News & Events

52 News items, Awards, Events or Talks found.



Learn about the MERL Seminar Series.



  •  NEWS    MERL launches Postdoctoral Research Fellow program
    Date: September 21, 2022
    MERL Contacts: Philip V. Orlik; 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
    • Mitsubishi Electric Research Laboratories (MERL) invites qualified postdoctoral candidates to apply for the position of Postdoctoral Research Fellow. This position provides early career scientists the opportunity to work at a unique, academically-oriented industrial research laboratory. Successful candidates will be expected to define and pursue their own original research agenda, explore connections to established laboratory initiatives, and publish high impact articles in leading venues. Please refer to our web page for further details.
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  •  NEWS    Yebin Wang appointed as an Associate Editor for ICRA 2023.
    Date: September 15, 2022
    MERL Contact: Yebin Wang
    Research Areas: Control, Dynamical Systems, Robotics
    Brief
    • Yebin Wang, a Senior Principal Research Scientist in MERL's Electric Machines and Devices, is serving as an Associate Editor for the IEEE International Conference on Robotics and Automation (ICRA) 2023.


      As the flagship conference of the IEEE Robotics and Automation Society, ICRA will bring together the world's top researchers and most important companies to share ideas and advances in our field.
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  •  AWARD    Marcus Greiff receives Outstanding Student Paper Award at CCTA 2022
    Date: August 25, 2022
    Awarded to: Marcus Greiff
    MERL Contact: Marcus Greiff
    Research Areas: Control, Dynamical Systems, Robotics
    Brief
    • Marcus Greiff, a Visiting Research Scientist at MERL, was awarded one of three outstanding student paper awards at the IEEE CCTA 2022 conference for his paper titled "Quadrotor Control on SU(2)xR3 with SLAM Integration". The award was given for originality, clarity, and potential impact on practical applications of control. The work presents a complete UAV control system design, facilitating autonomous supermarket inventorying without the need for external motion capture systems. A video of the experiments is on YouTube, including both simulations and real-time examples.
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  •  TALK    [MERL Seminar Series 2022] Prof. Sebastien Gros presents talk titled RLMPC: An Ideal Combination of Formal Optimal Control and Reinforcement Learning?
    Date & Time: Tuesday, April 12, 2022; 11:00 AM EDT
    Speaker: Sebastien Gros, NTNU
    MERL Host: Rien Quirynen
    Research Areas: Control, Dynamical Systems, Optimization
    Abstract
    • Reinforcement Learning (RL), similarly to many AI-based techniques, is currently receiving a very high attention. RL is most commonly supported by classic Machine Learning techniques, i.e. typically Deep Neural Networks (DNNs). While there are good motivations for using DNNs in RL, there are also significant drawbacks. The lack of “explainability” of the resulting control policies, and the difficulty to provide guarantees on their closed-loop behavior (safety, stability) makes DNN-based policies problematic in many applications. In this talk, we will discuss an alternative approach to support RL, via formal optimal control tools based on Model Predictive Control (MPC). This approach alleviates the issues detailed above, but also presents some challenges. In this talk, we will discuss why MPC is a valid tool to support RL, and how MPC can be combined with RL (RLMPC). We will then discuss some recent results regarding this combination, the known challenges, and the kind of control applications where we believe that RLMPC will be a valuable approach.
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  •  TALK    [MERL Seminar Series 2022] Albert Benveniste, Benoît Caillaud, and Mathias Malandain present talk titled Exact Structural Analysis of Multimode Modelica Models
    Date & Time: Tuesday, April 5, 2022; 11:00 AM EDT
    Speaker: Albert Benveniste, Benoît Caillaud, and Mathias Malandain, Inria
    MERL Host: Scott A. Bortoff
    Research Areas: Dynamical Systems, Multi-Physical Modeling
    Abstract
    • Since its 3.3 release, Modelica offers the possibility to specify models of dynamical systems with multiple modes having different DAE-based dynamics. However, the handling of such models by the current Modelica tools is not satisfactory, with mathematically sound models yielding exceptions at runtime. In our introduction, will briefly explain why and when the approximate structural analysis implemented in current Modelica tools leads to such errors. Then we will present our multimode Pryce Sigma-method for index reduction, in which the mode-dependent Sigma-matrix is represented in a dual form, by attaching, to every valuation of the sigma_ij entry of the Sigma matrix, the predicate characterizing the set of modes in which sigma_ij takes this value. We will illustrate this multimode analysis on example, by using our IsamDAE tool. In a second part, we will complement this multimode DAE structural analysis by a new structural analysis of mode changes (and, more generally, transient modes holding for zero time). Also, mode changes often give raise to impulsive behaviors: we will present a compile-time analysis identifying such behaviors. Our structural analysis of mode changes deeply relies on nonstandard analysis, which is a mathematical framework in which infinitesimals and infinities are first class citizens.
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  •  NEWS    Dr. Benosman is invited to give the mini-course in control theory at the 2022 edition of the Benelux Meeting on Systems and Control
    Date: July 5, 2022 - July 7, 2022
    MERL Contact: Mouhacine Benosman
    Research Areas: Control, Data Analytics, Dynamical Systems
    Brief
    • The Benelux meeting is an annual conference gathering of the scientific community of Belgium, the Netherlands, and Luxemburg around systems and control. It is especially intended for PhD researchers and a number of activities are dedicated to them, including plenary talks and a mini-course.

      Dr. Benosman has been invited to give the mini-course of the 2022 edition of the conference. This course, entitled 'A hybrid approach to control: classical control theory meets machine learning theory', will be centered around the topic of safe and robust machine learning-based control.
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  •  TALK    [MERL Seminar Series 2021] Harnessing machine learning to build better Earth system models for climate projection
    Date & Time: Tuesday, December 14, 2021; 1:00 PM EST
    Speaker: Prof. Chris Fletcher, University of Waterloo
    MERL Host: Ankush Chakrabarty
    Research Areas: Dynamical Systems, Machine Learning, Multi-Physical Modeling
    Abstract
    • Decision-making and adaptation to climate change requires quantitative projections of the physical climate system and an accurate understanding of the uncertainty in those projections. Earth system models (ESMs), which solve the Navier-Stokes equations on the sphere, are the only tool that climate scientists have to make projections forward into climate states that have not been observed in the historical data record. Yet, ESMs are incredibly complex and expensive codes and contain many poorly constrained physical parameters—for processes such as clouds and convection—that must be calibrated against observations. In this talk, I will describe research from my group that uses ensembles of ESM simulations to train statistical models that learn the behavior and sensitivities of the ESM. Once trained and validated the statistical models are essentially free to run, which allows climate modelling centers to make more efficient use of precious compute cycles. The aim is to improve the quality of future climate projections, by producing better calibrated ESMs, and to improve the quantification of the uncertainties, by better sampling the equifinality of climate states.
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  •  EVENT    Prof. Melanie Zeilinger of ETH to give keynote at MERL's Virtual Open House
    Date & Time: Thursday, December 9, 2021; 1:00pm - 5:30pm EST
    Location: Virtual Event
    Speaker: Prof. Melanie Zeilinger, ETH
    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, Digital Video, Human-Computer Interaction, Information Security
    Brief
    • MERL is excited to announce the second keynote speaker for our Virtual Open House 2021:
      Prof. Melanie Zeilinger from ETH .

      Our virtual open house will take place on December 9, 2021, 1:00pm - 5:30pm (EST).

      Join us to learn more about who we are, what we do, and discuss our internship and employment opportunities. Prof. Zeilinger's talk is scheduled for 3:15pm - 3:45pm (EST).

      Registration: https://mailchi.mp/merl/merlvoh2021

      Keynote Title: Control Meets Learning - On Performance, Safety and User Interaction

      Abstract: With increasing sensing and communication capabilities, physical systems today are becoming one of the largest generators of data, making learning a central component of autonomous control systems. While this paradigm shift offers tremendous opportunities to address new levels of system complexity, variability and user interaction, it also raises fundamental questions of learning in a closed-loop dynamical control system. In this talk, I will present some of our recent results showing how even safety-critical systems can leverage the potential of data. I will first briefly present concepts for using learning for automatic controller design and for a new safety framework that can equip any learning-based controller with safety guarantees. The second part will then discuss how expert and user information can be utilized to optimize system performance, where I will particularly highlight an approach developed together with MERL for personalizing the motion planning in autonomous driving to the individual driving style of a passenger.
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  •  EVENT    Prof. Ashok Veeraraghavan of Rice University to give keynote at MERL's Virtual Open House
    Date & Time: Thursday, December 9, 2021; 1:00pm - 5:30pm EST
    Location: Virtual Event
    Speaker: Prof. Ashok Veeraraghavan, Rice University
    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, Digital Video, Human-Computer Interaction, Information Security
    Brief
    • MERL is excited to announce the first keynote speaker for our Virtual Open House 2021:
      Prof. Ashok Veeraraghavan from Rice University.

      Our virtual open house will take place on December 9, 2021, 1:00pm - 5:30pm (EST).

      Join us to learn more about who we are, what we do, and discuss our internship and employment opportunities. Prof. Veeraraghavan's talk is scheduled for 1:15pm - 1:45pm (EST).

      Registration: https://mailchi.mp/merl/merlvoh2021

      Keynote Title: Computational Imaging: Beyond the limits imposed by lenses.

      Abstract: The lens has long been a central element of cameras, since its early use in the mid-nineteenth century by Niepce, Talbot, and Daguerre. The role of the lens, from the Daguerrotype to modern digital cameras, is to refract light to achieve a one-to-one mapping between a point in the scene and a point on the sensor. This effect enables the sensor to compute a particular two-dimensional (2D) integral of the incident 4D light-field. We propose a radical departure from this practice and the many limitations it imposes. In the talk we focus on two inter-related research projects that attempt to go beyond lens-based imaging.

      First, we discuss our lab’s recent efforts to build flat, extremely thin imaging devices by replacing the lens in a conventional camera with an amplitude mask and computational reconstruction algorithms. These lensless cameras, called FlatCams can be less than a millimeter in thickness and enable applications where size, weight, thickness or cost are the driving factors. Second, we discuss high-resolution, long-distance imaging using Fourier Ptychography, where the need for a large aperture aberration corrected lens is replaced by a camera array and associated phase retrieval algorithms resulting again in order of magnitude reductions in size, weight and cost. Finally, I will spend a few minutes discussing how the wholistic computational imaging approach can be used to create ultra-high-resolution wavefront sensors.
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  •  TALK    [MERL Seminar Series 2021] Prof. Thomas Schön presents talk at MERL entitled Deep probabilistic regression
    Date & Time: Tuesday, November 16, 2021; 11:00 AM EST
    Speaker: Thomas Schön, Uppsala University
    MERL Host: Karl Berntorp
    Research Areas: Dynamical Systems, Machine Learning
    Abstract
    • While deep learning-based classification is generally addressed using standardized approaches, this is really not the case when it comes to the study of regression problems. There are currently several different approaches used for regression and there is still room for innovation. We have developed a general deep regression method with a clear probabilistic interpretation. The basic building block in our construction is an energy-based model of the conditional output density p(y|x), where we use a deep neural network to predict the un-normalized density from input-output pairs (x, y). Such a construction is also commonly referred to as an implicit representation. The resulting learning problem is challenging and we offer some insights on how to deal with it. We show good performance on several computer vision regression tasks, system identification problems and 3D object detection using laser data.
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  •  EVENT    MERL Virtual Open House 2021
    Date & Time: Thursday, December 9, 2021; 100pm-5:30pm (EST)
    Location: Virtual Event
    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, Digital Video, Human-Computer Interaction, Information Security
    Brief
    • Mitsubishi Electric Research Laboratories cordially invites you to join our Virtual Open House, on December 9, 2021, 1:00pm - 5:30pm (EST).

      The event will feature keynotes, live sessions, research area booths, and time for open interactions with our researchers. Join us to learn more about who we are, what we do, and discuss our internship and employment opportunities.

      Registration: https://mailchi.mp/merl/merlvoh2021
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  •  NEWS    Diego Romeres appointed as Associate Editor at ICRA 2022.
    Date: September 17, 2021 - October 31, 2021
    MERL Contact: Diego Romeres
    Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Optimization, Robotics
    Brief
    • Diego Romeres, a Principal Research Scientist in MERL's Data Analytics group, is serving as an Associate Editor (AE) for the IEEE International Conference on Robotics and Automation (ICRA) 2022.
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  •  NEWS    Mouhacine Benosman has been invited to speak at the data-centric engineering summit held at the Alan Turing Institute
    Date: September 22, 2021
    Where: The Alan Turing Institute
    MERL Contact: Mouhacine Benosman
    Research Area: Dynamical Systems
    Brief
    • Mouhacine Benosman will give a talk about merging physical models with data-driven and machine learning methods for real-world application. The talk will include results about data-driven auto-tuning for feedback controllers with application to power amplifiers, extremum seeking and Gaussian processes for reduction/estimation of fluid dynamics models with application to indoor airflow modeling, and safe reinforcement learning for safety-critical and Sim2Real applications.
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  •  NEWS    Anthony Vetro delivers keynote on robotic manipulation at inaugural IEEE Conference on Autonomous Systems
    Date: August 12, 2021
    MERL Contact: Anthony Vetro
    Research Areas: Artificial Intelligence, Computer Vision, Control, Dynamical Systems, Machine Learning, Optimization, Robotics
    Brief
    • Anthony Vetro gave a keynote at the inaugural IEEE Conference on Autonomous Systems (ICAS), which was held virtually from August 11-13, 2021. The talk focused on challenges and recent progress in the area of robotic manipulation. The conference is sponsored by IEEE Signal Processing Society (SPS) through the SPS Autonomous Systems Initiative.

      Abstract: Human-level manipulation continues to be beyond the capabilities of today’s robotic systems. Not only do current industrial robots require significant time to program a specific task, but they lack the flexibility to generalize to other tasks and be robust to changes in the environment. While collaborative robots help to reduce programming effort and improve the user interface, they still fall short on generalization and robustness. This talk will highlight recent advances in a number of key areas to improve the manipulation capabilities of autonomous robots, including methods to accurately model the dynamics of the robot and contact forces, sensors and signal processing algorithms to provide improved perception, optimization-based decision-making and control techniques, as well as new methods of interactivity to accelerate and enhance robot learning.
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  •  NEWS    MERL researcher co-edits a special issue on Extremum Seeking Control in the International Journal of Adaptive Control and Signal Processing
    Date: July 14, 2021
    MERL Contact: Mouhacine Benosman
    Research Area: Dynamical Systems
    Brief
    • Mouhacine Benosman co-edits a special issue on Extremum Seeking Control in the International Journal of Adaptive Control and Signal Processing.

      The issue contains some of the newest theoretical developments on continuous-time optimizers, known as extremum seekers, with applications ranging from microalgae cultivation control to heating and ventilation systems optimization.

      The special issue is available at:
      https://onlinelibrary.wiley.com/toc/10991115/current
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  •  NEWS    Rien Quirynen and Karl Berntorp to deliver keynote at IFAC NMPC 2021 Conference
    Date: July 12, 2021
    MERL Contacts: Karl Berntorp; Rien Quirynen
    Research Areas: Control, Dynamical Systems, Optimization
    Brief
    • MERL researcher Rien Quirynen will present work in collaboration with Karl Berntorp on "Uncertainty Propagation by Linear Regression Kalman Filters for Stochastic Nonlinear MPC" as a keynote speaker at the 7th IFAC Conference on Nonlinear Model Predictive Control 2021 on July, 12th. The paper is 1 out of 5 keynote presentations chosen among more than 50 accepted papers at the conference. An abstract of the talk can be found in the link below.
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  •  NEWS    Karl Berntorp gave an invited lecture at University of Houston
    Date: April 22, 2021
    Where: Houston, Texas
    MERL Contact: Karl Berntorp
    Research Areas: Control, Dynamical Systems, Robotics, Signal Processing
    Brief
    • The invited seminar "System Design, Planning, and Control for Autonomous Driving" was part of the Distinguished Seminar series at the Department of Mechanical Engineering at the University of Houston, Houston, Tx. The invited lecture described MERL research related to the different system components involved in autonomous driving, with particular focus on motion-planning and predictive-control methods.
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  •  NEWS    Karl Berntorp gave an invited lecture at the Department of Electrical Engineering at Linköping University
    Date: April 6, 2021
    Where: Linköping University, Sweden
    MERL Contact: Karl Berntorp
    Research Areas: Control, Dynamical Systems, Robotics
    Brief
    • MERL researcher Karl Berntorp was invited to give a lecture in the ELLIIT PhD course "Motion Planning and Control" at the Division of Vehicular Systems, Department of Electrical Engineering, Linköping University. The course is open for Ph.D. students as well as senior undergraduate students, and covers both fundamental algorithms and state-of-the-art methods for motion planning and control. The invited lecture described MERL research on the use of invariant sets for safe motion planning and control, with application to autonomous vehicles.
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  •  NEWS    Stefano Di Cairano joins the Editorial Board of IEEE Transactions on Intelligent Vehicles
    Date: March 7, 2021
    MERL Contact: Stefano Di Cairano
    Research Areas: Control, Dynamical Systems, Robotics
    Brief
    • Stefano Di Cairano has joined the Editorial Board of the IEEE Transactions on Intelligent Vehicles (T-IV) as an Associate Editor. The IEEE T-IV publishes peer-reviewed articles in the area of intelligent vehicles in a roadway environment, and in particular in automated vehicles. While primarily led by the IEEE ITS Society, IEEE T-IV is an IEEE multi-society journal.
      As Associate Editor Stefano will be responsible for the review process of some of the papers submitted to T-IV and will work with the Editorial Board to monitor the status and continuously strengthen the journal.
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  •  EVENT    MERL Virtual Open House 2020
    Date & Time: Wednesday, December 9, 2020; 1:00-5:00PM EST
    Location: Virtual
    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
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  •  NEWS    Karl Berntorp gave an invited lecture at the Department of Electrical Engineering at Linkoping University
    Date: October 8, 2020
    Where: Linkoping University
    MERL Contact: Karl Berntorp
    Research Areas: Control, Dynamical Systems, Robotics, Signal Processing
    Brief
    • MERL researcher Karl Berntorp was invited to give a lecture in the class "Autonomous vehicles – planning, control, and learning systems" at the Division of Vehicular Systems, Department of Electrical Engineering, Linkoping University. The course is for the engineering-program students at Linkoping University and gives a basic understanding of the available models, methods, and software libraries to work on autonomous vehicles, with particular focus on motion-planning and control methods. The invited lecture described the different system components and design of motion planning and predictive control methods targeted to autonomous driving.
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  •  NEWS    M. Benosman will give an invited talk at the Industrial and Applied Mathematics (SIAM) Student Chapter at Virginia Tech.
    Date: October 9, 2020
    MERL Contact: Mouhacine Benosman
    Research Area: Dynamical Systems
    Brief
    • M. Benosman will give an invited talk at the SIAM student chapter at Virginia Tech. to speak about several applications of mathematics to industrial problems.

      The Society for Industrial and Applied Mathematics (SIAM) Student Chapter at Virginia Tech will host a number of talks by mathematicians working in industry. The speakers will describe the path they followed to reach this point in their careers and also tell us more about their industry and how mathematics is used.
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  •  NEWS    Saleh Nabi gave an invited talk at the Department of Mechanical Engineering at Rice University
    Date: September 30, 2020
    Where: Rice University
    MERL Contact: Saleh Nabi
    Research Areas: Dynamical Systems, Optimization
    Brief
    • MERL researcher Dr. S. Nabi was invited to give a talk on the state-of-the-art methods for airflow optimization and control at Rice University. Several industrial applications to buoyancy-driven flows in the built environment, atmospheric flows, and prevention of transmission of COVID-19 were discussed. Furthermore, some novel advances on data-driven fluid mechanics for industrial applications were covered.
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  •  NEWS    MERL Researcher Ankush Chakrabarty organized a special session on data-driven control at IEEE CCTA 2020
    Date: August 25, 2020
    MERL Contact: Ankush Chakrabarty
    Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Robotics
    Brief
    • Ankush Chakrabarty co-organized an invited session on “Data-Driven Control For Industrial Applications” at the IEEE Conference on Control Technology and Applications with Shahin Shahrampour (Asst. Prof., Texas A&M). Talks covered topics including reinforcement learning for aerospace systems, constrained reinforcement learning for motors, deep Q learning for traffic systems and participants included speakers from Stanford University, North Carolina State University, Texas A&M, Oklahoma State University, University of Science and Technology at Beijing, and TU Delft.

      MERL presented research (Chakrabarty, Danielson, Wang) on constraint-enforcing output-tracking with approximate dynamic programming for servomotor systems.
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  •  NEWS    MERL researchers presenting three papers at ICML 2020
    Date: July 12, 2020 - July 18, 2020
    Where: Vienna, Austria (virtual this year)
    MERL Contacts: Mouhacine Benosman; Anoop Cherian; Devesh K. Jha; Daniel N. Nikovski
    Research Areas: Artificial Intelligence, Computer Vision, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Robotics
    Brief
    • MERL researchers are presenting three papers at the International Conference on Machine Learning (ICML 2020), which is virtually held this year from 12-18th July. ICML is one of the top-tier conferences in machine learning with an acceptance rate of 22%. The MERL papers are:

      1) "Finite-time convergence in Continuous-Time Optimization" by Orlando Romero and Mouhacine Benosman.

      2) "Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?" by Kei Ota, Tomoaki Oiki, Devesh Jha, Toshisada Mariyama, and Daniel Nikovski.

      3) "Representation Learning Using Adversarially-Contrastive Optimal Transport" by Anoop Cherian and Shuchin Aeron.
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