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检索条件"机构=Signal Processing and Machine Learning Lab."
40 条 记 录,以下是1-10 订阅
排序:
On the Robustness of Out-of-Distribution Detection Methods for Camera-based Systems  58
On the Robustness of Out-of-Distribution Detection Methods f...
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58th Asilomar Conference on signals, Systems and Computers, ACSSC 2024
作者: Huber, Christian Lehner, Bernhard Hofmann, Claus Moser, Bernhard Feger, Reinhard Silicon Austria Labs GmbH Jku Lit Sal eSPML Lab Austria Johannes Kepler University Linz Jku Lit Sal eSPML Lab Austria Institute for Machine Learning Austria Institute of Signal Processing China Institute for Communications Engineering and RF-Systems China
Out-of-distribution (OOD) detection refers to recognizing instances that lie outside the scope of what a machine learning model has been exposed to during training. In safetycritical domains like autonomous driving, O... 详细信息
来源: 评论
A Neuro-Symbolic Approach for Marine Vessels Power Prediction Under Distribution Shifts
A Neuro-Symbolic Approach for Marine Vessels Power Predictio...
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2023 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, JEEIT 2023
作者: Hammoudeh, Ahmad Ghannam, Ibrahim Mubarak, Hamza Jean, Emmanuael Vandenbulcke, Virginie Dupont, Stephane MAiA Lab ISlA Lab Umons Trail Belgium RWTH Aachen University Electrical Engineering Germany Griffith University Engineering and Built Environment Australia Machine Learning & Signal Processing Multitel Trail Belgium Umons Isia Lab Belgium Umons Maia Lab Belgium
This paper proposes a neuro-symbolic approach to predict the power of marine cargo vessels. The neuro-symbolic approach combines two parts. The first is a neural networks part, and the second is a symbolic part that r... 详细信息
来源: 评论
Revealing Emotional Clusters in Speaker Embeddings: A Contrastive learning Strategy for Speech Emotion Recognition
Revealing Emotional Clusters in Speaker Embeddings: A Contra...
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International Conference on Acoustics, Speech, and signal processing (ICASSP)
作者: Ismail Rasim Ulgen Zongyang Du Carlos Busso Berrak Sisman Speech & Machine Learning (SML) Lab The University of Texas at Dallas USA Multimodal Signal Processing (MSP) Lab The University of Texas at Dallas USA
Speaker embeddings carry valuable emotion-related information, which makes them a promising resource for enhancing speech emotion recognition (SER), especially with limited lab.led data. Traditionally, it has been ass...
来源: 评论
TurkishMMLU: Measuring Massive Multitask Language Understanding in Turkish
arXiv
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arXiv 2024年
作者: Yüksel, Arda Köksal, Abdullatif Senel, Lütfi Kerem Korhonen, Anna Schütze, Hinrich Technical University of Munich Germany Center for Information and Language Processing LMU Munich Germany Munich Center for Machine Learning Germany Language Technology Lab. University of Cambridge United Kingdom
Multiple choice question answering tasks evaluate the reasoning, comprehension, and mathematical abilities of Large Language Models (LLMs). While existing benchmarks employ automatic translation for multilingual evalu... 详细信息
来源: 评论
On the Robustness of Out-of-Distribution Detection Methods for Camera-based Systems
On the Robustness of Out-of-Distribution Detection Methods f...
收藏 引用
Asilomar Conference on signals, Systems & Computers
作者: Christian Huber Bernhard Lehner Claus Hofmann Bernhard Moser Reinhard Feger Silicon Austria Labs GmbH JKU LIT SAL eSPML Lab Austria JKU LIT SAL eSPML Lab Johannes Kepler University Linz Austria Institute for Machine Learning Institute of Signal Processing Institute for Communications Engineering and RF-Systems
Out-of-distribution (OOD) detection refers to recognizing instances that lie outside the scope of what a machine learning model has been exposed to during training. In safetycritical domains like autonomous driving, O... 详细信息
来源: 评论
A Neuro-Symbolic Approach for Marine Vessels Power Prediction Under Distribution Shifts
A Neuro-Symbolic Approach for Marine Vessels Power Predictio...
收藏 引用
IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT)
作者: Ahmad Hammoudeh Ibrahim Ghannam Hamza Mubarak Emmanuael Jean Virginie Vandenbulcke Stephane Dupont MAiA Lab ISlA Lab UMONS TRAIL Belgium Electrical Engineering RWTH Aachen University Germany Engineering and Built Environment Griffith University Australia Machine Learning & Signal Processing Multitel TRAIL Belgium ISIA Lab UMONS Belgium MAIA Lab UMONS Belgium
This paper proposes a neuro-symbolic approach to predict the power of marine cargo vessels. The neuro-symbolic approach combines two parts. The first is a neural networks part, and the second is a symbolic part that r...
来源: 评论
learning Fair Representations through Uniformly Distributed Sensitive Attributes
Learning Fair Representations through Uniformly Distributed ...
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Secure and Trustworthy machine learning (SaTML), IEEE Conference on
作者: Patrik Joslin Kenfack Adín Ramírez Rivera Adil Mehmood Khan Manuel Mazzara Machine Learning and Knowledge Representation Lab Innopolis University Innopolis Russia Department of Informatics Digital Signal Processing and Image Analysis (DSB) group University of Oslo Oslo Norway School of Computer Science University of Hull Hull UK Institute of Software Development and Engineering Innopolis University Innopolis Russia
machine learning (ML) models trained on biased data can reproduce and even amplify these biases. Since such models are deployed to make decisions that can affect people's lives, ensuring their fairness is critical...
来源: 评论
Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder
arXiv
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arXiv 2022年
作者: Tian, Yu Pang, Guansong Liu, Yuyuan Wang, Chong Chen, Yuanhong Liu, Fengbei Singh, Rajvinder Verjans, Johan W. Wang, Mengyu Carneiro, Gustavo Harvard Ophthalmology AI Lab Harvard University United States Australian Institute for Machine Learning University of Adelaide Australia Faculty of Health and Medical Sciences University of Adelaide Australia South Australian Health and Medical Research Institute Australia Singapore Management University Singapore Centre for Vision Speech and Signal Processing University of Surrey United Kingdom
Unsupervised anomaly detection (UAD) aims to find anomalous images by optimising a detector using a training set that contains only normal images. UAD approaches can be based on reconstruction methods, self-supervised... 详细信息
来源: 评论
Adversarial learning with Cost-Sensitive Classes
arXiv
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arXiv 2021年
作者: Shen, Haojing Chen, Sihong Wang, Ran Wang, Xizhao Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China The College of Mathematics and Statistics Shenzhen University Shenzhen518060 China The Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China
It is necessary to improve the performance of some special classes or to particularly protect them from attacks in adversarial learning. This paper proposes a framework combining cost-sensitive classification and adve... 详细信息
来源: 评论
ScanMix: learning from Severe lab.l Noise via Semantic Clustering and Semi-Supervised learning
arXiv
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arXiv 2021年
作者: Sachdeva, Ragav Cordeiro, Filipe Rolim Belagiannis, Vasileios Reid, Ian Carneiro, Gustavo Visual Geometry Group Department of Engineering Science University of Oxford United Kingdom School of Computer Science Australian Institute for Machine Learning Australia Visual Computing Lab Department of Computing Universidade Federal Rural de Pernambuco Brazil Otto-von-Guericke-Universität Magdeburg Germany Centre for Vision Speech and Signal Processing University of Surrey United Kingdom
We propose a new training algorithm, ScanMix, that explores semantic clustering and semi-supervised learning (SSL) to allow superior robustness to severe lab.l noise and competitive robustness to non-severe lab.l nois... 详细信息
来源: 评论