Machine Learning
Data-driven approaches to design intelligent algorithms.
MERL has a long history of research activity in machine learning, including the development of various boosting algorithms and contributing to the theory and practice of highly scalable collaborative filtering. Our recent work has focused on deep learning and reinforcement learning, with application to a wide range of applications including automotive, robotics, factory automation, transportation, as well as building and home systems.
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Researchers
Takaaki
Hori
Michael
Jones
Tim
Marks
Jonathan
Le Roux
Anoop
Cherian
Philip
Orlik
Devesh
Jha
Yebin
Wang
Chiori
Hori
Alan
Sullivan
Daniel
Nikovski
Diego
Romeres
Ye
Wang
Gordon
Wichern
Karl
Berntorp
Toshiaki
Koike-Akino
Jeroen
van Baar
Ankush
Chakrabarty
Stefano
Di Cairano
Arvind
Raghunathan
Mouhacine
Benosman
Uroš
Kalabić
Hassan
Mansour
Niko
Moritz
Rien
Quirynen
Hongbo
Sun
Bingnan
Wang
William
Yerazunis
Petros
Boufounos
Matthew
Brand
Siheng
Chen
Radu
Corcodel
Claus
Danielson
Bret
Harsham
Kyeong Jin
(K.J.)
Kim
Chungwei
Lin
Dehong
Liu
Pu
(Perry)
Wang
Jinyun
Zhang
Jianlin
Guo
Hiroyuki
Hashimoto
Keisuke
Kojima
Emil
Laftchiev
Suhas
Lohit
Rui
Ma
Kieran
Parsons
Koon Hoo
Teo
Jing
Zhang
Siddarth
Jain
Takashi
Morimoto
Kuan-Chuan
Peng
Kazuto
Sugimoto
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Awards
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AWARD Best Paper Award at the IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) 2019 Date: December 18, 2019
Awarded to: Xuankai Chang, Wangyou Zhang, Yanmin Qian, Jonathan Le Roux, Shinji Watanabe
MERL Contact: Jonathan Le Roux
Research Areas: Artificial Intelligence, Machine Learning, Speech & AudioBrief- MERL researcher Jonathan Le Roux and co-authors Xuankai Chang, Shinji Watanabe (Johns Hopkins University), Wangyou Zhang, and Yanmin Qian (Shanghai Jiao Tong University) won the Best Paper Award at the 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2019), for the paper "MIMO-Speech: End-to-End Multi-Channel Multi-Speaker Speech Recognition". MIMO-Speech is a fully neural end-to-end framework that can transcribe the text of multiple speakers speaking simultaneously from multi-channel input. The system is comprised of a monaural masking network, a multi-source neural beamformer, and a multi-output speech recognition model, which are jointly optimized only via an automatic speech recognition (ASR) criterion. The award was received by lead author Xuankai Chang during the conference, which was held in Sentosa, Singapore from December 14-18, 2019.
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AWARD MERL Researchers win Best Paper Award at ICCV 2019 Workshop on Statistical Deep Learning in Computer Vision Date: October 27, 2019
Awarded to: Abhinav Kumar, Tim K. Marks, Wenxuan Mou, Chen Feng, Xiaoming Liu
MERL Contact: Tim Marks
Research Areas: Artificial Intelligence, Computer Vision, Machine LearningBrief- MERL researcher Tim Marks, former MERL interns Abhinav Kumar and Wenxuan Mou, and MERL consultants Professor Chen Feng (NYU) and Professor Xiaoming Liu (MSU) received the Best Oral Paper Award at the IEEE/CVF International Conference on Computer Vision (ICCV) 2019 Workshop on Statistical Deep Learning in Computer Vision (SDL-CV) held in Seoul, Korea. Their paper, entitled "UGLLI Face Alignment: Estimating Uncertainty with Gaussian Log-Likelihood Loss," describes a method which, given an image of a face, estimates not only the locations of facial landmarks but also the uncertainty of each landmark location estimate.
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AWARD MERL Researcher Devesh Jha Wins the Rudolf Kalman Best Paper Award 2019 Date: October 10, 2019
Awarded to: Devesh Jha, Nurali Virani, Zhenyuan Yuan, Ishana Shekhawat and Asok Ray
MERL Contact: Devesh Jha
Research Areas: Artificial Intelligence, Control, Data Analytics, Machine Learning, RoboticsBrief- MERL researcher Devesh Jha has won the Rudolf Kalman Best Paper Award 2019 for the paper entitled "Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models". This paper, published in a Special Commemorative Issue for Rudolf E. Kalman in the ASME JDSMC in March 2018, uses Bayesian filtering for imitation learning in Hidden Mode Hybrid Systems. This award is given annually by the Dynamic Systems and Control Division of ASME to the authors of the best paper published in the ASME Journal of Dynamic Systems Measurement and Control during the preceding year.
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News & Events
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NEWS MERL researchers presented 8 papers at Conference on Decision and Control (CDC) Date: December 11, 2019 - December 13, 2019
Where: Nice, France
MERL Contacts: Mouhacine Benosman; Karl Berntorp; Scott Bortoff; Ankush Chakrabarty; Claus Danielson; Stefano Di Cairano; Uroš Kalabić; Jing Zhang
Research Areas: Control, Machine Learning, OptimizationBrief- At the Conference on Decision and Control, MERL presented 8 papers on subjects including estimation for thermal-fluid models and transportation networks, analysis of HVAC systems, extremum seeking for multi-agent systems, reinforcement learning for vehicle platoons, and learning with applications to autonomous vehicles.
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NEWS MERL Scientists Presenting 11 Papers at IEEE Global Communications Conference (GLOBECOM) 2019 Date: December 9, 2019 - December 13, 2019
Where: Waikoloa, Hawaii, USA
MERL Contacts: Jianlin Guo; Kyeong Jin (K.J.) Kim; Toshiaki Koike-Akino; Rui Ma; Philip Orlik; Pu (Perry) Wang
Research Areas: Communications, Computer Vision, Machine Learning, Signal Processing, Information SecurityBrief- MERL Signal Processing scientists and collaborators will be presenting 11 papers at the IEEE Global Communications Conference (GLOBECOM) 2019, which is being held in Waikoloa, Hawaii from December 9-13, 2019. Topics to be presented include recent advances in power amplifier, MIMO algorithms, WiFi sensing, video casting, visible light communications, user authentication, vehicular communications, secrecy, and relay systems, including sophisticated machine learning applications. A number of these papers are a result of successful collaboration between MERL and world-leading Universities including: Osaka University, University of New South Wales, Oxford University, Princeton University, South China University of Technology, Massachusetts Institute of Technology and Aalborg University.
GLOBECOM is one of the IEEE Communications Society’s two flagship conferences dedicated to driving innovation in nearly every aspect of communications. Each year, more than 3000 scientific researchers and their management submit proposals for program sessions to be held at the annual conference. Themed “Revolutionizing Communications,” GLOBECOM2019 will feature a comprehensive high-quality technical program including 13 symposia and a variety of tutorials and workshops to share visions and ideas, obtain updates on latest technologies and expand professional and social networking.
- MERL Signal Processing scientists and collaborators will be presenting 11 papers at the IEEE Global Communications Conference (GLOBECOM) 2019, which is being held in Waikoloa, Hawaii from December 9-13, 2019. Topics to be presented include recent advances in power amplifier, MIMO algorithms, WiFi sensing, video casting, visible light communications, user authentication, vehicular communications, secrecy, and relay systems, including sophisticated machine learning applications. A number of these papers are a result of successful collaboration between MERL and world-leading Universities including: Osaka University, University of New South Wales, Oxford University, Princeton University, South China University of Technology, Massachusetts Institute of Technology and Aalborg University.
See All News & Events for Machine Learning -
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Research Highlights
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Internships
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CD1383: Collaborative Estimation for Robotic Manipulators
MERL is seeking a highly skilled and self-motivated intern to conduct research on condition monitoring for robotic manipulators. The ideal candidate should have solid backgrounds in robotic manipulators, stochastic estimation methods for dynamical systems, and collaborative strategies over multi-agents. Experience of applying machine learning to dynamical systems is a strong plus. Excellent coding skill and strong publication records are necessary. Senior Ph.D. students in control, robotics, or related areas are encouraged to apply. Start date for this internship is flexible, and the expected duration is about 3 months.
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SP1370: Machine Learning based DPD for Power Amplifier
MERL is looking for a talented intern to work on the next generation Digital-predistortion algorithms for power amplifier linearization such as 5G. The development of a DPD system involves aspects of signal processing and statistical algorithm design, RF components and instrumentation, digital hardware and software. It is therefore both a challenging and intellectually rewarding experience. This will involve MATLAB coding, interfacing to test equipment such as power sources, signal generators and analyzers and construction and calibration of RF component assemblies. The ideal candidate should have knowledge and experience in adaptive signal processing, machine learning, and radio communication. Good practical laboratory skills are needed. RF semiconductor devices and circuit knowledge is a plus. Duration is 3 to 6 months.
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CV1422: Generative Adversarial Networks (GANs)
Generative adversarial networks (GANs) and related methods have generated much excitement for their ability to synthesize images and data that appear remarkably realistic. MERL is seeking a highly motivated intern to conduct original research in the area of generative adversarial networks. The successful candidate will collaborate with MERL researchers to design and implement new models, conduct experiments, and prepare results for publication. The candidate should be a PhD student in computer vision with experience in GANs and related deep learning methods, as well as good general knowledge in machine learning and a strong publication record. Strong programming skills in Python, flexibility working across various deep learning platforms (e.g., PyTorch and TensorFlow), and previous experience coding GANs are expected.
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Openings
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Recent Publications
- , "Spatio-Temporal Ranked-Attention Networks for Video Captioning", IEEE Winter Conference on Applications of Computer Vision (WACV), February 2020.BibTeX Download PDFAbout TR2020-016
- @inproceedings{Cherian2020feb,
- author = {Cherian, Anoop and Wang, Jue and Hori, Chiori and Marks, Tim},
- title = {Spatio-Temporal Ranked-Attention Networks for Video Captioning},
- booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
- year = 2020,
- month = feb,
- url = {https://www.merl.com/publications/TR2020-016}
- }
- , "FX-GAN: Self-Supervised GAN Learning via Feature Exchange", IEEE Winter Conference on Applications of Computer Vision (WACV), February 2020.BibTeX Download PDFAbout TR2020-014
- @inproceedings{Huang2020feb,
- author = {Huang, Rui and Xu, Wenju and Lee, Teng-Yok and Cherian, Anoop and Wang, Ye and Marks, Tim},
- title = {FX-GAN: Self-Supervised GAN Learning via Feature Exchange},
- booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
- year = 2020,
- month = feb,
- url = {https://www.merl.com/publications/TR2020-014}
- }
- , "Learning a distance function with a Siamese network to localize anomalies in videos", IEEE Winter Conference on Applications of Computer Vision (WACV), February 2020.BibTeX Download PDFAbout TR2020-015
- @inproceedings{Jones2020feb,
- author = {Ramachandra, Bharathkumar and Jones, Michael J. and Vatsavai, Ranga},
- title = {Learning a distance function with a Siamese network to localize anomalies in videos},
- booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
- year = 2020,
- month = feb,
- url = {https://www.merl.com/publications/TR2020-015}
- }
- , "DynamicsExplorer: Visual Analytics for Robot Control Tasks involving Dynamics and LSTM-based Control Policies", IEEE Pacific Visualization Symposium (PacificVis), January 2020.BibTeX Download PDFAbout TR2020-011
- @inproceedings{He2020jan,
- author = {He, Wenbin and Lee, Teng-Yok and van Baar, Jeroen and Wittenburg, Kent B. and Shen, Han-Wei},
- title = {DynamicsExplorer: Visual Analytics for Robot Control Tasks involving Dynamics and LSTM-based Control Policies},
- booktitle = {IEEE Pacific Visualization Symposium (PacificVis)},
- year = 2020,
- month = jan,
- url = {https://www.merl.com/publications/TR2020-011}
- }
- , "Trajectory Tracking for Autonomous Vehicles on Varying Road Surfaces by Friction-Adaptive Nonlinear Model Predictive Control", Journal of Vehicle Systems Dynamics, January 2020.BibTeX Download PDFAbout TR2020-005
- @article{Berntorp2020jan,
- author = {Berntorp, Karl and Quirynen, Rien and Uno, Tomoki and Di Cairano, Stefano},
- title = {Trajectory Tracking for Autonomous Vehicles on Varying Road Surfaces by Friction-Adaptive Nonlinear Model Predictive Control},
- journal = {Journal of Vehicle Systems Dynamics},
- year = 2020,
- month = jan,
- url = {https://www.merl.com/publications/TR2020-005}
- }
- , "Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering", IEEE Transactions on Image Processing, January 2020.BibTeX Download PDFAbout TR2020-004
- @article{Chen2020jan,
- author = {Chen, Siheng and Duan, Chaojing and Yang, Yaoqing and Feng, Chen and Li, Duanshun and Tian, Dong},
- title = {Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering},
- journal = {IEEE Transactions on Image Processing},
- year = 2020,
- month = jan,
- url = {https://www.merl.com/publications/TR2020-004}
- }
- , "Deep Reinforcement Learning for Joint Bidding and Pricing of Load Serving Entity", IEEE Transactions on smart grids, DOI: 10.1109/TSG.2019.2903756, ISSN: 1949-3061, Vol. 10, No. 6, pp. 6366-6375, January 2020.BibTeX Download PDFAbout TR2020-003
- @article{Xu2020jan,
- author = {Xu, Hanchen and Sun, Hongbo and Nikovski, Daniel N. and Kitamura, Shoichi and Mori, Kazuyuki and Hashimoto, Hiroyuki},
- title = {Deep Reinforcement Learning for Joint Bidding and Pricing of Load Serving Entity},
- journal = {IEEE Transactions on smart grids},
- year = 2020,
- volume = 10,
- number = 6,
- pages = {6366--6375},
- month = jan,
- doi = {10.1109/TSG.2019.2903756},
- issn = {1949-3061},
- url = {https://www.merl.com/publications/TR2020-003}
- }
- , "MIMO-Speech: End-to-End Multi-Channel Multi-Speaker Speech Recognition", IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), ISBN: 978-1-7281-0305-1, December 2019, pp. 237-144.BibTeX Download PDFAbout TR2019-157
- @inproceedings{Chang2019dec,
- author = {Chang, Xuankai and Zhang, Wangyou and Qian, Yanmin and Le Roux, Jonathan and Watanabe, Shinji},
- title = {MIMO-Speech: End-to-End Multi-Channel Multi-Speaker Speech Recognition},
- booktitle = {IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)},
- year = 2019,
- pages = {237--144},
- month = dec,
- isbn = {978-1-7281-0305-1},
- url = {https://www.merl.com/publications/TR2019-157}
- }
- , "Spatio-Temporal Ranked-Attention Networks for Video Captioning", IEEE Winter Conference on Applications of Computer Vision (WACV), February 2020.
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