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Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions [article]

Nicolas Vasilache, Oleksandr Zinenko, Theodoros Theodoridis, Priya Goyal, Zachary DeVito, William S. Moses, Sven Verdoolaege, Andrew Adams, Albert Cohen
<span title="2018-06-29">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Our contributions include (1) a language close to the mathematics of deep learning called Tensor Comprehensions, (2) a polyhedral Just-In-Time compiler to convert a mathematical description of a deep learning  ...  [Abstract cutoff]  ...  comprehensions go through a framework-agnostic API for loading and running code; to exchange tensor data, the API uses DLTensor, a common interchange format for in-memory tensor data schedule tree presented  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1802.04730v3">arXiv:1802.04730v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2ef5ete4mvao5bz43h7z7dtlwi">fatcat:2ef5ete4mvao5bz43h7z7dtlwi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191013031505/https://arxiv.org/pdf/1802.04730v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ca/e9/cae9d90524cccac5081666985d5d055b71697cee.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1802.04730v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Compiler Support for Sparse Tensor Computations in MLIR [article]

Aart J.C. Bik, Penporn Koanantakool, Tatiana Shpeisman, Nicolas Vasilache, Bixia Zheng, Fredrik Kjolstad
<span title="2022-02-09">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Sparse tensors arise in problems in science, engineering, machine learning, and data analytics.  ...  Therefore, we propose treating sparsity as a property of tensors, not a tedious implementation task, and letting a sparse compiler generate sparse code automatically from a sparsity-agnostic definition  ...  Sparse tensors arise in a wide range of problems in science, engineering, machine learning, and data analytics.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.04305v1">arXiv:2202.04305v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6qijxmernbev7pwmt6lgqdvziy">fatcat:6qijxmernbev7pwmt6lgqdvziy</a> </span>
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Tensor Relational Algebra for Machine Learning System Design [article]

Binhang Yuan and Dimitrije Jankov and Jia Zou and Yuxin Tang and Daniel Bourgeois and Chris Jermaine
<span title="2021-08-09">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We consider the question: what is the abstraction that should be implemented by the computational engine of a machine learning system?  ...  Current machine learning systems typically push whole tensors through a series of compute kernels such as matrix multiplications or activation functions, where each kernel runs on an AI accelerator (ASIC  ...  EVALUATION The goal of our paper is to design a computational abstraction that could be exported by the back-end of a machine learning system.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2009.00524v3">arXiv:2009.00524v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sf5qh72wtbco3jhlmxjbhz4kxe">fatcat:sf5qh72wtbco3jhlmxjbhz4kxe</a> </span>
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Parallel programming models for heterogeneous many-cores: a comprehensive survey

Jianbin Fang, Chun Huang, Tao Tang, Zheng Wang
<span title="2020-07-31">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/oyzrfqv3i5ghbipholgpvqu37y" style="color: black;">CCF Transactions on High Performance Computing</a> </i> &nbsp;
While heterogeneous many-core design offers the potential for energy-efficient high-performance, such potential can only be unlocked if the application programs are suitably parallel and can be made to  ...  In this article, we provide a comprehensive survey for parallel programming models for heterogeneous many-core architectures and review the compiling techniques of improving programmability and portability  ...  As a recent example, Google's Tensor Processing Units (TPUs) are application-specific integrated circuits (ASICs) to accelerate machine learning workloads (Patterson 2018) .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s42514-020-00039-4">doi:10.1007/s42514-020-00039-4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nn56xhjm6rcu7kya6gfnyjg66q">fatcat:nn56xhjm6rcu7kya6gfnyjg66q</a> </span>
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A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts [article]

Gesina Schwalbe, Bettina Finzel
<span title="2022-05-17">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Trust, Joint Performance Figure 1 A framework for comprehensible artificial intelligence (Bruckert et al, 2020) .  ...  The first one is to create machine learning techniques that produce models that can be explained (their decision-making process as well as the output), while maintaining a high level of learning performance  ...  This showed an increasing breadth of application fields and method types investigated in order to provide explainable yet accurate learned models.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2105.07190v3">arXiv:2105.07190v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zy7vl6o4gzcbrpqkrxeyazyeuq">fatcat:zy7vl6o4gzcbrpqkrxeyazyeuq</a> </span>
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Reinforcement Learning in Factored Action Spaces using Tensor Decompositions [article]

Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk, Animesh Garg, Jean Kossaifi, Shimon Whiteson, Yuke Zhu, Animashree Anandkumar
<span title="2021-10-27">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We present an extended abstract for the previously published work TESSERACT [Mahajan et al., 2021], which proposes a novel solution for Reinforcement Learning (RL) in large, factored action spaces using  ...  The goal of this abstract is twofold: (1) To garner greater interest amongst the tensor research community for creating methods and analysis for approximate RL, (2) To elucidate the generalised setting  ...  Tensor methods have been used in machine learning, in the context of learning latent variable models Anandkumar et al. [2014] , signal processing Sidiropoulos et al. [2017] , deep learning and computer  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.14538v1">arXiv:2110.14538v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lrtmuzgaqna4phf5qf7h4uv3rq">fatcat:lrtmuzgaqna4phf5qf7h4uv3rq</a> </span>
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Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI [article]

Jiangchao Yao, Shengyu Zhang, Yang Yao, Feng Wang, Jianxin Ma, Jianwei Zhang, Yunfei Chu, Luo Ji, Kunyang Jia, Tao Shen, Anpeng Wu, Fengda Zhang (+6 others)
<span title="2022-05-23">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We also discuss potentials and practical experiences of some on-going advanced edge AI topics including pretraining models, graph neural networks and reinforcement learning.  ...  Specifically, we are the first to set up the collaborative learning mechanism for cloud and edge modeling with a thorough review of the architectures that enable such mechanism.  ...  In terms of machine learning framework it supports TensorFlow, Caffe, MXNet and PyTorch etc.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2111.06061v3">arXiv:2111.06061v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5rq6s5s4cvcidblidgahwynp34">fatcat:5rq6s5s4cvcidblidgahwynp34</a> </span>
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LibFewShot: A Comprehensive Library for Few-shot Learning [article]

Wenbin Li, Chuanqi Dong, Pinzhuo Tian, Tiexin Qin, Xuesong Yang, Ziyi Wang, Jing Huo, Yinghuan Shi, Lei Wang, Yang Gao, Jiebo Luo
<span title="2021-11-08">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To address these situations, we propose a comprehensive library for few-shot learning (LibFewShot) by re-implementing seventeen state-of-the-art few-shot learning methods in a unified framework with the  ...  a few-shot learning method.  ...  Conclusions In this paper, we present a comprehensive library for few-shot learning (LibFewShot) by re-implementing the state-of-the-art FSL methods in a unified framework to facilitate healthy research  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.04898v2">arXiv:2109.04898v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/l3dldznugbcyfpxogpq63khqra">fatcat:l3dldznugbcyfpxogpq63khqra</a> </span>
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Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation

Hojjat Navidan, Parisa Fard Moshiri, Mohammad Nabati, Reza Shahbazian, Seyed Ali Ghorashi, Vahid Shah-Mansouri, David Windridge
<span title="">2021</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/blfmvfslmbggxhopuigjdb3jma" style="color: black;">Computer Networks</a> </i> &nbsp;
In doing so, we shall provide a novel evaluation framework for comparing the performance of different models in non-image applications, applying this to a number of reference network datasets.  ...  The need for a comprehensive survey of such activity is therefore urgent.  ...  Machine learning and NNs have been widely used to learn probability features from these signal properties to perform localization.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.comnet.2021.108149">doi:10.1016/j.comnet.2021.108149</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4ekgil24ijha3evmzruez63tdq">fatcat:4ekgil24ijha3evmzruez63tdq</a> </span>
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A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing [article]

Ping Lang, Xiongjun Fu, Marco Martorella, Jian Dong, Rui Qin, Xianpeng Meng, Min Xie
<span title="2020-09-29">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems.  ...  Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments.  ...  Traditional Machine Learning Models 1) Support Vector Machines (SVMs).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2009.13702v1">arXiv:2009.13702v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/m6am73324zdwba736sn3vmph3i">fatcat:m6am73324zdwba736sn3vmph3i</a> </span>
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What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review [article]

Xiaoqi Li, Haoyuan Chen, Chen Li, Md Mamunur Rahaman, Xintong Li, Jian Wu, Xiaoyan Li, Hongzan Sun, Marcin Grzegorzek
<span title="2022-05-08">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In the past ten years, the computing power of machine vision (MV) has been continuously improved, and image analysis algorithms have developed rapidly.  ...  In particular, the continuous improvement of deep learning algorithms has further improved the accuracy of MV in disease detection and diagnosis.  ...  Traditional Machine Learning based Classification Methods In [101] , SVM is applied to discriminate high and low lymphocytic infiltration samples.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2201.08550v2">arXiv:2201.08550v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tzc4oiurzngkrlke4w4zt4f26u">fatcat:tzc4oiurzngkrlke4w4zt4f26u</a> </span>
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Exploring deep learning-based architecture, strategies, applications and current trends in generic object detection: A comprehensive review

Lubna Aziz, Sah bin Haji Salam, Sara Ayub
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
In early 2000, the popularity of deep learning began to decline due to a lack of big data, high computational power requirements, and performance insignificance as compared to other machine learning tools  ...  systems to learn abstract, complex, and subtle representations.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3021508">doi:10.1109/access.2020.3021508</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/guri46oiejhfzeitxuuprpmjka">fatcat:guri46oiejhfzeitxuuprpmjka</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200909055220/https://ieeexplore.ieee.org/ielx7/6287639/6514899/09186021.pdf?tp=&amp;arnumber=9186021&amp;isnumber=6514899&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/24/27/2427febf42d89415048dd23fc5dcaaec97d2c058.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3021508"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

DeepReduce: A Sparse-tensor Communication Framework for Distributed Deep Learning [article]

Kelly Kostopoulou, Hang Xu, Aritra Dutta, Xin Li, Alexandros Ntoulas, Panos Kalnis
<span title="2021-02-05">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper introduces DeepReduce, a versatile framework for the compressed communication of sparse tensors, tailored for distributed deep learning.  ...  Existing communication primitives are agnostic to the peculiarities of deep learning; consequently, they impose unnecessary communication overhead.  ...  DeepReduce resides between the machine learning framework (e.g., Tensorow, PyTorch) and the communication library.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2102.03112v1">arXiv:2102.03112v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3c6m6t5vpjbqvncqaxpdbi35qy">fatcat:3c6m6t5vpjbqvncqaxpdbi35qy</a> </span>
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Flashlight: Enabling Innovation in Tools for Machine Learning [article]

Jacob Kahn, Vineel Pratap, Tatiana Likhomanenko, Qiantong Xu, Awni Hannun, Jeff Cai, Paden Tomasello, Ann Lee, Edouard Grave, Gilad Avidov, Benoit Steiner, Vitaliy Liptchinsky (+2 others)
<span title="2022-01-29">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
As the computational requirements for machine learning systems and the size and complexity of machine learning frameworks increases, essential framework innovation has become challenging.  ...  learning frameworks.  ...  frameworks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2201.12465v1">arXiv:2201.12465v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/v44hox26rbf3vcxqa6qvv3x37u">fatcat:v44hox26rbf3vcxqa6qvv3x37u</a> </span>
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A Comprehensive Survey on Electronic Design Automation and Graph Neural Networks: Theory and Applications

Daniela Sánchez Lopera, Lorenzo Servadei, Gamze Naz Kiprit, Robert Wille, Wolfgang Ecker
<span title="2022-06-14">2022</span> <i title="Association for Computing Machinery (ACM)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ditvnky3lrdclbnymbnpcslo5m" style="color: black;">ACM Transactions on Design Automation of Electronic Systems</a> </i> &nbsp;
To alleviate these, Machine Learning (ML) has been incorporated into many stages of the design flow, such as in placement and routing.  ...  In this paper, we present a comprehensive review of the existing works linking the EDA flow for chip design and Graph Neural Networks.  ...  Classical Machine Learning Techniques Some lessons learned from non-graph ML models are also valid for GNNs.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3543853">doi:10.1145/3543853</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/halhg7zwgvdpjkzf7g7ctxzuby">fatcat:halhg7zwgvdpjkzf7g7ctxzuby</a> </span>
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