Artificial Intelligence
Making machines smarter for improved safety, efficiency and comfort.
Our AI research encompasses advances in computer vision, speech and audio processing, as well as data analytics. Key research themes include improved perception based on machine learning techniques, learning control policies through model-based reinforcement learning, as well as cognition and reasoning based on learned semantic representations. We apply our work to a broad range of automotive and robotics applications, as well as building and home systems.
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Researchers
Jonathan
Le Roux
Takaaki
Hori
Daniel
Nikovski
Michael
Jones
Tim
Marks
Chiori
Hori
Devesh
Jha
Anoop
Cherian
Alan
Sullivan
Toshiaki
Koike-Akino
Philip
Orlik
Ye
Wang
Bret
Harsham
Diego
Romeres
Gordon
Wichern
William
Yerazunis
Jeroen
van Baar
Arvind
Raghunathan
Emil
Laftchiev
Niko
Moritz
Saleh
Nabi
Petros
Boufounos
Siheng
Chen
Keisuke
Kojima
Chungwei
Lin
Kieran
Parsons
Hongbo
Sun
Yebin
Wang
Mouhacine
Benosman
Matthew
Brand
Radu
Corcodel
Kyeong Jin
(K.J.)
Kim
Hassan
Mansour
Bingnan
Wang
Jinyun
Zhang
Jianlin
Guo
Hiroyuki
Hashimoto
David
Millar
Anthony
Vetro
Siddarth
Jain
Suhas
Lohit
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 researcher Diego Romeres gave an invited talk at University of Connecticut on Reinforcement Learning for Robotics Date: November 20, 2019
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Data Analytics, Machine Learning, RoboticsBrief- Diego Romeres, a Research Scientist in MERL's Data Analytics group, gave a seminar lecture at the Electrical and Computer Engineering Colloquium of the University of Connecticut. The talk described novel reinforcement algorithms based on combining physical models with non-parametric models of robotic systems derived from data.
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NEWS Takaaki Hori elected to IEEE Technical Committee on Speech and Language Processing Date: November 9, 2019
MERL Contact: Takaaki Hori
Research Areas: Artificial Intelligence, Machine Learning, Speech & AudioBrief- Takaaki Hori has been elected to serve on the Speech and Language Processing Technical Committee (SLTC) of the IEEE Signal Processing Society for a 3-year term.
The SLTC promotes and influences all the technical areas of speech and language processing such as speech recognition, speech synthesis, spoken language understanding, speech to speech translation, spoken dialog management, speech indexing, information extraction from audio, and speaker and language recognition.
- Takaaki Hori has been elected to serve on the Speech and Language Processing Technical Committee (SLTC) of the IEEE Signal Processing Society for a 3-year term.
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Research Highlights
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Internships
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CV1386: Machine Learning for Computer Vision
MERL is looking for a self-motivated intern to work on machine learning for 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. Publication in a top-tier machine learning or computer vision venue (NIPS, CVPR, ECCV, ICCV, ICML, PAMI, etc) is preferred. Proficiency in Python programming is necessary. You are expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The internship would be 3-6 months, and the start date is flexible.
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CV1372: Computer Vision for Biased or Scarce Data
MERL is looking for a self-motivated intern to work on data scarcity and bias issues for computer vision. The topics in the scope include (but not limited to): domain adaptation, generative modeling, transfer/low-shot/unsupervised/webly-supervised learning, etc. The ideal candidate would be a PhD student with a strong background in computer vision and machine learning. Proficiency in Python programming and familiarity in at least one deep learning framework are necessary. The ideal candidate is expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The duration of the internship is expected to be at least 3 months with a flexible start date.
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CV1423: Health Monitoring from Video
MERL is seeking a highly motivated intern to conduct original research in the area of monitoring vital signs, such as heart rate and heart rate variability, from video of a person. The successful candidate will collaborate with MERL researchers to derive and implement new models, collect data, conduct experiments, and prepare results for publication. The candidate should be a PhD student in computer vision with a strong publication record and experience in computer vision, signal processing, machine learning, and health monitoring. Strong programming skills (C/C++, Python, Matlab, etc.) are expected.
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Recent Publications
- , "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}
- }
- , "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}
- }
- , "DNN-based Simultaneous Screen-to-Camera and Screen-to-Eye Communications", IEEE Global Communications Conference (GLOBECOM), December 2019.BibTeX Download PDFAbout TR2019-132
- @inproceedings{Fujihashi2019dec,
- author = {Fujihashi, Takuya and Koike-Akino, Toshiaki and Watanabe, Takashi and Orlik, Philip V.},
- title = {DNN-based Simultaneous Screen-to-Camera and Screen-to-Eye Communications},
- booktitle = {IEEE Global Communications Conference (GLOBECOM)},
- year = 2019,
- month = dec,
- url = {https://www.merl.com/publications/TR2019-132}
- }
- , "Deep Learning for Synchronization and Channel Estimation in NB-IoT Random Access Channel", IEEE Global Communications Conference (GLOBECOM), December 2019.BibTeX Download PDFAbout TR2019-140
- @inproceedings{Jespersen2019dec,
- author = {Jespersen, Mads Helge and Pajovic, Milutin and Koike-Akino, Toshiaki and Wang, Ye and Popovski, Petar and Orlik, Philip V.},
- title = {Deep Learning for Synchronization and Channel Estimation in NB-IoT Random Access Channel},
- booktitle = {IEEE Global Communications Conference (GLOBECOM)},
- year = 2019,
- month = dec,
- url = {https://www.merl.com/publications/TR2019-140}
- }
- , "QNTRPO: Including Curvature in TRPO", Optimization Foundations for Reinforcement Learning Workshop at NeurIPS, December 2019.BibTeX Download PDFAbout TR2019-154
- @inproceedings{Jha2019dec,
- author = {Jha, Devesh and Raghunathan, Arvind and Romeres, Diego},
- title = {QNTRPO: Including Curvature in TRPO},
- booktitle = {Optimization Foundations for Reinforcement Learning Workshop at NeurIPS},
- year = 2019,
- month = dec,
- url = {https://www.merl.com/publications/TR2019-154}
- }
- , "Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering", IEEE Transactions on Image Processing, January 2020.
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