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Proceedings of the 41st International Conference on Machine Learning

作者： Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel David Dunson Maurizio Filippone Vincent Fortuin Philipp Hennig José Miguel Hernández-Lobato Aliaksandr Hubin Alexander Immer Theofanis Karaletsos Mohammad Emtiyaz Khan Agustinus Kristiadi Yingzhen Li Stephan Mandt Christopher Nemeth Michael A. Osborne Tim G. J. Rudner David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang Department of Mathematics The University of Manchester Manchester UK Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge Spotify London UK Computational Neuroscience Unit University of Bristol Bristol UK Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany and Department of Computer Science Technical University of Munich Munich Germany and Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge UK Department of Mathematics University of Oslo Oslo Norway and Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative California Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London UK Department of Computer Science UC Irvine Irvine Department of Mathematics and Statistics Lancaster University Lancaster UK Department of Engineering Science University of Oxford Oxford UK Center for Data Science New York University New York Munich Center for Machine Learning Munich Germany and Department of Statistics LMU Munich Munich Germany DeepMind London UK and Department of Statistics University of Oxford Oxford UK Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences and Center for Data Science Computer Science Department New York University New York Department of Computer Science Purdue University West Lafayette

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.

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Engineering the directionality of hot carrier tunneling in plasmonic tunneling structures

arXiv

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arXiv 2023年

作者： Abbasi, Mahdiyeh Liao, Shusen Zhu, Yunxuan Natelson, Douglas Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics and Astronomy Engineering Rice University HoustonTX77005 United States Department of Materials Science and Nanoengineering Rice University HoustonTX77005 United States

Tunneling metal-insulator-metal (MIM) junctions can exhibit an open-circuit photovoltage (OCPV) response under illumination that may be useful for photodetection. One mechanism for photovoltage generation is hot carrier tunneling, in which photoexcited carriers generate a net photocurrent that must be balanced by a drift current in the open-circuit configuration. We present experiments in electromigrated planar MIM structures, designed with asymmetric plasmonic properties using Au and Pt electrodes. Decay of optically excited local plasmonic modes preferentially creates hot carriers on the Au side of the junction, leading to a clear preferred directionality of the hot electron photocurrent and hence a preferred polarity of the resulting OCPV. In contrast, in an ensemble of symmetric devices constructed from only one Au, polarity of the OCPV has no preferred direction. Copyright © 2023, The Authors. All rights reserved.

关键词： Plasmonics

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Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon Coupling in Hybrid Organic-Inorganic Perovskites

Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Dasom Kim Jin Hou Geon Lee Ayush Agrawal Sunghwan Kim Hao Zhang Di Bao Andrey Baydin Wenjing WU Fuyang Tay Shengxi Huang Daivd Hagenmüller Elbert E. M. Chia Dai-Sik Kim Minah Seo Aditya D. Mohite Junichiro Kono Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Department of Electrical and Computer Engineering Rice University Houston TX USA Department of Materials Science and NanoEngineering Rice University Houston TX USA Sensor System Research Center Korea Institute of Science and Technology Seoul Republic of Korea Department of Physics and Astronomy Seoul National University Seoul Republic of Korea Department of Chemical and Biomolecular Engineering Rice University Houston TX USA Department of Physics Long-wavelength Nanotechnology Laboratory Quantum Photonics Institute Ulsan National Institute of Science and Technology (UNIST) Ulsan Republic of Korea Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore Smalley-Curl institute Rice University Houston TX USA CESQ-ISIS (UMR 7006) Universite de Strasbourg and CNRS Strasbourg France KU-KIST Graduate School of Converging Science and Technology Korea University Seoul Republic of Korea Department of Physics and Astronomy Rice University Houston TX USA

Methods for harnessing vibrational states are desired for phonon-based technologies. We realized ultrastrong coupling of two phonon modes in perovskite materials induced by ultrastrong coupling with a common photonic ... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Couplings Phonons Laser modes Perovskites Electro-optical waveguides Photonics

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Measurement of Water Diffusion Coefficient in Cross-Linked Gelatins Using the Terahertz Time-Domain Spectroscopy Technique

Measurement of Water Diffusion Coefficient in Cross-Linked G...

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Conference on Lasers and Electro-Optics (CLEO)

作者： D. Walden D. Chakraborty J. Cheng A. Straksys A. Stirke W. CMA Melo A. Jukna I. Komissarov Roman Sobolewski Department of Physics and Astronomy University of Rochester Rochester New York USA Laboratory for Laser Energetics University of Rochester Rochester New York USA Materials Science Graduate Program University of Rochester Rochester New York USA State Research Institute Center for Physical Sciences & Technology Vilnius Lithuania Department of Physics Vilnius Gediminas Technical University Vilnius Lithuania Department of Electrical and Computer Engineering University of Rochester Rochester New York USA

A novel method has been developed to evaluate the water diffusion dynamics in gelatin-based gels, based on transient terahertz time-domain spectroscopy measurements. Such gels are widely used models for human skin.

ISBN: (纸本)9798350369311

关键词： Spectroscopy Measurement by laser beam Lasers and electrooptics Laser modes Skin Time-domain analysis Transient analysis Electro-optical waveguides

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Quality and complexity assessment of learning-based image compression solutions

arXiv

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arXiv 2021年

作者： Dick, João Abreu, Brunno Grellert, Mateus Bampi, Sergio Informatics Institute PGMICRO Federal University of Rio Grande do Sul Porto Alegre Brazil Graduate Program in Computer Science Federal University of Santa Catarina Florianópolis Brazil

This work presents an analysis of state-of-the-art learning-based image compression techniques. We compare 8 models available in the Tensorflow Compression package in terms of visual quality metrics and processing time, using the KODAK data set. The results are compared with the Better Portable Graphics (BPG) and the JPEG2000 codecs. Results show that JPEG2000 has the lowest execution times compared with the fastest learning-based model, with a speedup of 1.46× in compression and 30× in decompression. However, the learning-based models achieved improvements over JPEG2000 in terms of quality, specially for lower bitrates. Our findings also show that BPG is more efficient in terms of PSNR, but the learning models are better for other quality metrics, and sometimes even faster. The results indicate that learning-based techniques are promising solutions towards a future mainstream compression method. © 2021, CC BY-NC-SA.

关键词： Image compression

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Evaluation of sar to optical image translation using conditional generative adversarial network for cloud removal in a crop dataset 24

Evaluation of sar to optical image translation using conditi...

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2021 24th ISPRS Congress Commission III: Imaging Today, Foreseeing Tomorrow

作者： Christovam, L.E. Shimabukuro, M.H. Galo, M.L.B.T. Honkavaara, E. Graduate Program in Cartographic Sciences São Paulo State University Brazil Dept. of Mathematics and Computer Science São Paulo State University Brazil Dept. of Cartography São Paulo State University Brazil Dept. of Remote Sensing and Photogrammetry Finnish Geospatial Research Institute in National Land Survey of Finland Finland

Most methods developed to map crop fields with high-quality are based on optical image time-series. However, often accuracy of these approaches is deteriorated due to clouds and cloud shadows, which can decrease the availably of optical data required to represent crop phenological stages. In this sense, the objective of this study was to implement and evaluate the conditional Generative Adversarial Network (cGAN) that has been indicated as a potential tool to address the cloud and cloud shadow removal;we also compared it with the Witthaker Smother (WS), which is a well-known data cleaning algorithm. The dataset used to train and assess the methods was the Luis Eduardo Magalhães benchmark for tropical agricultural remote sensing applications. We selected one MSI/Sentinel-2 and C-SAR/Sentinel-1 image pair taken in days as close as possible. A total of 5000 image pair patches were generated to train the cGAN model, which was used to derive synthetic optical pixels for a testing area. Visual analysis, spectral behaviour comparison, and classification were used to evaluate and compare the pixels generated with the cGAN and WS against the pixel values from the real image. The cGAN provided consistent pixel values for most crop types in comparison to the real pixel values and outperformed the WS significantly. The results indicated that the cGAN has potential to fill cloud and cloud shadow gaps in optical image time-series. © 2021 International Archives of the Photogrammetry, Remote Sensing and Spatial Information sciences - ISPRS Archives. All rights reserved.

关键词： Crops

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Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records

arXiv

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arXiv 2023年

作者： Sivarajkumar, Sonish Huang, Yufei Wang, Yanshan Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA United States Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Department of Health Information Management University of Pittsburgh PittsburghPA United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States

Objective: To pre-train fair and unbiased patient representations from Electronic Health Records (EHRs) using a novel weighted loss function that reduces bias and improves fairness in deep representation learning models. Methods: We defined a new loss function, called weighted loss function, in the deep representation learning model to balance the importance of different groups of patients and features. We applied the proposed model, called Fair Patient Model (FPM), to a sample of 34,739 patients from the MIMIC-III dataset and learned patient representations for four clinical outcome prediction tasks. Results: FPM outperformed the baseline models in terms of three fairness metrics: demographic parity, equality of opportunity difference, and equalized odds ratio. FPM also achieved comparable predictive performance with the baselines, with an average accuracy of 0.7912. Feature analysis revealed that FPM captured more information from clinical features than the baselines. Conclusion: FPM is a novel method to pre-train fair and unbiased patient representations from EHR data using a weighted loss function. The learned representations can be used for various downstream tasks in healthcare and can be extended to other domains where bias and fairness are important. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Scaling Limitations of Line TFETs at Sub-8-nm Technology Node

Scaling Limitations of Line TFETs at Sub-8-nm Technology Nod...

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2020 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2020

作者： Thoti, Narasimhulu Li, Yiming Kola, Sekhar Reddy Parallel and Scientific Computing Laboratory Eecs International Graduate Program Institute of Communications Engineering Department of Electrical and Computer Engineering Center for MmWave Smart Radar System and Technologies National Chiao Tung University 1001 Ta-Hsueh Rd Hsinchu300 Taiwan

ISBN: (纸本)9781728142326

The scope of the work is to investigate limitations in device scaling by identifying various parameters of short channel effects (SCEs) in current challenging geometries of the line tunnel field effect transistors (TFETs). Key factors of the considered device, such as the doping and the thickness (tn) of n-epitaxial region, and source-to-drain length (LSDeff) scaling cannot be tuned anymore to boost the device characteristics for the sub-8-nm technology node. The main results of this study indicates that the engineering acceptable performance are achieved at a low doping of 5 1018 cm-3, an optimal tn as low (about 0.5 nm), and a LSDeff greater than 12.5 nm. Hence, the line TFETs below sub-8-nm faces serious bottleneck of scaling and cannot be further scaled with the conventional scaling rule at all. © 2020 IEEE.

关键词： Tunnel field effect transistors

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Subspace variational quantum simulator

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Physical Review Research 2023年第2期5卷 023078-023078页

作者： Kentaro Heya Ken M. Nakanishi Kosuke Mitarai Zhiguang Yan Kun Zuo Yasunari Suzuki Takanori Sugiyama Shuhei Tamate Yutaka Tabuchi Keisuke Fujii Yasunobu Nakamura RIKEN Center for Quantum Computing (RQC) Wako Saitama 351-0198 Japan Institute for Physics of Intelligence The University of Tokyo Tokyo 113-0033 Japan Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 Japan Center for Quantum Information and Quantum Biology Osaka University Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan NTT Computer and Data Science Laboratories NTT Corporation Musashino 180-8585 Japan Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Meguro-ku Tokyo 153-8904 Japan Department of Applied Physics Graduate School of Engineering The University of Tokyo Bunkyo-ku Tokyo 113-8656 Japan

Quantum simulation is one of the key applications of quantum computing, which accelerates research and development in the fields such as chemistry and material science. The recent development of noisy intermediate-scale quantum (NISQ) devices urges the exploration of applications without the necessity of quantum error correction. In this paper, we propose an efficient method to simulate quantum dynamics driven by a static Hamiltonian on NISQ devices, named subspace variational quantum simulator (SVQS). SVQS employs the subspace-search variational quantum eigensolver (SSVQE) [Phys. Rev. Res. 1, 033062 (2019)] to find a low-lying eigensubspace and extends it to simulate dynamics within the subspace with lower overhead compared to the existing schemes. We experimentally simulate the time-evolution operator in a low-lying eigensubspace of a hydrogen molecule. We also define the subspace process fidelity as a measure between two quantum processes in a subspace. The subspace time evolution mimicked by SVQS shows the subspace process fidelity of 0.896–0.989.

关键词： Quantum algorithms Quantum algorithms for chemical calculations Quantum computation Quantum control Quantum gates Quantum simulation Quantum tomography

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EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) 2.0: A MANIFESTO OF OPEN CHALLENGES AND INTERDISCIPLINARY RESEARCH DIRECTIONS

arXiv

引用

arXiv 2023年

作者： Longo, Luca Brcic, Mario Cabitza, Federico Choi, Jaesik Confalonieri, Roberto Ser, Javier Del Guidotti, Riccardo Hayashi, Yoichi Herrera, Francisco Holzinger, Andreas Jiang, Richard Khosravi, Hassan Lecue, Freddy Malgieri, Gianclaudio Páez, Andrés Samek, Wojciech Schneider, Johannes Speith, Timo Stumpf, Simone The Artificial Intelligence and Cognitive Load Research Lab Technological University Dublin Ireland University of Zagreb Faculty of Electrical Engineering and Computing Croatia University of Milano-Bicocca Milan Italy IRCCS Ospedale Galeazzi Sant’Ambrogio Milan Italy Kim Jaechul Graduate School of AI Korea Advanced Institute of Science & Technology Korea Republic of INEEJI Corporation Korea Republic of Department of Mathematics University of Padua Italy Derio Spain Bilbao Spain University of Pisa Pisa Italy Department of Computer Science Meiji University Tokyo Japan Department of Computer Science and Artificial Intelligence DaSCI Andalusian Institute in Data Science & Computational Intelligence University of Granada Granada Spain Human-Centered AI Lab University of Natural Resources and Life Sciences Vienna Austria School of Computing and Communications Lancaster University United Kingdom The University of Queensland Brisbane Australia Sophia Antipolis France eLaw Center for Law and Digital Technologies Leiden University Netherlands Department of Philosophy Universidad de los Andes Bogotá Colombia Center for Research & Formation in Artificial Intelligence Universidad de los Andes Bogotá Colombia Technical University of Berlin Berlin Germany Fraunhofer Heinrich Hertz Institute Berlin Germany Berlin Germany Department of Information Systems and Computer Science University of Liechtenstein Liechtenstein Liechtenstein Department of Philosophy University of Bayreuth Bayreuth Germany Center for Perspicuous Computing Saarland University Saarbrücken Germany School of Computing Science University of Glasgow United Kingdom

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders. © 2023, CC BY-SA.

关键词： Ethical technology

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