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Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing

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arXiv 2024年

作者： Stewart, Kenneth Neumeier, Michael Shrestha, Sumit Bam Orchard, Garrick Neftci, Emre Department of Computer Science UC Irvine IrvineCA United States Department of Neuromorphic Computing Fortiss-Research Institute of the Free State of Bavaria Munich Germany Intel Labs Intel Corporation Santa ClaraCA United States Peter Grünberg Institute - Neuromorphic Software Ecosystems Forschungszentrum Jülich Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen Germany

Achieving personalized intelligence at the edge with real-time learning capabilities holds enormous promise in enhancing our daily experiences and helping decision making, planning, and sensing. However, efficient and reliable edge learning remains difficult with current technology due to the lack of personalized data, insufficient hardware capabilities, and inherent challenges posed by online learning. Over time and across multiple developmental stages, the brain has evolved to efficiently incorporate new knowledge by gradually building on previous knowledge. In this work, we emulate the multiple stages of learning with digital neuromorphic technology that simulates the neural and synaptic processes of the brain using two stages of learning. First, a meta-training stage trains the hyperparameters of synaptic plasticity for one-shot learning using a differentiable simulation of the neuromorphic hardware. This meta-training process refines a hardware local three-factor synaptic plasticity rule and its associated hyperparameters to align with the trained task domain. In a subsequent deployment stage, these optimized hyperparameters enable fast, data-efficient, and accurate learning of new classes. We demonstrate our approach using event-driven vision sensor data and the Intel Loihi neuromorphic processor with its plasticity dynamics, achieving real-time one-shot learning of new classes that is vastly improved over transfer learning. Our methodology can be deployed with arbitrary plasticity models and can be applied to situations demanding quick learning and adaptation at the edge, such as navigating unfamiliar environments or learning unexpected categories of data through user engagement. © 2024, CC BY.

关键词： Zero-shot learning

Efficient Algorithms for Approximating Quantum Partition Functions at Low Temperature

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arXiv 2022年

作者： Helmuth, Tyler Mann, Ryan L. Department of Mathematical Sciences Durham University DurhamDH1 3LE United Kingdom Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information School of Computer Science Faculty of Engineering & Information Technology University of Technology SydneyNSW2007 Australia School of Mathematics University of Bristol BristolBS8 1UG United Kingdom

We establish an efficient approximation algorithm for the partition functions of a class of quantum spin systems at low temperature, which can be viewed as stable quantum perturbations of classical spin systems. Our algorithm is based on combining the contour representation of quantum spin systems of this type due to Borgs, Kotecký, and Ueltschi with the algorithmic framework developed by Helmuth, Perkins, and Regts, and Borgs et al. © 2022, CC BY.

关键词： Temperature

Matrix Infinitely Divisible Series: Tail Inequalities and Their Applications

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arXiv 2018年

作者： Zhang, Chao Gao, Xianjie Hsieh, Min-Hsiu Hang, Hanyuan Tao, Dacheng School of Mathematical Sciences Dalian University of Technology Liaoning Dalian116024 China Centre for Quantum Software and Information University of Technology Sydney SydneyNSW2007 Australia AI Lab Samsung Research China - Beijing Beijing100028 China UBTECH Sydney Artificial Intelligence Centre The School of Computer Science Faculty of Engineering The University of Sydney 6 Cleveland St DarlingtonNSW2008 Australia

In this paper, we study tail inequalities of the largest eigenvalue of a matrix infinitely divisible (i.d.) series, which is a finite sum of fixed matrices weighted by i.d. random variables. We obtain several types of tail inequalities, including Bennett-type and Bernstein-type inequalities. This allows us to further bound the expectation of the spectral norm of a matrix i.d. series. Moreover, by developing a new lower-bound function for Q(s) = (s + 1) log(s + 1) − s that appears in the Bennett-type inequality, we derive a tighter tail inequality of the largest eigenvalue of the matrix i.d. series than the Bernstein-type inequality when the matrix dimension is high. The resulting lower-bound function is of independent interest and can improve any Bennett-type concentration inequality that involves the function Q(s). The class of i.d. probability distributions is large and includes Gaussian and Poisson distributions, among many others. Therefore, our results encompass the existing work [1] on matrix Gaussian series as a special case. Lastly, we show that the tail inequalities of a matrix i.d. series have applications in several optimization problems including the chance constrained optimization problem and the quadratic optimization problem with orthogonality constraints. In addition, we also use the resulting tail bounds to show that random matrices constructed from i.d. random variables satisfy the restricted isometry property (RIP) when it acts as a measurement matrix in compressed sensing. © 2018, CC BY-NC-ND.

关键词： Compressed sensing

Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration

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arXiv 2022年

作者： Trajanov, Risto Nikolikj, Ana Cenikj, Gjorgjina Teytaud, Fabien Videau, Mathurin Teytaud, Olivier Eftimov, Tome López-Ibáñez, Manuel Doerr, Carola Faculty of Computer Science and Engineering Ss. Cyril and Methodius Skopje Macedonia Jožef Stefan Institute Ljubljana Slovenia Jozef Stefan International Postgraduate School Ljubljana Slovenia Universitś Littoral Côte d'Opale France Facebook AI Research ITIS Software Universidad de Málaga Spain Sorbonne Universitś CNRS LIP6 Paris France

Algorithm selection wizards are effective and versatile tools that automatically select an optimization algorithm given high-level information about the problem and available computational resources, such as number and type of decision variables, maximal number of evaluations, possibility to parallelize evaluations, etc. State-of-the-art algorithm selection wizards are complex and difficult to improve. We propose in this work the use of automated configuration methods for improving their performance by finding better configurations of the algorithms that compose them. In particular, we use elitist iterated racing (irace) to find CMA configurations for specific artificial benchmarks that replace the hand-crafted CMA configurations currently used in the NGOpt wizard provided by the Nevergrad platform. We discuss in detail the setup of irace for the purpose of generating configurations that work well over the diverse set of problem instances within each benchmark. Our approach improves the performance of the NGOpt wizard, even on benchmark suites that were not part of the tuning by irace. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

Multi-error-correcting amplitude damping codes

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Multi-error-correcting amplitude damping codes

IEEE International Symposium on Information Theory

作者： Runyao Duan Markus Grassl Zhengfeng Ji Bei Zeng State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China Centre for Quantum Computation and Intelligent Systems (QCIS) Faculty of Engineering and Information Technology University of Technology Sydney NSW Australia Centre for Quantum Technologies National University of Singapore Singapore Perimeter Institute of Theoretical Physics Waterloo ONT Canada State Key Laboratory of Computer Science Institute of Software Chinese Academy and Sciences Beijing China Institute for Quantum Computing and the Department of Combinatorics and Optimization University of Waterloo Waterloo ONT Canada

ISBN: (纸本)9781424478903

We construct new families of multi-error-correcting quantum codes for the amplitude damping channel. Our key observation is that, with proper encoding, two uses of the amplitude damping channel simulate a quantum erasure channel. This allows us to use concatenated codes with quantum erasure-correcting codes as outer codes for correcting multiple amplitude damping errors. Our new codes are degenerate stabilizer codes and have parameters which are better than the amplitude damping codes obtained by any previously known construction.

关键词： Damping Quantum mechanics Quantum computing Error correction codes Noise level Laboratories Phase noise Physics computing Intelligent systems computer science

On the Parameterised Complexity of Induced Multipartite Graph Parameters

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arXiv 2020年

作者： Mann, Ryan L. Mathieson, Luke Greenhill, Catherine School of Mathematics University of Bristol BristolBS8 1UG United Kingdom Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information Faculty of Engineering & Information Technology University of Technology SydneyNSW2007 Australia School of Computer Science University of Technology SydneyNSW2007 Australia School of Mathematics and Statistics UNSW Sydney NSW2052 Australia

We introduce a family of graph parameters, called induced multipartite graph parameters, and study their computational complexity. First, we consider the following decision problem: an instance is an induced multipartite graph parameter p and a given graph G, and for natural numbers k ≥ 2 and , we must decide whether the maximum value of p over all induced k-partite subgraphs of G is at most . We prove that this problem is W[1]-hard. Next, we consider a variant of this problem, where we must decide whether the given graph G contains a sufficiently large induced k-partite subgraph H such that p(H) ≤ . We show that for certain parameters this problem is para-NP-hard, while for others it is fixed-parameter tractable. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph theory

RETRACTED ‘Application of adaptive neuro-fuzzy technique to determine the most relevant variables for daily soil temperature prediction at different depths’

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CATENA 2017年 149卷 446-446页

作者： Kasra Mohammadi Shahaboddin Shamshirband Amirrudin Kamsin Raja Jamilah Raja Yusof Department of Mechanical and Industrial Engineering University of Massachusetts Amherst MA 01003 USA Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology University Malaya 50603 Kuala Lumpur Malaysia

A comparative analysis on diagnosis of diabetes mellitus using different approaches – A survey

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Informatics in Medicine Unlocked 2020年 21卷

作者： Anwar, Fareeha Qurat-Ul-Ain Ejaz, Muhammad Yasir Mosavi, Amir Department of Computer Science and Software Engineering International Islamic University Islamabad (IIUI) Islamabad 44000 Pakistan School of Economics and Business Norwegian University of Life Sciences Ås 1430 Norway John von Neumann Faculty of Informatics Obuda University Budapest 1034 Hungary Department of Informatics J. Selye University Komarno 94501 Slovakia

Diabetes Mellitus is commonly known as diabetes. It is one of the most chronic diseases as the World Health Organization (WHO) report shows that the number of diabetes patients has risen from 108 million to 422 million in 2014. Early diagnosis of diabetes is important because it can cause different diseases that include kidney failure, stroke, blindness, heart attacks, and lower limb amputation. Different diabetes diagnosis models are found in literature, but there is still a need to perform a survey to analyze which model is best. This paper performs a literature review for diabetes diagnosis approaches using Artificial Intelligence (neural networks, machine learning, deep learning, hybrid methods, and/or stacked-integrated use of different machine learning algorithms). More than thirty-five papers have been shortlisted that focus on diabetes diagnosis approaches. Different datasets are available online for the diagnosis of diabetes. Pima Indian Diabetes Dataset (PIDD) is the most commonly used for diabetes prediction. In contrast with other datasets, it has key factors which play an important role in diabetes diagnosis. This survey also throws light on the weaknesses of the existing approaches that make them less appropriate for a diabetes diagnosis. In artificial intelligence techniques, deep learning is widespread and in medical research, heart rate is getting more attention. Deep learning combined with other algorithms can give better results in diabetes diagnosis and heart rate should be used for other cardiac disease diagnoses. © 2020 The Authors

关键词： Accuracy Deep learning Diabetes mellitus Machine learning

A Brief Review of Explainable Artificial Intelligence in Healthcare

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SSRN

SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, The Authors. All rights reserved.

关键词： Decision making