Search Results for author: Yusong Wang

Found 8 papers, 2 papers with code

Active Learning with Task Adaptation Pre-training for Speech Emotion Recognition

1 code implementation1 May 2024 Dongyuan Li, Ying Zhang, Yusong Wang, Funakoshi Kataro, Manabu Okumura

To address these issues, we propose an active learning (AL)-based fine-tuning framework for SER, called \textsc{After}, that leverages task adaptation pre-training (TAPT) and AL methods to enhance performance and efficiency.

Active Learning Speech Emotion Recognition +2

F$^3$low: Frame-to-Frame Coarse-grained Molecular Dynamics with SE(3) Guided Flow Matching

no code implementations1 May 2024 Shaoning Li, Yusong Wang, Mingyu Li, Jian Zhang, Bin Shao, Nanning Zheng, Jian Tang

Molecular dynamics (MD) is a crucial technique for simulating biological systems, enabling the exploration of their dynamic nature and fostering an understanding of their functions and properties.

Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion Recognition

no code implementations18 Nov 2023 Dongyuan Li, Yusong Wang, Kotaro Funakoshi, Manabu Okumura

In this paper, we propose a method for joint modality fusion and graph contrastive learning for multimodal emotion recognition (Joyful), where multimodality fusion, contrastive learning, and emotion recognition are jointly optimized.

Contrastive Learning Multimodal Emotion Recognition

Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition

no code implementations30 Sep 2023 Dongyuan Li, Yusong Wang, Kotaro Funakoshi, Manabu Okumura

However, existing SER methods ignore the information gap between the pre-training speech recognition task and the downstream SER task, leading to sub-optimal performance.

Active Learning Speech Emotion Recognition +2

Direct Molecular Conformation Generation

1 code implementation3 Feb 2022 Jinhua Zhu, Yingce Xia, Chang Liu, Lijun Wu, Shufang Xie, Yusong Wang, Tong Wang, Tao Qin, Wengang Zhou, Houqiang Li, Haiguang Liu, Tie-Yan Liu

Molecular conformation generation aims to generate three-dimensional coordinates of all the atoms in a molecule and is an important task in bioinformatics and pharmacology.

Molecular Docking

Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer

no code implementations14 Oct 2021 Siyuan Liu, Yusong Wang, Tong Wang, Yifan Deng, Liang He, Bin Shao, Jian Yin, Nanning Zheng, Tie-Yan Liu

The identification of active binding drugs for target proteins (termed as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery.

Drug Discovery Molecular Docking +1

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