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
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Researchers
Jonathan
Le Roux
Toshiaki
Koike-Akino
Ye
Wang
Tim K.
Marks
Anoop
Cherian
Ankush
Chakrabarty
Philip V.
Orlik
Gordon
Wichern
Devesh K.
Jha
Michael J.
Jones
Chiori
Hori
Daniel N.
Nikovski
Stefano
Di Cairano
Diego
Romeres
Alan
Sullivan
Yebin
Wang
Karl
Berntorp
Kieran
Parsons
Pu
(Perry)
WangChristopher R.
Laughman
Mouhacine
Benosman
Hassan
Mansour
Rien
Quirynen
Arvind
Raghunathan
Bingnan
Wang
Rui
Ma
Petros T.
Boufounos
Kyeong Jin
(K.J.)
KimMatthew E.
Brand
Jianlin
Guo
Suhas
Lohit
Hongbo
Sun
Radu
Corcodel
Marcel
Menner
Hongtao
Qiao
William S.
Yerazunis
Scott A.
Bortoff
Siddarth
Jain
Chungwei
Lin
Dehong
Liu
Kuan-Chuan
Peng
Koon Hoo
Teo
Anthony
Vetro
Jing
Zhang
Jinyun
Zhang
Marcus
Greiff
Yanting
Ma
Saleh
Nabi
Abraham P.
Vinod
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Awards
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AWARD Japan Telecommunications Advancement Foundation Award Date: March 15, 2022
Awarded to: Yukimasa Nagai, Jianlin Guo, Philip Orlik, Takenori Sumi, Benjamin A. Rolfe and Hiroshi Mineno
MERL Contacts: Jianlin Guo; Philip V. Orlik
Research Areas: Communications, Machine LearningBrief- MELCO/MERL research paper “Sub-1 GHz Frequency Band Wireless Coexistence for the Internet of Things” has won the 37th Telecommunications Advancement Foundation Award (Telecom System Technology Award) in Japan. This award started in 1984, and is given to research papers and works related to information and telecommunications that have made significant contributions and achievements to the advancement, development, and standardization of information and telecommunications from technical and engineering perspectives. The award recognizes both the IEEE 802.19.3 standardization efforts and the technological advancements using reinforcement learning and robust access methodologies for wireless communication system. This year, there were 43 entries with 5 winning awards and 3 winning encouragement awards. This is the first time MELCO/MERL has received this award. Our paper has been published by IEEE Access in 2021 and authors are Yukimasa Nagai, Jianlin Guo, Philip Orlik, Takenori Sumi, Benjamin A. Rolfe and Hiroshi Mineno.
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AWARD Mitsubishi Electric US Receives a 2022 CES Innovation Award for Touchless Elevator Control Jointly Developed with MERL Date: November 17, 2021
Awarded to: Elevators and Escalators Division of Mitsubishi Electric US, Inc.
MERL Contacts: Daniel N. Nikovski; William S. Yerazunis
Research Areas: Data Analytics, Machine Learning, Signal ProcessingBrief- The Elevators and Escalators Division of Mitsubishi Electric US, Inc. has been recognized as a 2022 CES® Innovation Awards honoree for its new PureRide™ Touchless Control for elevators, jointly developed with MERL. Sponsored by the Consumer Technology Association (CTA), the CES Innovation Awards is the largest and most influential technology event in the world. PureRide™ Touchless Control provides a simple, no-touch product that enables users to call an elevator and designate a destination floor by placing a hand or finger over a sensor. MERL initiated the development of PureRide™ in the first weeks of the COVID-19 pandemic by proposing the use of infra-red sensors for operating elevator call buttons, and participated actively in its rapid implementation and commercialization, resulting in a first customer installation in October of 2020.
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AWARD Daniel Nikovski receives Outstanding Reviewer Award at NeurIPS'21 Date: October 18, 2021
Awarded to: Daniel Nikovski
MERL Contact: Daniel N. Nikovski
Research Areas: Artificial Intelligence, Machine LearningBrief- Daniel Nikovski, Group Manager of MERL's Data Analytics group, has received an Outstanding Reviewer Award from the 2021 conference on Neural Information Processing Systems (NeurIPS'21). NeurIPS is the world's premier conference on neural networks and related technologies.
See All Awards for Machine Learning -
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News & Events
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NEWS MERL Scientists Presenting 5 Papers at IEEE International Conference on Communications (ICC) 2022 Date: May 16, 2022 - May 20, 2022
Where: Seoul, Korea
MERL Contacts: Jianlin Guo; Kyeong Jin (K.J.) Kim; Toshiaki Koike-Akino; Philip V. Orlik; Kieran Parsons; Pu (Perry) Wang; Ye Wang
Research Areas: Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Machine Learning, Signal ProcessingBrief- MERL Connectivity & Information Processing Team scientists remotely presented 5 papers at the IEEE International Conference on Communications (ICC) 2022, held in Seoul Korea on May 16-20, 2022. Topics presented include recent advancements in communications technologies, deep learning methods, and quantum machine learning (QML). Presentation videos are also found on our YouTube channel. In addition, K. J. Kim organized "Industrial Private 5G-and-beyond Wireless Networks Workshop" at the conference.
IEEE ICC is one of two IEEE Communications Society’s flagship conferences (ICC and Globecom). Each year, close to 2,000 attendees from over 70 countries attend IEEE ICC to take advantage of a program which consists of exciting keynote session, robust technical paper sessions, innovative tutorials and workshops, and engaging industry sessions. This 5-day event is known for bringing together audiences from both industry and academia to learn about the latest research and innovations in communications and networking technology, share ideas and best practices, and collaborate on future projects.
- MERL Connectivity & Information Processing Team scientists remotely presented 5 papers at the IEEE International Conference on Communications (ICC) 2022, held in Seoul Korea on May 16-20, 2022. Topics presented include recent advancements in communications technologies, deep learning methods, and quantum machine learning (QML). Presentation videos are also found on our YouTube channel. In addition, K. J. Kim organized "Industrial Private 5G-and-beyond Wireless Networks Workshop" at the conference.
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NEWS Arvind Raghunathan's publication is Featured Article in the current issue of the INFORMS Journal on Computing Date: April 1, 2022
Where: INFORMS Journal on Computing (https://pubsonline.informs.org/journal/ijoc)
MERL Contact: Arvind Raghunathan
Research Areas: Artificial Intelligence, Machine Learning, OptimizationBrief- Arvind Raghunathan co-authored a publication titled "JANOS: An Integrated Predictive and Prescriptive Modeling Framework" which has been chosen as a Featured Article in the current issue of the INFORMS Journal on Computing. The article was co-authored with Prof. David Bergman, a collaborator of MERL and Teng Huang, a former MERL intern, among others.
The paper describes a new software tool, JANOS, that integrates predictive modeling and discrete optimization to assist decision making. Specifically, the proposed solver takes as input user-specified pretrained predictive models and formulates optimization models directly over those predictive models by embedding them within an optimization model through linear transformations.
- Arvind Raghunathan co-authored a publication titled "JANOS: An Integrated Predictive and Prescriptive Modeling Framework" which has been chosen as a Featured Article in the current issue of the INFORMS Journal on Computing. The article was co-authored with Prof. David Bergman, a collaborator of MERL and Teng Huang, a former MERL intern, among others.
See All News & Events for Machine Learning -
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Research Highlights
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Internships
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CA1742: Mixed-Integer Programming for Motion Planning and Control
MERL is looking for a highly motivated individual to work on tailored computational algorithms and applications of mixed-integer programming for decision making, motion planning and control of hybrid systems. The research will involve the study and development of numerical optimization techniques and/or the implementation and validation of algorithms for industrial applications, e.g., related to autonomous driving and robotics. The ideal candidate should have experience in either one or multiple of the following topics: branch-and-bound type methods, heuristics for mixed-integer programming (pre-solve, cutting planes, warm starting, integer-feasible solutions), modeling and formulation of MIPs for hybrid control systems, convex and non-convex optimization, machine learning and real-time optimization. PhD students in engineering or mathematics, especially with a focus on mixed-integer programming or numerical optimization, are encouraged to apply. Publication of relevant results in conference proceedings and journals is expected. Capability of implementing the designs and algorithms in MATLAB/Python is expected; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3-6 months and the start date is flexible.
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CI1468: Quantum Machine Learning
MERL is seeking an intern to work on research for quantum machine learning (QML). The ideal candidate is an experienced PhD student or post-graduate researcher having an excellent background in quantum computing, deep learning, and signal processing. Proficient programming skills with PyTorch, Qiskit, and PennyLane will be additional assets to this position.
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MS1838: Data-Driven Optimization for Building Energy Systems
MERL is looking for a highly motivated and qualified candidate to work on data-driven, sample-efficient optimization with real-world applications in building energy systems. The ideal candidate will have a strong understanding machine learning or sampling-based optimization with expertise demonstrated via, e.g., publications, in at least one of: few-shot optimization, Bayesian methods, and/or learning for control/estimation of buildings and energy systems. Hands-on programming experience with standard ML toolkits such as PyTorch/Tensorflow is preferred; knowledge of additional, relevant tools (e.g., GPyTorch, Pyro) is a plus. PhD students are preferred, as an expected outcome of the internship is a publication in a high-tier venue. The minimum duration of the internship is 12 weeks; start time is flexible. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.
See All Internships for Machine Learning -
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Recent Publications
- "Safe multi-agent motion planning via filtered reinforcement learning", IEEE International Conference on Robotics and Automation (ICRA) 2022, May 2022.BibTeX TR2022-053 PDF Video
- @inproceedings{Vinod2022may,
- author = {Vinod, Abraham P. and Safaoui, Sleiman and Chakrabarty, Ankush and Quirynen, Rien and yoshikawa, nobuyuki and Di Cairano, Stefano},
- title = {Safe multi-agent motion planning via filtered reinforcement learning},
- booktitle = {IEEE International Conference on Robotics and Automation (ICRA) 2022},
- year = 2022,
- month = may,
- url = {https://www.merl.com/publications/TR2022-053}
- }
, - "Variational Quantum Compressed Sensing for Joint User and Channel State Acquisition in Grant-Free Device Access Systems", IEEE International Conference on Communications (ICC), May 2022.BibTeX TR2022-052 PDF Video Presentation
- @inproceedings{Liu2022may3,
- author = {Liu, Bryan and Koike-Akino, Toshiaki and Wang, Ye and Parsons, Kieran},
- title = {Variational Quantum Compressed Sensing for Joint User and Channel State Acquisition in Grant-Free Device Access Systems},
- booktitle = {IEEE International Conference on Communications (ICC)},
- year = 2022,
- month = may,
- url = {https://www.merl.com/publications/TR2022-052}
- }
, - "Quantum Transfer Learning for Wi-Fi Sensing", IEEE International Conference on Communications (ICC), May 2022.BibTeX TR2022-044 PDF Video Presentation
- @inproceedings{Koike-Akino2022may2,
- author = {Koike-Akino, Toshiaki and Pu, Wang and Wang, Ye},
- title = {Quantum Transfer Learning for Wi-Fi Sensing},
- booktitle = {IEEE International Conference on Communications (ICC)},
- year = 2022,
- month = may,
- url = {https://www.merl.com/publications/TR2022-044}
- }
, - "Finding the Right Deep Neural Network Model for Efficient Design of Tunable Nanophotonic Devices", Conference on Lasers and Electro-Optics (CLEO), May 2022.BibTeX TR2022-047 PDF Video Presentation
- @inproceedings{Jung2022may,
- author = {Jung, Minwoo and Kojima, Keisuke and Koike-Akino, Toshiaki and Wang, Ye and Zhu, Dayu and Brand, Matthew E.},
- title = {Finding the Right Deep Neural Network Model for Efficient Design of Tunable Nanophotonic Devices},
- booktitle = {Conference on Lasers and Electro-Optics (CLEO)},
- year = 2022,
- month = may,
- url = {https://www.merl.com/publications/TR2022-047}
- }
, - "AutoML Hyperparameter Tuning of Generative DNN Architecture for Nanophotonic Device Design", Conference on Lasers and Electro-Optics (CLEO), May 2022.BibTeX TR2022-046 PDF Presentation
- @inproceedings{Koike-Akino2022may3,
- author = {Koike-Akino, Toshiaki and Kojima, Keisuke and Wang, Ye},
- title = {AutoML Hyperparameter Tuning of Generative DNN Architecture for Nanophotonic Device Design},
- booktitle = {Conference on Lasers and Electro-Optics (CLEO)},
- year = 2022,
- month = may,
- url = {https://www.merl.com/publications/TR2022-046}
- }
, - "Reinforcement Learning State Estimation for High-Dimensional Nonlinear Systems", International Conference on Learning Representations (ICLR) Workshop, April 2022.BibTeX TR2022-042 PDF
- @inproceedings{Mowlavi2022apr,
- author = {Mowlavi, Saviz and Benosman, Mouhacine and Nabi, Saleh},
- title = {Reinforcement Learning State Estimation for High-Dimensional Nonlinear Systems},
- booktitle = {International Conference on Learning Representations (ICLR) Workshop},
- year = 2022,
- month = apr,
- url = {https://www.merl.com/publications/TR2022-042}
- }
, - "Extended Graph Temporal Classification for Multi-Speaker End-to-End ASR", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), April 2022.BibTeX TR2022-021 PDF
- @inproceedings{Chang2022apr,
- author = {Chang, Xuankai and Moritz, Niko and Hori, Takaaki and Watanabe, Shinji and Le Roux, Jonathan},
- title = {Extended Graph Temporal Classification for Multi-Speaker End-to-End ASR},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2022,
- month = apr,
- url = {https://www.merl.com/publications/TR2022-021}
- }
, - "Advancing Momentum Pseudo-Labeling with Conformer and Initialization Strategy", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), April 2022.BibTeX TR2022-026 PDF
- @inproceedings{Higuchi2022apr,
- author = {Higuchi, Yosuke and Moritz, Niko and Le Roux, Jonathan and Hori, Takaaki},
- title = {Advancing Momentum Pseudo-Labeling with Conformer and Initialization Strategy},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2022,
- month = apr,
- url = {https://www.merl.com/publications/TR2022-026}
- }
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- "Safe multi-agent motion planning via filtered reinforcement learning", IEEE International Conference on Robotics and Automation (ICRA) 2022, May 2022.
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Videos
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[MERL Seminar Series Spring 2022] Hybrid robotics and implicit learning
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Toshiaki Koike-Akino Gives Seminar Talk at IEEE Boston Photonics
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[MERL Seminar Series Spring 2022] RLMPC: An Ideal Combination of Formal Optimal Control and Reinforcement Learning?
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[MERL Seminar Series Spring 2022] Self-Supervised Scene Representation Learning
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[MERL Seminar Series Spring 2022] Learning Speech Representations with Multimodal Self-Supervision
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[MERL Seminar Series Spring 2022] Extreme optics design as a large-scale optimization problem
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HealthCam: A system for non-contact monitoring of vital signs
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[MERL Seminar Series 2021] Harnessing machine learning to build better Earth system models for climate projection
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[MERL Seminar Series 2021] Deep probabilistic regression
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[MERL Seminar Series 2021] Learning to See by Moving: Self-supervising 3D scene representations for perception, control, and visual reasoning
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Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models
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Application of Deep Learning for Nanophotonic Device Design (Invited)
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Towards Human-Level Learning of Complex Physical Puzzles
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Tactile-RL for Insertion: Generalization to Objects of Unknown Geometry
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Scene-Aware Interaction Technology
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Action Detection Using A Deep Recurrent Neural Network
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Obstacle Detection
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Semantic Scene Labeling
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MERL Research on Autonomous Vehicles
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Deep Hierarchical Parsing for Semantic Segmentation
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Global Local Face Upsampling Network
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Software Downloads
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Instance Segmentation GAN
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Audio Visual Scene-Graph Segmentor
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Hierarchical Musical Instrument Separation
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Generating Visual Dynamics from Sound and Context
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Adversarially-Contrastive Optimal Transport
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Online Feature Extractor Network
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MotionNet
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FoldingNet++
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Quasi-Newton Trust Region Policy Optimization
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Landmarks’ Location, Uncertainty, and Visibility Likelihood
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Robust Iterative Data Estimation
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Gradient-based Nikaido-Isoda
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Circular Maze Environment
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Discriminative Subspace Pooling
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Kernel Correlation Network
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Fast Resampling on Point Clouds via Graphs
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FoldingNet
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Deep Category-Aware Semantic Edge Detection
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