends on the collaborative approach among multiple agents.

Multi-agent Reinforcement Learning reinforcement-learning +2

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