Search Results for author: Yixuan Zhang

Found 24 papers, 6 papers with code

Out-of-Distribution Knowledge Distillation via Confidence Amendment

1 code implementation14 Nov 2023 Zhilin Zhao, Longbing Cao, Yixuan Zhang

This paper introduces OOD knowledge distillation, a pioneering learning framework applicable whether or not training ID data is available, given a standard network.

Knowledge Distillation Out of Distribution (OOD) Detection

CompeteAI: Understanding the Competition Behaviors in Large Language Model-based Agents

no code implementations26 Oct 2023 Qinlin Zhao, Jindong Wang, Yixuan Zhang, Yiqiao Jin, Kaijie Zhu, Hao Chen, Xing Xie

Large language models (LLMs) have been widely used as agents to complete different tasks, such as personal assistance or event planning.

Language Modelling Large Language Model

Can Large Language Model Comprehend Ancient Chinese? A Preliminary Test on ACLUE

no code implementations14 Oct 2023 Yixuan Zhang, Haonan Li

To bridge this gap, we present ACLUE, an evaluation benchmark designed to assess the capability of language models in comprehending ancient Chinese.

Language Modelling Large Language Model +1

MetaAgents: Simulating Interactions of Human Behaviors for LLM-based Task-oriented Coordination via Collaborative Generative Agents

no code implementations10 Oct 2023 Yuan Li, Yixuan Zhang, Lichao Sun

We propose a novel framework that equips collaborative generative agents with human-like reasoning abilities and specialized skills.

Advancing Acoustic Howling Suppression through Recursive Training of Neural Networks

no code implementations27 Sep 2023 Hao Zhang, Yixuan Zhang, Meng Yu, Dong Yu

In this paper, we introduce a novel training framework designed to comprehensively address the acoustic howling issue by examining its fundamental formation process.

Acoustic echo cancellation

Neural Network Augmented Kalman Filter for Robust Acoustic Howling Suppression

no code implementations27 Sep 2023 Yixuan Zhang, Hao Zhang, Meng Yu, Dong Yu

Acoustic howling suppression (AHS) is a critical challenge in audio communication systems.

Large Language Models Understand and Can be Enhanced by Emotional Stimuli

no code implementations14 Jul 2023 Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, Xing Xie

In addition to those deterministic tasks that can be automatically evaluated using existing metrics, we conducted a human study with 106 participants to assess the quality of generative tasks using both vanilla and emotional prompts.

Emotional Intelligence Informativeness

CMMLU: Measuring massive multitask language understanding in Chinese

1 code implementation15 Jun 2023 Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin

As the capabilities of large language models (LLMs) continue to advance, evaluating their performance becomes increasingly crucial and challenging.

Large Language Model

Hierarchical Optimization-Derived Learning

no code implementations11 Feb 2023 Risheng Liu, Xuan Liu, Shangzhi Zeng, Jin Zhang, Yixuan Zhang

In recent years, by utilizing optimization techniques to formulate the propagation of deep model, a variety of so-called Optimization-Derived Learning (ODL) approaches have been proposed to address diverse learning and vision tasks.

NeuralKalman: A Learnable Kalman Filter for Acoustic Echo Cancellation

no code implementations29 Jan 2023 Yixuan Zhang, Meng Yu, Hao Zhang, Dong Yu, DeLiang Wang

The Kalman filter is widely used for addressing acoustic echo cancellation (AEC) problems due to their robustness to double-talk and fast convergence.

Acoustic echo cancellation

De-biased Representation Learning for Fairness with Unreliable Labels

no code implementations1 Aug 2022 Yixuan Zhang, Feng Zhou, Zhidong Li, Yang Wang, Fang Chen

In other words, the fair pre-processing methods ignore the discrimination encoded in the labels either during the learning procedure or the evaluation stage.

Fairness Representation Learning

Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training

no code implementations16 Jun 2022 Risheng Liu, Xuan Liu, Shangzhi Zeng, Jin Zhang, Yixuan Zhang

Recently, Optimization-Derived Learning (ODL) has attracted attention from learning and vision areas, which designs learning models from the perspective of optimization.

Image Deconvolution

Continuous Speech Separation with Recurrent Selective Attention Network

no code implementations28 Oct 2021 Yixuan Zhang, Zhuo Chen, Jian Wu, Takuya Yoshioka, Peidong Wang, Zhong Meng, Jinyu Li

In this paper, we propose to apply recurrent selective attention network (RSAN) to CSS, which generates a variable number of output channels based on active speaker counting.

speech-recognition Speech Recognition +1

Value-Function-based Sequential Minimization for Bi-level Optimization

1 code implementation11 Oct 2021 Risheng Liu, Xuan Liu, Shangzhi Zeng, Jin Zhang, Yixuan Zhang

We also extend BVFSM to address BLO with additional functional constraints.

Bias-Tolerant Fair Classification

no code implementations7 Jul 2021 Yixuan Zhang, Feng Zhou, Zhidong Li, Yang Wang, Fang Chen

Therefore, we propose a Bias-TolerantFAirRegularizedLoss (B-FARL), which tries to regain the benefits using data affected by label bias and selection bias.

Classification Fairness +2

Nonlinear Hawkes Processes in Time-Varying System

no code implementations9 Jun 2021 Feng Zhou, Quyu Kong, Yixuan Zhang, Cheng Feng, Jun Zhu

Hawkes processes are a class of point processes that have the ability to model the self- and mutual-exciting phenomena.

Bayesian Inference Point Processes +1

Efficient Inference of Flexible Interaction in Spiking-neuron Networks

no code implementations ICLR 2021 Feng Zhou, Yixuan Zhang, Jun Zhu

Hawkes process provides an effective statistical framework for analyzing the time-dependent interaction of neuronal spiking activities.

Digital Collaborator: Augmenting Task Abstraction in Visualization Design with Artificial Intelligence

no code implementations3 Mar 2020 Aditeya Pandey, Yixuan Zhang, John A. Guerra-Gomez, Andrea G. Parker, Michelle A. Borkin

In the task abstraction phase of the visualization design process, including in "design studies", a practitioner maps the observed domain goals to generalizable abstract tasks using visualization theory in order to better understand and address the users needs.

Fine-grained Image-to-Image Transformation towards Visual Recognition

no code implementations CVPR 2020 Wei Xiong, Yutong He, Yixuan Zhang, Wenhan Luo, Lin Ma, Jiebo Luo

In this paper, we aim at transforming an image with a fine-grained category to synthesize new images that preserve the identity of the input image, which can thereby benefit the subsequent fine-grained image recognition and few-shot learning tasks.

Few-Shot Learning Fine-Grained Image Recognition

Fashion Editing with Adversarial Parsing Learning

no code implementations CVPR 2020 Haoye Dong, Xiaodan Liang, Yixuan Zhang, Xujie Zhang, Zhenyu Xie, Bowen Wu, Ziqi Zhang, Xiaohui Shen, Jian Yin

Interactive fashion image manipulation, which enables users to edit images with sketches and color strokes, is an interesting research problem with great application value.

Human Parsing Image Manipulation

Human-Centered Emotion Recognition in Animated GIFs

1 code implementation27 Apr 2019 Zhengyuan Yang, Yixuan Zhang, Jiebo Luo

The framework consists of a facial attention module and a hierarchical segment temporal module.

Emotion Recognition

Boundary-based Image Forgery Detection by Fast Shallow CNN

1 code implementation20 Jan 2018 Zhongping Zhang, Yixuan Zhang, Zheng Zhou, Jiebo Luo

In this paper, we substantiate that Fast SCNN can detect drastic change of chroma and saturation.

Demosaicking Image Forgery Detection

End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perception

1 code implementation20 Jan 2018 Zhengyuan Yang, Yixuan Zhang, Jerry Yu, Junjie Cai, Jiebo Luo

In this work, we propose a multi-task learning framework to predict the steering angle and speed control simultaneously in an end-to-end manner.

Autonomous Driving Multi-Task Learning +2

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