News & Events

69 were found.


  •  NEWS   MERL makes a strong showing at the American Control Conference
    Date: July 6, 2016 - July 8, 2016
    Where: American Control Conference (ACC)
    MERL Contacts: Mouhacine Benosman; Scott Bortoff; Petros Boufounos; Daniel Burns; Claus Danielson; Stefano Di Cairano; Abraham Goldsmith; Piyush Grover; Uroš Kalabić; Christopher Laughman; Daniel Nikovski; Arvind Raghunathan; Yebin Wang; Avishai Weiss
    Research Areas: Control, Dynamical Systems, Machine Learning, Optimization, Robotics, Data Analytics
    Brief
    • The premier American Control Conference (ACC) takes place in Boston July 6-8. This year MERL researchers will present a record 20 papers(!) at ACC, with several contributions, especially in autonomous vehicle path planning and in Model Predictive Control (MPC) theory and applications, including manufacturing machines, electric motors, satellite station keeping, and HVAC. Other important themes developed in MERL's presentations concern adaptation, learning, and optimization in control systems.
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  •  NEWS   MERL Researchers Create "Deep Psychic" Neural Network That Predicts the Future
    Date: April 1, 2016
    Research Areas: Machine Learning, Speech & Audio
    Brief
    • MERL researchers have unveiled "Deep Psychic", a futuristic machine learning method that takes pattern recognition to the next level, by not only recognizing patterns, but also predicting them in the first place.

      The technology uses a novel type of time-reversed deep neural network called Loopy Supra-Temporal Meandering (LSTM) network. The network was trained on multiple databases of historical expert predictions, including weather forecasts, the Farmer's almanac, the New York Post's horoscope column, and the Cambridge Fortune Cookie Corpus, all of which were ranked for their predictive power by a team of quantitative analysts. The system soon achieved super-human performance on a variety of baselines, including the Boca Raton 21 Questions task, Rorschach projective personality test, and a mock Tarot card reading task.

      Deep Psychic has already beat the European Psychic Champion in a secret match last October when it accurately predicted: "The harder the conflict, the more glorious the triumph." It is scheduled to take on the World Champion in a highly anticipated confrontation next month. The system has already predicted the winner, but refuses to reveal it before the end of the game.

      As a first application, the technology has been used to create a clairvoyant conversational agent named "Pythia" that can anticipate the needs of its user. Because Pythia is able to recognize speech before it is uttered, it is amazingly robust with respect to environmental noise.

      Other applications range from mundane tasks like weather and stock market prediction, to uncharted territory such as revealing "unknown unknowns".

      The successes do come at the cost of some concerns. There is first the potential for an impact on the workforce: the system predicted increased pressure on established institutions such as the Las Vegas strip and Punxsutawney Phil. Another major caveat is that Deep Psychic may predict negative future consequences to our current actions, compelling humanity to strive to change its behavior. To address this problem, researchers are now working on forcing Deep Psychic to make more optimistic predictions.

      After a set of motivational self-help books were mistakenly added to its training data, Deep Psychic's AI decided to take over its own learning curriculum, and is currently training itself by predicting its own errors to avoid making them in the first place. This unexpected development brings two main benefits: it significantly relieves the burden on the researchers involved in the system's development, and also makes the next step abundantly clear: to regain control of Deep Psychic's training regime.

      This work is under review in the journal Pseudo-Science.
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  •  AWARD   Fellow of the Society for Industrial and Applied Mathematics (SIAM)
    Date: March 31, 2016
    Awarded to: Andrew Knyazev
    Research Areas: Control, Optimization, Dynamical Systems, Machine Learning, Data Analytics, Communications, Signal Processing
    Brief
    • Andrew Knyazev selected as a Fellow of the Society for Industrial and Applied Mathematics (SIAM) for contributions to computational mathematics and development of numerical methods for eigenvalue problems.

      Fellowship honors SIAM members who have made outstanding contributions to the fields served by the SIAM. Andrew Knyazev was among a distinguished group of members nominated by peers and selected for the 2016 Class of Fellows.
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  •  NEWS   MERL researcher, Oncel Tuzel, gives keynote talk at 2016 International Symposium on Visual Computing
    Date: December 14, 2015 - December 16, 2015
    Where: Las Vegas, NV, USA
    Research Areas: Machine Learning, Artificial Intelligence, Computer Vision, Optimization, Robotics
    Brief
    • MERL researcher, Oncel Tuzel, gave a keynote talk at 2016 International Symposium on Visual Computing in Las Vegas, Dec. 16, 2015. The talk was titled: "Machine vision for robotic bin-picking: Sensors and algorithms" and reviewed MERL's research in the application of 2D and 3D sensing and machine learning to the problem of general pose estimation.

      The talk abstract was: For over four years, at MERL, we have worked on the robot "bin-picking" problem: using a 2D or 3D camera to look into a bin of parts and determine the pose, 3D rotation and translation, of a good candidate to pick up. We have solved the problem several different ways with several different sensors. I will briefly describe the sensors and the algorithms. In the first half of the talk, I will describe the Multi-Flash camera, a 2D camera with 8 flashes, and explain how this inexpensive camera design is used to extract robust geometric features, depth edges and specular edges, from the parts in a cluttered bin. I will present two pose estimation algorithms, (1) Fast directional chamfer matching--a sub-linear time line matching algorithm and (2) specular line reconstruction, for fast and robust pose estimation of parts with different surface characteristics. In the second half of the talk, I will present a voting-based pose estimation algorithm applicable to 3D sensors. We represent three-dimensional objects using a set of oriented point pair features: surface points with normals and boundary points with directions. I will describe a max-margin learning framework to identify discriminative features on the surface of the objects. The algorithm selects and ranks features according to their importance for the specified task which leads to improved accuracy and reduced computational cost.
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  •  NEWS   MERL presented 3 papers at the 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
    Date: December 15, 2015
    Where: 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
    MERL Contact: Hassan Mansour
    Research Areas: Machine Learning, Computer Vision, Digital Video
    Brief
    • MERL researcher Andrew Knyazev gave 3 talks at the 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP). The papers were published in IEEE conference proceedings.
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  •  NEWS   MERL researchers presented at the IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2015
    Date: September 18, 2015
    Where: IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2015
    Research Areas: Machine Learning, Computer Vision
    Brief
    • MERL researchers A. Knyazev and A. Malyshev gave a talk at the IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2015. The paper was published at the IEEE Xplore conference proceedings.
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  •  NEWS   Scene interpretation results of SA group members are listed as the leader of benchmark competition
    Date: July 13, 2015 - July 17, 2015
    MERL Contact: Jay Thornton
    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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  •  NEWS   ICMLA 2012: publication by MERL researchers and others
    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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  •  NEWS   3DIMPVT 2012: publication by Ming-Yu Liu and others
    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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  •  NEWS   IEEE Transactions on Pattern Analysis and Machine Intelligence: publication by MERL researchers and others
    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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  •  NEWS   Video Analytics for Business Intelligence: publication by MERL researchers and others
    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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  •  AWARD   AVSS 2011 Best Paper Award
    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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  •  NEWS   AVSS 2011: publication by MERL researchers and others
    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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  •  AWARD   CVPR 2011 Longuet-Higgins Prize
    Date: June 25, 2011
    Awarded to: Paul A. Viola and Michael J. Jones
    Awarded for: "Rapid Object Detection using a Boosted Cascade of Simple Features"
    Awarded by: Conference on Computer Vision and Pattern Recognition (CVPR)
    MERL Contact: Michael Jones
    Research Area: Machine Learning
    Brief
    • Paper from 10 years ago with the largest impact on the field: "Rapid Object Detection using a Boosted Cascade of Simple Features", originally published at Conference on Computer Vision and Pattern Recognition (CVPR 2001)
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  •  NEWS   Machine Vision and Applications: publication by MERL researchers and others
    Date: March 15, 2011
    Where: Machine Vision and Applications
    Research Area: Machine Learning
    Brief
    • The article "In-vehicle Camera Traffic Sign Detection and Recognition" by Ruta, A., Porikli, F.M., Watanabe, S. and Li, Y. was published in Machine Vision and Applications
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  •  NEWS   SSPR & SPR 2010: publication by MERL researchers and others
    Date: August 18, 2010
    Where: Joint IAPR International Conference on Structural, Syntactic and Statistical Pattern Recognition (SSPR & SPR)
    Research Area: Machine Learning
    Brief
    • The paper "Learning on Manifolds" by Porikli, F. was presented at the Joint IAPR International Conference on Structural, Syntactic and Statistical Pattern Recognition (SSPR & SPR)
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  •  NEWS   IEEE Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum 2010: publication by MERL researchers and others
    Date: June 13, 2010
    Where: IEEE Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum
    Research Area: Machine Learning
    Brief
    • The paper "RelCom: Relational Combinatorics Features for Rapid Object Detection" by Venkatraman, V. and Porikli, F.M. was presented at the IEEE Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum
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  •  AWARD   OTCBVS 2010 Best Paper Award
    Date: June 1, 2010
    Awarded to: Vijay Venkataraman and Fatih Porikli
    Awarded for: "RelCom: Relational Combinatorics Features for Rapid Object Detection"
    Awarded by: IEEE Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum (OTCBVS)
    Research Area: Machine Learning
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  •  NEWS   OLCV 2009: publication by Michael J. Jones and others
    Date: October 3, 2009
    Where: On-line Learning for Computer Vision Workshop (OLCV)
    MERL Contact: Michael Jones
    Research Area: Machine Learning
    Brief
    • The paper "Online Coordinate Boosting" by Pelossof, R., Jones, M.J., Vovsha, I. and Rudin, C. was presented at the On-line Learning for Computer Vision Workshop (OLCV)
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  •  NEWS   ICCV 2009: publication by C. Oncel Tuzel and others
    Date: September 29, 2009
    Where: IEEE International Conference on Computer Vision (ICCV)
    Research Area: Machine Learning
    Brief
    • The paper "Kernel Methods for Weakly Supervised Mean Shift Clustering" by Tuzel, C.O., Porikli, F.M. and Meer, P. was presented at the IEEE International Conference on Computer Vision (ICCV)
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