no code implementations • 20 Sep 2023 • Tianyi Han, Jingya Li, Zhipeng Guo, Yuan Jin
Derivative-free optimization algorithms play an important role in scientific and engineering design optimization problems, especially when derivative information is not accessible.
1 code implementation • 30 Aug 2023 • Jianning Li, Zongwei Zhou, Jiancheng Yang, Antonio Pepe, Christina Gsaxner, Gijs Luijten, Chongyu Qu, Tiezheng Zhang, Xiaoxi Chen, Wenxuan Li, Marek Wodzinski, Paul Friedrich, Kangxian Xie, Yuan Jin, Narmada Ambigapathy, Enrico Nasca, Naida Solak, Gian Marco Melito, Viet Duc Vu, Afaque R. Memon, Christopher Schlachta, Sandrine de Ribaupierre, Rajnikant Patel, Roy Eagleson, Xiaojun Chen, Heinrich Mächler, Jan Stefan Kirschke, Ezequiel de la Rosa, Patrick Ferdinand Christ, Hongwei Bran Li, David G. Ellis, Michele R. Aizenberg, Sergios Gatidis, Thomas Küstner, Nadya Shusharina, Nicholas Heller, Vincent Andrearczyk, Adrien Depeursinge, Mathieu Hatt, Anjany Sekuboyina, Maximilian Löffler, Hans Liebl, Reuben Dorent, Tom Vercauteren, Jonathan Shapey, Aaron Kujawa, Stefan Cornelissen, Patrick Langenhuizen, Achraf Ben-Hamadou, Ahmed Rekik, Sergi Pujades, Edmond Boyer, Federico Bolelli, Costantino Grana, Luca Lumetti, Hamidreza Salehi, Jun Ma, Yao Zhang, Ramtin Gharleghi, Susann Beier, Arcot Sowmya, Eduardo A. Garza-Villarreal, Thania Balducci, Diego Angeles-Valdez, Roberto Souza, Leticia Rittner, Richard Frayne, Yuanfeng Ji, Vincenzo Ferrari, Soumick Chatterjee, Florian Dubost, Stefanie Schreiber, Hendrik Mattern, Oliver Speck, Daniel Haehn, Christoph John, Andreas Nürnberger, João Pedrosa, Carlos Ferreira, Guilherme Aresta, António Cunha, Aurélio Campilho, Yannick Suter, Jose Garcia, Alain Lalande, Vicky Vandenbossche, Aline Van Oevelen, Kate Duquesne, Hamza Mekhzoum, Jef Vandemeulebroucke, Emmanuel Audenaert, Claudia Krebs, Timo Van Leeuwen, Evie Vereecke, Hauke Heidemeyer, Rainer Röhrig, Frank Hölzle, Vahid Badeli, Kathrin Krieger, Matthias Gunzer, Jianxu Chen, Timo van Meegdenburg, Amin Dada, Miriam Balzer, Jana Fragemann, Frederic Jonske, Moritz Rempe, Stanislav Malorodov, Fin H. Bahnsen, Constantin Seibold, Alexander Jaus, Zdravko Marinov, Paul F. Jaeger, Rainer Stiefelhagen, Ana Sofia Santos, Mariana Lindo, André Ferreira, Victor Alves, Michael Kamp, Amr Abourayya, Felix Nensa, Fabian Hörst, Alexander Brehmer, Lukas Heine, Yannik Hanusrichter, Martin Weßling, Marcel Dudda, Lars E. Podleska, Matthias A. Fink, Julius Keyl, Konstantinos Tserpes, Moon-Sung Kim, Shireen Elhabian, Hans Lamecker, Dženan Zukić, Beatriz Paniagua, Christian Wachinger, Martin Urschler, Luc Duong, Jakob Wasserthal, Peter F. Hoyer, Oliver Basu, Thomas Maal, Max J. H. Witjes, Gregor Schiele, Ti-chiun Chang, Seyed-Ahmad Ahmadi, Ping Luo, Bjoern Menze, Mauricio Reyes, Thomas M. Deserno, Christos Davatzikos, Behrus Puladi, Pascal Fua, Alan L. Yuille, Jens Kleesiek, Jan Egger
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
no code implementations • 9 Aug 2023 • Wentao Zhu, Yuan Jin, Gege Ma, Geng Chen, Jan Egger, Shaoting Zhang, Dimitris N. Metaxas
The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements.
no code implementations • 23 Dec 2022 • Fucai Ke, Weiqing Wang, Weicong Tan, Lan Du, Yuan Jin, Yujin Huang, Hongzhi Yin
Knowledge tracing (KT) aims to leverage students' learning histories to estimate their mastery levels on a set of pre-defined skills, based on which the corresponding future performance can be accurately predicted.
1 code implementation • 7 Nov 2022 • Erxin Yu, Lan Du, Yuan Jin, Zhepei Wei, Yi Chang
Recently, discrete latent variable models have received a surge of interest in both Natural Language Processing (NLP) and Computer Vision (CV), attributed to their comparable performance to the continuous counterparts in representation learning, while being more interpretable in their predictions.
no code implementations • 6 Sep 2022 • Christina Gsaxner, Jianning Li, Antonio Pepe, Yuan Jin, Jens Kleesiek, Dieter Schmalstieg, Jan Egger
The HoloLens (Microsoft Corp., Redmond, WA), a head-worn, optically see-through augmented reality display, is the main player in the recent boost in medical augmented reality research.
no code implementations • Findings (EMNLP) 2021 • Kelvin Lo, Yuan Jin, Weicong Tan, Ming Liu, Lan Du, Wray Buntine
This paper proposes a transformer over transformer framework, called Transformer$^2$, to perform neural text segmentation.
no code implementations • EMNLP 2021 • Yuan Jin, He Zhao, Ming Liu, Lan Du, Wray Buntine
Neural topic models (NTMs) apply deep neural networks to topic modelling.
no code implementations • Findings (EMNLP) 2021 • Jiaxin Ju, Ming Liu, Huan Yee Koh, Yuan Jin, Lan Du, Shirui Pan
This paper presents an unsupervised extractive approach to summarize scientific long documents based on the Information Bottleneck principle.
1 code implementation • 11 Aug 2021 • Jianning Li, Antonio Pepe, Christina Gsaxner, Yuan Jin, Jan Egger
However, downsampling can lead to a loss of image quality, which is undesirable especially in reconstruction tasks, where the fine geometric details need to be preserved.
no code implementations • 6 Aug 2021 • Yuan Jin, Antonio Pepe, Jianning Li, Christina Gsaxner, Fen-hua Zhao, Kelsey L. Pomykala, Jens Kleesiek, Alejandro F. Frangi, Jan Egger
The standard imaging modality for diagnosis and monitoring is computed tomography (CT), which can provide a detailed picture of the aorta and its branching vessels if completed with a contrast agent, called CT angiography (CTA).
no code implementations • 27 Jul 2021 • Ming Liu, Stella Ho, Mengqi Wang, Longxiang Gao, Yuan Jin, He Zhang
Recent Natural Language Processing techniques rely on deep learning and large pre-trained language models.
no code implementations • 28 Feb 2021 • He Zhao, Dinh Phung, Viet Huynh, Yuan Jin, Lan Du, Wray Buntine
Topic modelling has been a successful technique for text analysis for almost twenty years.
no code implementations • 16 Nov 2020 • Jan Egger, Antonio Pepe, Christina Gsaxner, Yuan Jin, Jianning Li, Roman Kern
These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years.
no code implementations • 12 Nov 2020 • Yuan Jin, Wray Buntine, Francois Petitjean, Geoffrey I. Webb
For this task, we survey a wide range of techniques available for handling missing values, self-supervised training and pseudo-likelihood training, and adapt them to a suite of algorithms that are suitable for the task.
1 code implementation • 17 Jul 2020 • Jinming Zhao, Ming Liu, Longxiang Gao, Yuan Jin, Lan Du, He Zhao, He Zhang, Gholamreza Haffari
Obtaining training data for multi-document summarization (MDS) is time consuming and resource-intensive, so recent neural models can only be trained for limited domains.
no code implementations • 15 Jun 2020 • Ce Ju, Ruihui Zhao, Jichao Sun, Xiguang Wei, Bo Zhao, Yang Liu, Hongshan Li, Tianjian Chen, Xinwei Zhang, Dashan Gao, Ben Tan, Han Yu, Chuning He, Yuan Jin
It adopts federated averaging during the model training process, without patient data being taken out of the hospitals during the whole process of model training and forecasting.
no code implementations • 21 Feb 2020 • Yuan Jin, He Zhao, Ming Liu, Ye Zhu, Lan Du, Longxiang Gao, He Zhang, Yunfeng Li
Based on the ELBOs, we propose a VAE-based Bayesian MF framework.
no code implementations • 12 Oct 2019 • Yuan Jin, Ming Liu, Yunfeng Li, Ruohua Xu, Lan Du, Longxiang Gao, Yong Xiang
Under synthetic data evaluation, VAE-BPTF tended to recover the right number of latent factors and posterior parameter values.
no code implementations • 5 Dec 2018 • Yuan Jin, Mark Carman, Ye Zhu, Yong Xiang
Our survey is the first to bridge the two branches by providing technical details on how they work together under frameworks that systematically unify crowdsourcing aspects modelled by both of them to determine the response quality.
1 code implementation • 8 Oct 2018 • Ye Zhu, Kai Ming Ting, Yuan Jin, Maia Angelova
This paper focuses on density-based clustering, particularly the Density Peak (DP) algorithm and the one based on density-connectivity DBSCAN; and proposes a new method which takes advantage of the individual strengths of these two methods to yield a density-based hierarchical clustering algorithm.
no code implementations • 12 Feb 2018 • Yuan Jin, Mark Carman, Ye Zhu, Wray Buntine
Experiments show that our model(1) improves the performance of both quality control for crowd-sourced answers and next answer prediction for crowd-workers, and (2) can potentially provide coherent rankings of questions in terms of their difficulty and subjectivity, so that task providers can refine their designs of the crowdsourcing tasks, e. g. by removing highly subjective questions or inappropriately difficult questions.