- Date: March 20, 2019
Where: Hsinchu, Taiwan
MERL Contact: Anthony Vetro
Research Area: Artificial Intelligence
Brief - Anthony Vetro gave a keynote at the inaugural IEEE Conference on Artificial Intelligence Circuits and Systems (AICAS), which was held in Hsinchu, Taiwan from March 18-20, 2019. The talk focused on edge intelligence for optimized systems and high-performance devices.
Abstract: The combination of IoT sensing, edge computing and AI algorithms is creating new opportunities to use real-time data to optimize system capabilities and increase device performance. In the manufacturing domain, edge intelligence allows us to realize various forms of anomaly detection, predict the lifetime or maintenance schedule of components, and adaptive learn improved control policies. Connected cars will benefit from edge intelligence to improve safety and optimize traffic flows. Additionally, the parameters of a circuit can be automatically tuned using data-driven machine learning techniques to increase efficiency and performance. This presentation highlights the numerous benefits of the edge intelligence framework, and identifies several open challenges and issues.
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- Date: November 16, 2018
Awarded to: Ziming Zhang, Alan Sullivan, Hideaki Maehara, Kenji Taira, Kazuo Sugimoto
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
Brief - Researchers and developers from MERL, Mitsubishi Electric and Mitsubishi Electric Engineering (MEE) have been recognized with an R&D100 award for the development of a deep learning-based water detector. Automatic detection of water levels in rivers and streams is critical for early warning of flash flooding. Existing systems require a height gauge be placed in the river or stream, something that is costly and sometimes impossible. The new deep learning-based water detector uses only images from a video camera along with 3D measurements of the river valley to determine water levels and warn of potential flooding. The system is robust to lighting and weather conditions working well during the night as well as during fog or rain. Deep learning is a relatively new technique that uses neural networks and AI that are trained from real data to perform human-level recognition tasks. This work is powered by Mitsubishi Electric's Maisart AI technology.
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- Date: October 15, 2018 - October 19, 2018
Where: CEATEC'18, Makuhari Messe, Tokyo
MERL Contacts: Devesh K. Jha; Daniel N. Nikovski; Diego Romeres; William S. Yerazunis
Research Areas: Artificial Intelligence, Computer Vision, Data Analytics, Robotics
Brief - MERL's work on robot learning algorithms was demonstrated at CEATEC'18, Japan's largest IT and electronics exhibition and conference held annually at Makuhari Messe near Tokyo. A team of researchers from the Data Analytics Group at MERL and the Artificial Intelligence Department of the Information Technology Center (ITC) of MELCO presented an interactive demonstration of a model-based artificial intelligence algorithm that learns how to control equipment autonomously. The algorithm developed at MERL constructs models of mechanical equipment through repeated trial and error, and then learns control policies based on these models. The demonstration used a circular maze, where the objective is to drive a ball to the center of the maze by tipping and tilting the maze, a task that is difficult even for humans; approximately half of the CEATEC'18 visitors who tried to steer the ball by means of a joystick could not bring it to the center of the maze within one minute. In contrast, MERL's algorithm successfully learned how to drive the ball to the goal within ten seconds without the need for human programming. The demo was at the entrance of MELCO's booth at CEATEC'18, inviting visitors to learn more about MELCO's many other AI technologies on display, and was seen by an estimated more than 50,000 visitors over the five days of the expo.
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- Date: Wednesday, September 26, 2018 - Friday, September 28, 2018
Location: Houston, Texas
MERL Contacts: Chiori Hori; Elizabeth Phillips
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
Brief - "MERL, in partnership with Mitsubishi Electric was a Gold Sponsor of the Grace Hopper Celebration 2018 (GHC18) held in Houston, TX on September 26-28th. Presented by AnitaB.org and the Association for Computing Machinery, this is world's largest gathering of women technologists. Chiori Hori and Elizabeth Phillips from MERL, and Yoshiyuki Umei, Jared Baker and Lien Randle from MEUS, proudly represented Mitsubishi Electric at the recruiting expo, that drew over 20,000 female technologists this year.
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- Date & Time: Thursday, November 29, 2018; 4-6pm
Location: 201 Broadway, 8th floor, Cambridge, MA
MERL Contacts: Elizabeth Phillips; Anthony Vetro
Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio
Brief - Snacks, demos, science: On Thursday 11/29, Mitsubishi Electric Research Labs (MERL) will host an open house for graduate+ students interested in internships, post-docs, and research scientist positions. The event will be held from 4-6pm and will feature demos & short presentations in our main areas of research including artificial intelligence, robotics, computer vision, speech processing, optimization, machine learning, data analytics, signal processing, communications, sensing, control and dynamical systems, as well as multi-physyical modeling and electronic devices. MERL is a high impact publication-oriented research lab with very extensive internship and university collaboration programs. Most internships lead to publication; many of our interns and staff have gone on to notable careers at MERL and in academia. Come mix with our researchers, see our state of the art technologies, and learn about our research opportunities. Dress code: casual, with resumes.
Pre-registration for the event is strongly encouraged:
merlopenhouse.eventbrite.com
Current internship and employment openings:
www.merl.com/internship/openings
www.merl.com/employment/employment
Information about working at MERL:
www.merl.com/employment.
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- Date: Thursday, October 19, 2017
Location: Google, New York, NY
MERL Contact: Jonathan Le Roux
Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
Brief - SANE 2017, a one-day event gathering researchers and students in speech and audio from the Northeast of the American continent, was held on Thursday October 19, 2017 at Google, in New York, NY. It broke the attendance record for a SANE event, with 180 participants.
It was a follow-up to SANE 2012, held at Mitsubishi Electric Research Labs (MERL), SANE 2013, held at Columbia University, SANE 2014, held at MIT CSAIL, SANE 2015, (already!) held at Google NY, and SANE 2016, held at MIT's McGovern Institute for Brain Research. Since the first edition, the audience has steadily grown, gathering over 100 researchers and students in recent editions.
As in 2013 and 2015, this year's SANE took place in conjunction with the WASPAA workshop, held October 15-18 in upstate New York. Many WASPAA attendees (around 70!) also attended SANE.
SANE 2017 featured invited talks by seven leading researchers from the Northeast and beyond: Sacha Krstulović (Audio Analytic), Yusuf Aytar (Google DeepMind), Florian Metze (CMU), Gunnar Evermann (Apple), Eric Humphrey (Spotify), Aaron Courville (University of Montreal), Aäron van den Oord (Google DeepMind). It also featured a live demo session with presentations by Jonathan Le Roux (MERL), Dan Ellis (Google), Arlo Faria (Remeeting), Tatsuya Komatsu (NEC), and a lively poster session with 26 posters.
SANE 2017 was co-organized by Jonathan Le Roux (MERL), Dan Ellis (Google), Michael I. Mandel (CUNY), Hank Liao (Google), and John R. Hershey (MERL). SANE remained a free event thanks to generous sponsorship by Google and MERL.
Slides and videos of the talks are available from the SANE workshop website.
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- Date: July 13, 2015 - July 17, 2015
Research Area: Machine Learning
Brief - SA group members (M. Liu, S. Lin (intern), S. Ramalingam, O. Tuzel) presented a paper at the Robotics Science and Systems Conference in Rome July 13-17 called 'Layered Interpretation of Street View Images'. The results they reported are now listed as the leader of the benchmark competition sponsored by Daimler. [Note that at that URL ref 2 is from collaboration with Daimler and it uses a FPGA for high speed, whereas MERL result is obtained with desktop computer and GPU.].
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- Date: April 20, 2015
Brief - Mitsubishi Electric researcher, Yuuki Tachioka of Japan, and MERL researcher, Shinji Watanabe, presented a paper at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP) entitled, "A Discriminative Method for Recurrent Neural Network Language Models". This paper describes a discriminative (language modelling) method for Japanese speech recognition. The Japanese Nikkei newspapers and some other press outlets reported on this method and its performance for Japanese speech recognition tasks.
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- Date: May 10, 2014
Where: REVERB Workshop
Research Area: Speech & Audio
Brief - Mitsubishi Electric's submission to the REVERB workshop achieved the second best performance among all participating institutes. The team included Yuuki Tachioka and Tomohiro Narita of MELCO in Japan, and Shinji Watanabe and Felix Weninger of MERL. The challenge addresses automatic speech recognition systems that are robust against varying room acoustics.
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- Date: June 1, 2013
Where: International Workshop on Machine Listening in Multisource Environments (CHiME)
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - The paper "Discriminative Methods for Noise Robust Speech Recognition: A CHiME Challenge Benchmark" by Tachioka, Y., Watanabe, S., Le Roux, J. and Hershey, J.R. was presented at the International Workshop on Machine Listening in Multisource Environments (CHiME).
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- Date: June 1, 2013
Awarded to: Yuuki Tachioka, Shinji Watanabe, Jonathan Le Roux and John R. Hershey
Awarded for: "Discriminative Methods for Noise Robust Speech Recognition: A CHiME Challenge Benchmark"
Awarded by: International Workshop on Machine Listening in Multisource Environments (CHiME)
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - The results of the 2nd 'CHiME' Speech Separation and Recognition Challenge are out! The team formed by MELCO researcher Yuuki Tachioka and MERL Speech & Audio team researchers Shinji Watanabe, Jonathan Le Roux and John Hershey obtained the best results in the continuous speech recognition task (Track 2). This very challenging task consisted in recognizing speech corrupted by highly non-stationary noises recorded in a real living room. Our proposal, which also included a simple yet extremely efficient denoising front-end, focused on investigating and developing state-of-the-art automatic speech recognition back-end techniques: feature transformation methods, as well as discriminative training methods for acoustic and language modeling. Our system significantly outperformed other participants. Our code has since been released as an improved baseline for the community to use.
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- Date: May 2, 2013
Where: International Conference on Learning Representations (ICLR)
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - The paper "Block Coordinate Descent for Sparse NMF" by Potluru, V.K., Plis, S.M., Le Roux, J., Pearlmutter, B.A., Calhoun, V.D. and Hayes, T.P. was presented at the International Conference on Learning Representations (ICLR).
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- Date: December 12, 2012
Where: International Conference on Machine Learning and Applications (ICMLA)
Research Area: Machine Learning
Brief - The paper "Compressive Clustering of High-Dimensional Data" by Ruta, A. and Porikli, F. was presented at the International Conference on Machine Learning and Applications (ICMLA).
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- Date: December 6, 2012
Where: APSIPA Transactions on Signal and Information Processing
Research Area: Speech & Audio
Brief - The article "Bayesian Approaches to Acoustic Modeling: A Review" by Watanabe, S. and Nakamura, A. was published in APSIPA Transactions on Signal and Information Processing.
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- Date: November 28, 2012
Where: Techniques for Noise Robustness in Automatic Speech Recognition
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - The article "Factorial Models for Noise Robust Speech Recognition" by Hershey, J.R., Rennie, S.J. and Le Roux, J. was published in the book Techniques for Noise Robustness in Automatic Speech Recognition.
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- Date: November 1, 2012
Where: IEEE Signal Processing Magazine
Research Area: Speech & Audio
Brief - The article "Structured Discriminative Models For Speech Recognition" by Gales, M., Watanabe, S. and Fosler-Lussier, E. was published in IEEE Signal Processing Magazine.
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- Date: October 22, 2012
Where: Annual Meeting of the Human Factors and Ergonomics Society (HFES)
Research Area: Speech & Audio
Brief - The paper "Evaluation of Two Types of In-Vehicle Music Retrieval and Navigation Systems" by Zhang, J., Borowsky, A., Schmidt-Nielsen, B., Harsham, B., Weinberg, G., Romoser, M.R.E. and Fisher, D.L. was presented at the Annual Meeting of the Human Factors and Ergonomics Society (HFES).
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- Date: October 13, 2012
Where: IEEE International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)
Research Area: Machine Learning
Brief - The paper "Classification and Pose Estimation of Vehicles in Videos by 3D Modeling within Discrete-Continuous Optimization" by Hodlmoser, M., Micusik, B., Liu, M.-Y., Pollefeys, M. and Kaampel, M. was presented at the IEEE International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT).
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- Date: June 28, 2012
Where: International Conference on Enterprise Information Systems (ICEIS)
MERL Contact: Daniel N. Nikovski
Research Area: Data Analytics
Brief - The paper "Bayesian Networks for Matcher Composition in Automatic Schema Matching" by Nikovski, D., Esenther, A., Ye, X., Shiba, M. and Takayama, S. was presented at the International Conference on Enterprise Information Systems (ICEIS).
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- Date: March 31, 2012
Where: International Workshop on Statistical Machine Learning for Speech Processing (IWSML)
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - The paper "Latent Dirichlet Reallocation for Term Swapping" by Heaukulani, C., Le Roux, J. and Hershey, J.R. was presented at the International Workshop on Statistical Machine Learning for Speech Processing (IWSML).
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- Date: March 13, 2012
Where: Acoustical Society of Japan Spring Meeting (ASJ)
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - The paper "Speech Enhancement by Indirect VTS" by Le Roux, J. and Hershey, J.R. was presented at the Acoustical Society of Japan Spring Meeting (ASJ).
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- Date: January 10, 2012
Where: IEEE Transactions on Pattern Analysis and Machine Intelligence
Research Area: Machine Learning
Brief - The article "Scalable Active Learning for Multi-Class Image Classification" by Joshi, A.J., Porikli, F. and Papanikolopoulos, N. was published in IEEE Transactions on Pattern Analysis and Machine Intelligence.
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- Date: January 1, 2012
Where: Video Analytics for Business Intelligence
Research Area: Machine Learning
Brief - The article "Object Detection & Tracking" by Porikli, F. and Yilmaz, A. was published in the book Video Analytics for Business Intelligence.
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- Date: September 2, 2011
Awarded to: Fatih Porikli and Huseyin Ozkan.
Awarded for: "Data Driven Frequency Mapping for Computationally Scalable Object Detection"
Awarded by: IEEE Advanced Video and Signal Based Surveillance (AVSS)
Research Area: Machine Learning
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- Date: August 30, 2011
Where: IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Research Area: Machine Learning
Brief - The paper "Data Driven Frequency Mapping for Computationally Scalable Object Detection" by Porikli, F. and Ozkan, H. was presented at the IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).
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