Search Results for author: Shuo Liu

Found 19 papers, 2 papers with code

An adaptive graph learning method for automated molecular interactions and properties predictions

1 code implementation Nature Machine Intelligence 2022 Yuquan Li, Chang-Yu Hsieh, Ruiqiang Lu, Xiaoqing Gong, Xiaorui Wang, Pengyong Li, Shuo Liu, Yanan Tian, Dejun Jiang, Jiaxian Yan, Qifeng Bai, Huanxiang Liu, Shengyu Zhang, Xiaojun Yao

In fact, the pursuit of high prediction performance on a limited number of datasets has crystallized their architectures and hyperparameters, making them lose advantage in repurposing to new data generated in drug discovery.

Drug Discovery Graph Learning +2

A Temporal-oriented Broadcast ResNet for COVID-19 Detection

no code implementations31 Mar 2022 Xin Jing, Shuo Liu, Emilia Parada-Cabaleiro, Andreas Triantafyllopoulos, Meishu Song, Zijiang Yang, Björn W. Schuller

Detecting COVID-19 from audio signals, such as breathing and coughing, can be used as a fast and efficient pre-testing method to reduce the virus transmission.

Audio Self-supervised Learning: A Survey

no code implementations2 Mar 2022 Shuo Liu, Adria Mallol-Ragolta, Emilia Parada-Cabeleiro, Kun Qian, Xin Jing, Alexander Kathan, Bin Hu, Bjoern W. Schuller

Inspired by the humans' cognitive ability to generalise knowledge and skills, Self-Supervised Learning (SSL) targets at discovering general representations from large-scale data without requiring human annotations, which is an expensive and time consuming task.

Natural Language Processing Self-Supervised Learning

Utilizing Redundancy in Cost Functions for Resilience in Distributed Optimization and Learning

no code implementations21 Oct 2021 Shuo Liu, Nirupam Gupta, Nitin Vaidya

We demonstrate, both theoretically and empirically, the merits of our proposed redundancy model in improving the robustness of DGD against asynchronous and Byzantine agents, and their extensions to distributed stochastic gradient descent (D-SGD) for robust distributed machine learning with asynchronous and Byzantine agents.

Distributed Optimization

Semi-supervised Multi-task Learning for Semantics and Depth

no code implementations14 Oct 2021 Yufeng Wang, Yi-Hsuan Tsai, Wei-Chih Hung, Wenrui Ding, Shuo Liu, Ming-Hsuan Yang

Multi-Task Learning (MTL) aims to enhance the model generalization by sharing representations between related tasks for better performance.

Depth Estimation Multi-Task Learning +1

Multistage linguistic conditioning of convolutional layers for speech emotion recognition

no code implementations13 Oct 2021 Andreas Triantafyllopoulos, Uwe Reichel, Shuo Liu, Stephan Huber, Florian Eyben, Björn W. Schuller

In this contribution, we investigate the effectiveness of deep fusion of text and audio features for categorical and dimensional speech emotion recognition (SER).

Speech Emotion Recognition

On designing finite time iterative learning control based on steady state frequency response

no code implementations6 Oct 2021 Shuo Liu, Richard W. Longman, Benjamas Panomruttanarug

Iterative Learning Control (ILC) is useful in spacecraft application for repeated high precision scanning maneuvers.

Modifying and optimizing the inverse of the frequency response circulant matrix as an iterative learning control compensator

no code implementations6 Oct 2021 Shuo Liu, Richard W. Longman

The purpose of this paper is to create a method of designing ILC compensators based on steady state frequency response, and have the ILC converge to zero error in spite of transients and bandwidth.

Byzantine Fault-Tolerant Distributed Machine Learning Using Stochastic Gradient Descent (SGD) and Norm-Based Comparative Gradient Elimination (CGE)

no code implementations11 Aug 2020 Nirupam Gupta, Shuo Liu, Nitin H. Vaidya

We show that the CGE gradient-filter guarantees fault-tolerance against a bounded fraction of Byzantine agents under standard stochastic assumptions, and is computationally simpler compared to many existing gradient-filters such as multi-KRUM, geometric median-of-means, and the spectral filters.

An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety

no code implementations30 Apr 2020 Jing Han, Kun Qian, Meishu Song, Zijiang Yang, Zhao Ren, Shuo Liu, Juan Liu, Huaiyuan Zheng, Wei Ji, Tomoya Koike, Xiao Li, Zixing Zhang, Yoshiharu Yamamoto, Björn W. Schuller

In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety.

Sleep Quality

N-HANS: Introducing the Augsburg Neuro-Holistic Audio-eNhancement System

1 code implementation16 Nov 2019 Shuo Liu, Gil Keren, Björn Schuller

N-HANS is a Python toolkit for in-the-wild audio enhancement, including speech, music, and general audio denoising, separation, and selective noise or source suppression.

Sound Audio and Speech Processing

AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition

no code implementations10 Jul 2019 Fabien Ringeval, Björn Schuller, Michel Valstar, NIcholas Cummins, Roddy Cowie, Leili Tavabi, Maximilian Schmitt, Sina Alisamir, Shahin Amiriparian, Eva-Maria Messner, Siyang Song, Shuo Liu, Ziping Zhao, Adria Mallol-Ragolta, Zhao Ren, Mohammad Soleymani, Maja Pantic

The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) "State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition" is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions.

Emotion Recognition

Single-Channel Speech Separation with Auxiliary Speaker Embeddings

no code implementations24 Jun 2019 Shuo Liu, Gil Keren, Björn Schuller

We present a novel source separation model to decompose asingle-channel speech signal into two speech segments belonging to two different speakers.

Speech Separation

IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation

no code implementations25 Jun 2018 Shuo Liu, Vijay John, Erik Blasch, Zheng Liu, Ying Huang

Context enhancement is critical for night vision (NV) applications, especially for the dark night situation without any artificial lights.

Translation

Efficient Traffic-Sign Recognition with Scale-aware CNN

no code implementations31 May 2018 Yuchen Yang, Shuo Liu, Wei Ma, Qiuyuan Wang, Zheng Liu

The paper presents a Traffic Sign Recognition (TSR) system, which can fast and accurately recognize traffic signs of different sizes in images.

General Classification Traffic Sign Recognition

Multi-Channel CNN-based Object Detection for Enhanced Situation Awareness

no code implementations30 Nov 2017 Shuo Liu, Zheng Liu

In this study, we propose a novel detection algorithm for military objects by fusing multi-channel CNNs.

object-detection Object Detection +1

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