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A benchmark of selected algorithmic differentiation tools on some problems in computer vision and machine learning

Filip Srajer, Zuzana Kukelova, Andrew Fitzgibbon
2018 Optimization Methods and Software  
However, it is important for the success of algorithmic differentiation that such 'simple' objective functions are handled efficiently, as so many problems in computer vision and machine learning are of  ...  We look at three objective functions from computer vision and machine learning.  ...  We thank Jonathan Taylor for an example implementation of a hand tracking function in Python. Funding Zuzana Kukelova was supported by The Czech Science Foundation Project GACR P103/12/G084.  ... 
doi:10.1080/10556788.2018.1435651 fatcat:yd57h6bfhfds5krnrazqsfyiq4

Democratisation of Usable Machine Learning in Computer Vision [article]

Raymond Bond, Ansgar Koene, Alan Dix, Jennifer Boger, Maurice D. Mulvenna, Mykola Galushka, Bethany Waterhouse Bradley, Fiona Browne, Hui Wang, Alexander Wong
2019 arXiv   pre-print
This has resulted in the emergence of the 'data scientist' who is conversant in statistical thinking, machine learning (ML), computer vision, and computer programming.  ...  In this paper, we undertake a SWOT analysis to study the strengths, weaknesses, opportunities, and threats of building usable ML tools for mass adoption for important areas leveraging ML such as computer  ...  or certification for novice users of usable ML tools with the intention of mitigating irresponsible deployment of ML algorithms in computer vision.  ... 
arXiv:1902.06804v1 fatcat:jalnwcgyuvg6pek2u4fmf2tape

OpenML Benchmarking Suites [article]

Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Frank Hutter, Michel Lang, Rafael G. Mantovani, Jan N. van Rijn, Joaquin Vanschoren
2019 arXiv   pre-print
Machine learning research depends on objectively interpretable, comparable, and reproducible algorithm benchmarks.  ...  Therefore, we advocate the use of curated, comprehensive suites of machine learning tasks to standardize the setup, execution, and reporting of benchmarks.  ...  A Brief History of Benchmarking Suites Proper algorithm benchmarking is a hallmark of machine learning research.  ... 
arXiv:1708.03731v2 fatcat:rw5ex3yq3rfd5csvzhp5cnv7z4

Application of Machine Learning in Computer Vision: A Brief Review

Mr. Shismohammad Mulla
2020 International Journal for Research in Applied Science and Engineering Technology  
A scientific study on the importance of machine learning and its applications in the field of computer vision is carried out in this paper.  ...  Algorithms based on machine learning models are excellent at recognizing patterns but typically requires an enormous amount of data sets and lots of computational power.  ...  Agriculture Advancement in computer vision algorithms makes the prediction and detection of plant disease through some machine learning algorithm is possible.  ... 
doi:10.22214/ijraset.2020.30261 fatcat:2qy6ufryyreefnluui73nk72yq

Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop 12371)

Anthony D. Joseph, Pavel Laskov, Fabio Roli, J. Doug Tygar, Blaine Nelson, Marc Herbstritt
2013 Dagstuhl Reports  
Finally, benchmarks for and quantitative assessments of the security of learning algorithms were also deemed to be currently inadequate.  ...  Arising organically from a variety of independent research projects in both computer security and machine learning, the topic of secure learning is emerging as a major direction of research that offers  ...  Computer Vision Systems: Present and Potential Attacks The application of machine learning to computer vision has a long history because some components of computer vision systems are difficult to manually  ... 
doi:10.4230/dagrep.2.9.109 dblp:journals/dagstuhl-reports/JosephLRTN12 fatcat:4x3ng2szxfg5jnkf5rtwsmttrm

A Benchmarking of Learning Strategies for Pest Detection and Identification on Tomato Plants for Autonomous Scouting Robots Using Internal Databases

Aitor Gutierrez, Ander Ansuategi, Loreto Susperregi, Carlos Tubío, Ivan Rankić, Libor Lenža
2019 Journal of Sensors  
A solution that combines computer vision and machine learning is compared against a deep learning solution.  ...  Computer vision and recent advances in deep learning can play an important role in increasing the reliability and productivity.  ...  Acknowledgments The GreenPatrol project has received funding from the European GNSS Agency under the European Union's (EU) Horizon 2020 research and innovation programme under grant agreement no. 776324  ... 
doi:10.1155/2019/5219471 fatcat:z3tcfmoprvgcpmcnwd64xows5a

Towards Generating Consumer Labels for Machine Learning Models

Christin Seifert, Stefanie Scherzinger, Lena Wiese
2019 2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)  
We further discuss the feasibility of operationalizing and benchmarking these requirements in the automated generation of ML consumer labels.  ...  In this vision paper, we propose to issue consumer labels for trained and published ML models.  ...  In terms of capturing computational efficiency on a consumer label, we may turn to complexity theory (the big-O notation), or benchmark runtimes (for example, in milliseconds), main memory usage, and storage  ... 
doi:10.1109/cogmi48466.2019.00033 dblp:conf/cogmi/SeifertSW19 fatcat:lochyypdafhyflvcdckzfmxdzm

A Reinforcement Learning Environment For Job-Shop Scheduling [article]

Pierre Tassel, Martin Gebser, Konstantin Schekotihin
2021 arXiv   pre-print
Scheduling is a fundamental task occurring in various automated systems applications, e.g., optimal schedules for machines on a job shop allow for a reduction of production costs and waste.  ...  Recent advances of Deep Reinforcement Learning (DRL) in learning complex behavior enable new COP application possibilities.  ...  Another problem is that some choices done in the past can have a huge impact on the future outlook, e.g., force us to create a lot of holes.  ... 
arXiv:2104.03760v1 fatcat:3mltkox7bbhb3i3bll6434ni7i

Performance Analysis of Deep Neural Networks Using Computer Vision

Nidhi Sindhwani, Rohit Anand, Meivel S., Rati Shukla, Mahendra Yadav, Vikash Yadav
2021 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems  
INTRODUCTION: In recent years, deep learning techniques have been made to outperform the earlier state-of-the-art machine learning techniques in many areas, with one of the most notable cases being computer  ...  A brief account is given of their history, structure, benefits, and drawbacks, followed by a description of their applications in the different tasks of computer vision, such as object detection, face  ...  Some standard conventional tools are introduced to find the behaviour of machine learning models on the cell phones in view of memory and hardware usage and power injected.  ... 
doi:10.4108/eai.13-10-2021.171318 fatcat:e4wrsi6gm5c5jm6fzp3wtnsfjq

A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection [article]

Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, Bingsheng He
2021 arXiv   pre-print
In this survey, we conduct a comprehensive review on federated learning systems.  ...  Federated learning has been a hot research topic in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions.  ...  Acknowledgement This work is supported by a MoE AcRF Tier 1 grant (T1 251RES1824), an SenseTime Young Scholars Research Fund, and a MOE Tier 2 grant (MOE2017-T2-1-122) in Singapore.  ... 
arXiv:1907.09693v6 fatcat:d3l2l664mjdfrjgyok43pfxnvq

Julia Language in Machine Learning: Algorithms, Applications, and Open Issues [article]

Kaifeng Gao, Jingzhi Tu, Zenan Huo, Gang Mei, Francesco Piccialli, Salvatore Cuomo
2020 arXiv   pre-print
Machine learning is driving development across many fields in science and engineering. A simple and efficient programming language could accelerate applications of machine learning in various fields.  ...  This paper summarizes the related research work and developments in the application of the Julia language in machine learning.  ...  Computer Vision Computer vision is a simulation of biological vision using computers and related equipment.  ... 
arXiv:2003.10146v1 fatcat:f2ocidpu4rchnokkc46qzrjgyu

Synthetic Data and Simulators for Recommendation Systems: Current State and Future Directions [article]

Adam Lesnikowski, Gabriel de Souza Pereira Moreira, Sara Rabhi, Karl Byleen-Higley
2021 arXiv   pre-print
These approaches have already had a beneficial impact in other machine-learning driven fields.  ...  We identify and discuss a key trade-off between data fidelity and privacy in the past work on synthetic data and simulators for recommendation systems.  ...  INTRODUCTION Synthetic data generation and simulation techniques have been popular and successful in machine learning areas such as computer vision [25] [17] and robotics [24] [14], but have not  ... 
arXiv:2112.11022v1 fatcat:z6yevo45lnb4bm534l2vtqxdpm

A Survey on Deep Learning Methods for Robot Vision [article]

Javier Ruiz-del-Solar, Patricio Loncomilla, Naiomi Soto
2018 arXiv   pre-print
To achieve this, a comprehensive overview of deep learning and its usage in computer vision is given, that includes a description of the most frequently used neural models and their main application areas  ...  The application of this new paradigm has been particularly successful in computer vision, in which the development of deep learning methods for vision applications has become a hot research topic.  ...  SegNet Table 2 . 2 Performance of the Best Performing DNNs in some Selected Computer Vision Benchmarks.  ... 
arXiv:1803.10862v1 fatcat:bkxbwfkuxbfkrck4aafdgt3moy

Taking Human out of Learning Applications: A Survey on Automated Machine Learning [article]

Quanming Yao, Mengshuo Wang, Yuqiang Chen, Wenyuan Dai, Yu-Feng Li, Wei-Wei Tu, Qiang Yang, Yang Yu
2019 arXiv   pre-print
In this paper, we provide an up to date survey on AutoML. First, we introduce and define the AutoML problem, with inspiration from both realms of automation and machine learning.  ...  In order to make machine learning techniques easier to apply and reduce the demand for experienced human experts, automated machine learning (AutoML) has emerged as a hot topic with both industrial and  ...  Examples of learning processes are feature engineering, model and/or algorithm selection, and neural architecture design. • A learning tool is a method which can solve some problems appear in machine learning  ... 
arXiv:1810.13306v4 fatcat:crjmb62udjajfjkkkzqjvkyfwq

Julia language in machine learning: Algorithms, applications, and open issues

Kaifeng Gao, Gang Mei, Francesco Piccialli, Salvatore Cuomo, Jingzhi Tu, Zenan Huo
2020 Computer Science Review  
Machine learning is driving development across many fields in science and engineering. A simple and efficient programming language could accelerate applications of machine learning in various fields.  ...  This paper summarizes the related research work and developments in the applications of the Julia language in machine learning.  ...  Acknowledgments This research was jointly supported by the National Natural Science Foundation of China (11602235), the Fundamental Research Funds for China Central Universities (2652018091), and the Major  ... 
doi:10.1016/j.cosrev.2020.100254 fatcat:gdt66djfvjfqpjou3lvemxsxfy
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