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.
Quick Links
-
Researchers
Takaaki
Hori
Michael
Jones
Tim
Marks
Jonathan
Le Roux
Philip
Orlik
Anoop
Cherian
Devesh
Jha
Yebin
Wang
Alan
Sullivan
Chiori
Hori
Daniel
Nikovski
Diego
Romeres
Gordon
Wichern
Karl
Berntorp
Toshiaki
Koike-Akino
Ye
Wang
Ankush
Chakrabarty
Stefano
Di Cairano
Arvind
Raghunathan
Jeroen
van Baar
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
-
Awards
-
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.
-
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.
-
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.
See All Awards for Machine Learning -
-
News & Events
-
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.
-
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 -
-
Research Highlights
-
Internships
-
SP1428: Big data learning
MERL is seeking a highly motivated, qualified individual to join the Signal Processing group for a three-month internship program in summer 2020. The candidate will be expected to carry out research on data analysis and applications of Deep/Machine Learning to topics in communication systems and networking. Anomaly detection methods of are particular interest, but specific work plan can be flexible. The ideal candidate should have knowledge of machine learning such as neural networks and data analysis as well as familiarity with wireless communication and networking technologies. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply.
-
CV1365: Machine learning for 3D computer vision
MERL is looking for a self-motivated intern to work on machine learning for 3D computer vision. There are several available topics to choose from. The ideal candidate would be a Ph.D. student with a strong background in machine learning and computer vision. Proficiency in Python programming is necessary. You are expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. Start date is flexible.
-
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.
See All Internships for Machine Learning -
-
Openings
See All Openings at MERL -
Recent Publications
- , "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, 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,
- month = jan,
- 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), December 2019.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,
- month = dec,
- url = {https://www.merl.com/publications/TR2019-157}
- }
- , "Robust Optimization for Trajectory-Centric Model-based Reinforcement Learning", NeurIPS Workshop on Safety and Robustness in Decision Making, December 2019.BibTeX Download PDFAbout TR2019-156
- @inproceedings{Jha2019dec2,
- author = {Jha, Devesh and Kolaric, Patrik and Romeres, Diego and Raghunathan, Arvind and Benosman, Mouhacine and Nikovski, Daniel N.},
- title = {Robust Optimization for Trajectory-Centric Model-based Reinforcement Learning},
- booktitle = {NeurIPS Workshop on Safety and Robustness in Decision Making},
- year = 2019,
- month = dec,
- url = {https://www.merl.com/publications/TR2019-156}
- }
- , "A Comparative Study on Transformer Vs RNN in Speech Applications", IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), December 2019.BibTeX Download PDFAbout TR2019-158
- @inproceedings{Karita2019dec,
- author = {Karita, Shigeki and Chen, Nanxin and Hayashi, Tomoki and Hori, Takaaki and Inaguma, Hirofumi and Jiang, Ziyan and Someki, Masao and Enrique Yalta Soplin, Nelson and Yamamoto, Ryuichi and Wang, Xiaofei and Watanabe, Shinji and Yoshimura, Takenori and Zhang, Wangyou},
- title = {A Comparative Study on Transformer Vs RNN in Speech Applications},
- booktitle = {IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)},
- year = 2019,
- month = dec,
- url = {https://www.merl.com/publications/TR2019-158}
- }
- , "Streaming End-to-End Speech Recognition with Joint CTC-Attention Based Models", IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), December 2019.BibTeX Download PDFAbout TR2019-159
- @inproceedings{Moritz2019dec,
- author = {Moritz, Niko and Hori, Takaaki and Le Roux, Jonathan},
- title = {Streaming End-to-End Speech Recognition with Joint CTC-Attention Based Models},
- booktitle = {IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)},
- year = 2019,
- month = dec,
- url = {https://www.merl.com/publications/TR2019-159}
- }
- , "Bayesian Learning of Tire Friction with Automotive-Grade Sensors by Gaussian-Process State-Space Models", IEEE Conference on Decision and Control (CDC), December 2019.BibTeX Download PDFAbout TR2019-145
- @inproceedings{Berntorp2019dec,
- author = {Berntorp, Karl and Hiroaki, Kitano},
- title = {Bayesian Learning of Tire Friction with Automotive-Grade Sensors by Gaussian-Process State-Space Models},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2019,
- month = dec,
- url = {https://www.merl.com/publications/TR2019-145}
- }
- , "Trajectory Tracking for Autonomous Vehicles on Varying Road Surfaces by Friction-Adaptive Nonlinear Model Predictive Control", Journal of Vehicle Systems Dynamics, January 2020.
-
Videos
-
Free Downloads
