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.

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Researchers
Awards
- AWARD MERL Wins Awards at NeurIPS LLM Privacy Challenge
  Date: December 15, 2024
  Awarded to: Jing Liu, Ye Wang, Toshiaki Koike-Akino, Tsunato Nakai, Kento Oonishi, Takuya Higashi
  MERL Contacts: Toshiaki Koike-Akino; Jing Liu; Ye Wang
  Research Areas: Artificial Intelligence, Machine Learning, Information Security
  Brief
  - The Mitsubishi Electric Privacy Enhancing Technologies (MEL-PETs) team, consisting of a collaboration of MERL and Mitsubishi Electric researchers, won awards at the NeurIPS 2024 Large Language Model (LLM) Privacy Challenge. In the Blue Team track of the challenge, we won the 3rd Place Award, and in the Red Team track, we won the Special Award for Practical Attack.
- AWARD University of Padua and MERL team wins the AI Olympics with RealAIGym competition at IROS24
  Date: October 17, 2024
  Awarded to: Niccolò Turcato, Alberto Dalla Libera, Giulio Giacomuzzo, Ruggero Carli, Diego Romeres
  MERL Contact: Diego Romeres
  Research Areas: Artificial Intelligence, Dynamical Systems, Machine Learning, Robotics
  Brief
  - The team composed of the control group at the University of Padua and MERL's Optimization and Robotic team ranked 1st out of the 4 finalist teams that arrived to the 2nd AI Olympics with RealAIGym competition at IROS 24, which focused on control of under-actuated robots. The team was composed by Niccolò Turcato, Alberto Dalla Libera, Giulio Giacomuzzo, Ruggero Carli and Diego Romeres. The competition was organized by the German Research Center for Artificial Intelligence (DFKI), Technical University of Darmstadt and Chalmers University of Technology.
    
    The competition and award ceremony was hosted by IEEE International Conference on Intelligent Robots and Systems (IROS) on October 17, 2024 in Abu Dhabi, UAE. Diego Romeres presented the team's method, based on a model-based reinforcement learning algorithm called MC-PILCO.
- AWARD MERL team wins the Listener Acoustic Personalisation (LAP) 2024 Challenge
  Date: August 29, 2024
  Awarded to: Yoshiki Masuyama, Gordon Wichern, Francois G. Germain, Christopher Ick, and Jonathan Le Roux
  MERL Contacts: François Germain; Jonathan Le Roux; Gordon Wichern; Yoshiki Masuyama
  Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
  Brief
  - MERL's Speech & Audio team ranked 1st out of 7 teams in Task 2 of the 1st SONICOM Listener Acoustic Personalisation (LAP) Challenge, which focused on "Spatial upsampling for obtaining a high-spatial-resolution HRTF from a very low number of directions". The team was led by Yoshiki Masuyama, and also included Gordon Wichern, Francois Germain, MERL intern Christopher Ick, and Jonathan Le Roux.
    
    The LAP Challenge workshop and award ceremony was hosted by the 32nd European Signal Processing Conference (EUSIPCO 24) on August 29, 2024 in Lyon, France. Yoshiki Masuyama presented the team's method, "Retrieval-Augmented Neural Field for HRTF Upsampling and Personalization", and received the award from Prof. Michele Geronazzo (University of Padova, IT, and Imperial College London, UK), Chair of the Challenge's Organizing Committee.
    
    The LAP challenge aims to explore challenges in the field of personalized spatial audio, with the first edition focusing on the spatial upsampling and interpolation of head-related transfer functions (HRTFs). HRTFs with dense spatial grids are required for immersive audio experiences, but their recording is time-consuming. Although HRTF spatial upsampling has recently shown remarkable progress with approaches involving neural fields, HRTF estimation accuracy remains limited when upsampling from only a few measured directions, e.g., 3 or 5 measurements. The MERL team tackled this problem by proposing a retrieval-augmented neural field (RANF). RANF retrieves a subject whose HRTFs are close to those of the target subject at the measured directions from a library of subjects. The HRTF of the retrieved subject at the target direction is fed into the neural field in addition to the desired sound source direction. The team also developed a neural network architecture that can handle an arbitrary number of retrieved subjects, inspired by a multi-channel processing technique called transform-average-concatenate.
See All Awards for Machine Learning
News & Events
- NEWS MERL researchers present 7 papers at CDC 2024
  Date: December 16, 2024 - December 19, 2024
  Where: Milan, Italy
  MERL Contacts: Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; James Queeney; Abraham P. Vinod; Avishai Weiss; Gordon Wichern
  Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics
  Brief
  - MERL researchers presented 7 papers at the recently concluded Conference on Decision and Control (CDC) 2024 in Milan, Italy. The papers covered a wide range of topics including safety shielding for stochastic model predictive control, reinforcement learning using expert observations, physics-constrained meta learning for positioning, variational-Bayes Kalman filtering, Bayesian measurement masks for GNSS positioning, divert-feasible lunar landing, and centering and stochastic control using constrained zonotopes.
    
    As a sponsor of the conference, MERL maintained a booth for open discussions with researchers and students, and hosted a special session to discuss highlights of MERL research and work philosophy.
    
    In addition, Ankush Chakrabarty (Principal Research Scientist, Multiphysical Systems Team) was an invited speaker in the pre-conference Workshop on "Learning Dynamics From Data" where he gave a talk on few-shot meta-learning for black-box identification using data from similar systems.
- NEWS MERL Researchers to Present 2 Conference and 11 Workshop Papers at NeurIPS 2024
  Date: December 10, 2024 - December 15, 2024
  Where: Advances in Neural Processing Systems (NeurIPS)
  MERL Contacts: Petros T. Boufounos; Matthew Brand; Ankush Chakrabarty; Anoop Cherian; François Germain; Toshiaki Koike-Akino; Christopher R. Laughman; Jonathan Le Roux; Jing Liu; Suhas Lohit; Tim K. Marks; Yoshiki Masuyama; Kieran Parsons; Kuan-Chuan Peng; Diego Romeres; Pu (Perry) Wang; Ye Wang; Gordon Wichern
  Research Areas: Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Human-Computer Interaction, Information Security
  Brief
  - MERL researchers will attend and present the following papers at the 2024 Advances in Neural Processing Systems (NeurIPS) Conference and Workshops.
    
    1. "RETR: Multi-View Radar Detection Transformer for Indoor Perception" by Ryoma Yataka (Mitsubishi Electric), Adriano Cardace (Bologna University), Perry Wang (Mitsubishi Electric Research Laboratories), Petros Boufounos (Mitsubishi Electric Research Laboratories), Ryuhei Takahashi (Mitsubishi Electric). Main Conference. https://neurips.cc/virtual/2024/poster/95530
    
    2. "Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads" by Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Joanna Matthiesen (Math Kangaroo USA), Kevin Smith (Massachusetts Institute of Technology), Josh Tenenbaum (Massachusetts Institute of Technology). Main Conference, Datasets and Benchmarks track. https://neurips.cc/virtual/2024/poster/97639
    
    3. "Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models The Answer?" by Young-Jin Park (Massachusetts Institute of Technology), Jing Liu (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Gordon Wichern (Mitsubishi Electric Research Laboratories), Navid Azizan (Massachusetts Institute of Technology), Christopher R. Laughman (Mitsubishi Electric Research Laboratories), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories). Time Series in the Age of Large Models Workshop.
    
    4. "Forget to Flourish: Leveraging Model-Unlearning on Pretrained Language Models for Privacy Leakage" by Md Rafi Ur Rashid (Penn State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Shagufta Mehnaz (Penn State University), Ye Wang (Mitsubishi Electric Research Laboratories). Workshop on Red Teaming GenAI: What Can We Learn from Adversaries?
    
    5. "Spatially-Aware Losses for Enhanced Neural Acoustic Fields" by Christopher Ick (New York University), Gordon Wichern (Mitsubishi Electric Research Laboratories), Yoshiki Masuyama (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Jonathan Le Roux (Mitsubishi Electric Research Laboratories). Audio Imagination Workshop.
    
    6. "FV-NeRV: Neural Compression for Free Viewpoint Videos" by Sorachi Kato (Osaka University), Takuya Fujihashi (Osaka University), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Takashi Watanabe (Osaka University). Machine Learning and Compression Workshop.
    
    7. "GPT Sonography: Hand Gesture Decoding from Forearm Ultrasound Images via VLM" by Keshav Bimbraw (Worcester Polytechnic Institute), Ye Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). AIM-FM: Advancements In Medical Foundation Models: Explainability, Robustness, Security, and Beyond Workshop.
    
    8. "Smoothed Embeddings for Robust Language Models" by Hase Ryo (Mitsubishi Electric), Md Rafi Ur Rashid (Penn State University), Ashley Lewis (Ohio State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kieran Parsons (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories). Safe Generative AI Workshop.
    
    9. "Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation" by Xiangyu Chen (University of Kansas), Ye Wang (Mitsubishi Electric Research Laboratories), Matthew Brand (Mitsubishi Electric Research Laboratories), Pu Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). Workshop on Adaptive Foundation Models.
    
    10. "Preference-based Multi-Objective Bayesian Optimization with Gradients" by Joshua Hang Sai Ip (University of California Berkeley), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Ali Mesbah (University of California Berkeley), Diego Romeres (Mitsubishi Electric Research Laboratories). Workshop on Bayesian Decision-Making and Uncertainty. Lightning talk spotlight.
    
    11. "TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensions with Trust-Region-based Bayesian Novelty Search" by Wei-Ting Tang (Ohio State University), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Joel A. Paulson (Ohio State University). Workshop on Bayesian Decision-Making and Uncertainty.
    
    12. "MEL-PETs Joint-Context Attack for the NeurIPS 2024 LLM Privacy Challenge Red Team Track" by Ye Wang (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Special Award for Practical Attack.
    
    13. "MEL-PETs Defense for the NeurIPS 2024 LLM Privacy Challenge Blue Team Track" by Jing Liu (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Won 3rd Place Award.
    
    MERL members also contributed to the organization of the Multimodal Algorithmic Reasoning (MAR) Workshop (https://marworkshop.github.io/neurips24/). Organizers: Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Honglu Zhou (Salesforce Research), Kevin Smith (Massachusetts Institute of Technology), Tim K. Marks (Mitsubishi Electric Research Laboratories), Juan Carlos Niebles (Salesforce AI Research), Petar Veličković (Google DeepMind).
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Research Highlights
Internships
- EA0070: Internship - Multi-modal sensor fusion
  
  MERL is looking for a self-motivated intern to work on multi-modal sensor fusion for health condition monitoring and predictive maintenance of motor drive systems. The ideal candidate would be a Ph.D. candidate in electrical engineering or computer science with solid research background in signal processing and machine learning. Experience in motor drive system is a plus. The intern is expected to collaborate with MERL researchers to collect data, explore multi-modal data relationship, and prepare manuscripts for publications. The total duration is anticipated to be 3 months and the start date is flexible.
  Required Specific Experience
  - Experience with multi-modal sensor fusion.
- CV0078: Internship - Audio-Visual Learning with Limited Labeled Data
  
  MERL is looking for a highly motivated intern to work on an original research project on multimodal learning, such as audio-visual learning, using limited labeled data. A strong background in computer vision and deep learning is required. Experience in audio-visual (multimodal) learning, weakly/self-supervised learning, continual learning, and large (vision-) language models is an added plus and will be valued. The successful candidate is expected to have published at least one paper in a top-tier computer vision or machine learning venue, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS or AAAI, and possess solid programming skills in Python and popular deep learning frameworks such as Pytorch. The intern will collaborate with MERL researchers to develop and implement novel algorithms and prepare manuscripts for scientific publications. Successful applicants are typically graduate students on a Ph.D. track or recent Ph.D. graduates. Duration and start date are flexible, but the internship is expected to last for at least 3 months.
  Required Specific Experience
  - Prior publications in top-tier computer vision and/or machine learning venues, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS or AAAI.
  - Knowledge of the latest self-supervised and weakly-supervised learning techniques.
  - Experience with Large (Vision-) Language Models.
  - Proficiency in scripting languages, such as Python, and deep learning frameworks such as PyTorch or Tensorflow.
- CV0100: Internship - Simulation for Human-Robot Interaction
  
  MERL is looking for a self-motivated intern to develop a simulation platform to train vision-and-language models for dynamic human-robot interaction. The ideal intern must have a strong background in computer graphics, computer vision, and machine learning, as well as experience in using the latest graphics simulation toolboxes and physics engines. Working knowledge of recent multimodal generative AI methods is desired. The intern is expected to collaborate with researchers in the computer vision team at MERL to develop algorithms and prepare manuscripts for scientific publications.
  Required Specific Experience
  - Experience in designing novel realistic 3D interactive scenes for robot learning
  - Experience with extending vision-based embodied AI simulators
  - Strong foundations in machine learning and programming
  - Foundations in optimization, specifically scheduling algorithms, would be a strong plus.
  - Strong track record of publications in top-tier computer vision and machine learning venues (such as CVPR, NeurIPS, etc.)
  - Must be enrolled in a graduate program, ideally towards a Ph.D.
See All Internships for Machine Learning
Openings
See All Openings at MERL
Recent Publications
- Vinod, A.P., Safaoui, S., Summers, T., Yoshikawa, N., Di Cairano, S., "Decentralized, Safe, Multi-agent Motion Planning for Drones Under Uncertainty via Filtered Reinforcement Learning", IEEE Transactions on Control Systems Technology, DOI: 10.1109/TCST.2024.3433229, Vol. 32, No. 6, pp. 2492-2499, January 2025.
  BibTeX TR2024-136 PDF
  - @article{Vinod2025jan,
  - author = {Vinod, Abraham P. and Safaoui, Sleiman and Summers, Tyler and Yoshikawa, Nobuyuki and Di Cairano, Stefano}},
  - title = {Decentralized, Safe, Multi-agent Motion Planning for Drones Under Uncertainty via Filtered Reinforcement Learning},
  - journal = {IEEE Transactions on Control Systems Technology},
  - year = 2025,
  - volume = 32,
  - number = 6,
  - pages = {2492--2499},
  - month = jan,
  - doi = {10.1109/TCST.2024.3433229},
  - url = {https://www.merl.com/publications/TR2024-136}
  - }
- Koo, J., Wichern, G., Germain, F.G., Khurana, S., Le Roux, J., "SMITIN: Self-Monitored Inference-Time INtervention for Generative Music Transformers", IEEE Open Journal of Signal Processing, January 2025.
  BibTeX TR2025-012 PDF
  - @article{Koo2025jan,
  - author = {Koo, Junghyun and Wichern, Gordon and Germain, François G and Khurana, Sameer and Le Roux, Jonathan}},
  - title = {SMITIN: Self-Monitored Inference-Time INtervention for Generative Music Transformers},
  - journal = {IEEE Open Journal of Signal Processing},
  - year = 2025,
  - month = jan,
  - url = {https://www.merl.com/publications/TR2025-012}
  - }
- Lohit, S., Marks, T.K., "Rotation-Equivariant Neural Networks for Cloud Removal from Satellite Images", Asilomar Conference on Signals, Systems, and Computers (ACSSC), January 2025.
  BibTeX TR2025-009 PDF
  - @inproceedings{Lohit2025jan,
  - author = {Lohit, Suhas and Marks, Tim K.}},
  - title = {Rotation-Equivariant Neural Networks for Cloud Removal from Satellite Images},
  - booktitle = {Asilomar Conference on Signals, Systems, and Computers (ACSSC)},
  - year = 2025,
  - month = jan,
  - url = {https://www.merl.com/publications/TR2025-009}
  - }
- He, Y., Shin, S., Cherian, A., Trigoni, N., Markham, A., "SoundLoc3D: Invisible 3D Sound Source Localization and Classification Using a Multimodal RGB-D Acoustic Camera", IEEE Winter Conference on Applications of Computer Vision (WACV), December 2024.
  BibTeX TR2025-003 PDF
  - @inproceedings{He2024dec2,
  - author = {He, Yuhang and Shin, Sangyun and Cherian, Anoop and Trigoni, Niki and Markham, Andrew}},
  - title = {SoundLoc3D: Invisible 3D Sound Source Localization and Classification Using a Multimodal RGB-D Acoustic Camera},
  - booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
  - year = 2024,
  - month = dec,
  - url = {https://www.merl.com/publications/TR2025-003}
  - }
- Zhang, J., Zhang, F., Rodriguez, C., Ben-Shabat, I., Cherian, A., Gould, S., "Temporally Grounding Instructional Diagrams in Unconstrained Videos", IEEE Winter Conference on Applications of Computer Vision (WACV), December 2024.
  BibTeX TR2025-002 PDF
  - @inproceedings{Zhang2024dec,
  - author = {Zhang, Jiahao and Zhang, Frederic and Rodriguez, Cristian and Ben-Shabat, Itzik and Cherian, Anoop and Gould, Stephen}},
  - title = {Temporally Grounding Instructional Diagrams in Unconstrained Videos},
  - booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
  - year = 2024,
  - month = dec,
  - url = {https://www.merl.com/publications/TR2025-002}
  - }
- Chakrabarty, A., Deshpande, V.M., Wichern, G., Berntorp, K., "Physics-Constrained Meta-Learning for Online Adaptation and Estimation in Positioning Applications", IEEE Conference on Decision and Control (CDC), December 2024.
  BibTeX TR2024-180 PDF
  - @inproceedings{Chakrabarty2024dec,
  - author = {Chakrabarty, Ankush and Deshpande, Vedang M. and Wichern, Gordon and Berntorp, Karl}},
  - title = {Physics-Constrained Meta-Learning for Online Adaptation and Estimation in Positioning Applications},
  - booktitle = {IEEE Conference on Decision and Control (CDC)},
  - year = 2024,
  - month = dec,
  - url = {https://www.merl.com/publications/TR2024-180}
  - }
- Ozcan, E.C., Giammarino, V., Queeney, J., Paschalidis, I.C., "A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations", IEEE Conference on Decision and Control (CDC), December 2024.
  BibTeX TR2024-178 PDF
  - @inproceedings{Ozcan2024dec,
  - author = {Ozcan, Erhan Can and Giammarino, Vittorio and Queeney, James and Paschalidis, Ioannis Ch.}},
  - title = {A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations},
  - booktitle = {IEEE Conference on Decision and Control (CDC)},
  - year = 2024,
  - month = dec,
  - url = {https://www.merl.com/publications/TR2024-178}
  - }
- Bimbraw, K., Wang, Y., Liu, J., Koike-Akino, T., "GPT Sonograpy: Hand Gesture Decoding from Forearm Ultrasound Images via VLM", Advances in Neural Information Processing Systems (NeurIPS), December 2024.
  BibTeX TR2024-175 PDF Presentation
  - @inproceedings{Bimbraw2024dec,
  - author = {{Bimbraw, Keshav and Wang, Ye and Liu, Jing and Koike-Akino, Toshiaki}},
  - title = {GPT Sonograpy: Hand Gesture Decoding from Forearm Ultrasound Images via VLM},
  - booktitle = {Advancements In Medical Foundation Models: Explainability, Robustness, Security, and Beyond Workshop at Neural Information Processing Systems (NeurIPS)},
  - year = 2024,
  - month = dec,
  - url = {https://www.merl.com/publications/TR2024-175}
  - }
See All Publications for Machine Learning
Videos

Data-driven Spatial Classification using Multi-Arm Bandits for Monitoring with Robot Teams

[NeurIPS 2024] MEL-PETs Defense for the NeurIPS 2024 LLM Privacy Challenge Blue Team Track

[NeurIPS 2024] MEL-PETs Joint-Context Attack for the NeurIPS 2024 LLM Privacy Challenge Red Team Track

[NeurIPS 2024] Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads

[MERL Seminar Series Fall 2024] Audio for Object and Spatial Awareness

[IROS 2024] Few-shot Transparent Instance Segmentation for Bin Picking

[MERL Seminar Series Fall 2024] Tools from cognitive science to understand the behavior of large language models

[ECCV 2024] PS-NEUS: A Probability-guided Sampler for Neural Implicit Surface Rendering

[ECCV 2024] Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection

[CVPR 2024] SIRA: Scalable Inter-frame Relation and Association for Radar Perception

Decentralized, Safe, Multi-agent Motion Planning for Drones Under Uncertainty via Filtered Reinforcement Learning

[MERL Seminar Series Spring 2024] Neural Certificates and LLMs in Large-Scale Autonomy Design

[MERL Seminar Series Spring 2024] Decoding Hidden Worlds: Unprecedented Sensing and Connectivity for Climate, Robotics, & Smart Environments

Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-aware Spatio-Temporal Sampling

[MERL Seminar Series Spring 2024] Are Emergent Abilities of Large Language Models a Mirage?

MERL's Quantum AI Technology

[GLOBECOM 2022/VCC 2023 Tutorial] Post-Deep Learning Era: Emerging Quantum Machine Learning for Sensing and Communications (Session 2)

[GLOBECOM 2022/VCC 2023 Tutorial] Post-Deep Learning Era: Emerging Quantum Machine Learning for Sensing and Communications (Session I)

[MERL Seminar Series Fall 2023] Robust and Physics-informed machine learning for low light imaging

[MERL Seminar Series Fall 2023] Multiplicity in Machine Learning

Multi-level Reasoning for Robotic Assembly: From Sequence Inference to Contact Selection

[MERL Seminar Series Fall 2023] Visual Programming - A compositional approach to building General Purpose Vision Systems

[MERL Seminar Series Fall 2023] The Confluence of Vision, Language, and Robotics

[MERL Seminar Series Fall 2023] A Process Systems Engineering Perspective on Carbon Capture: Key Challenges and Opportunities

[TRO 2024] Safe Multiagent Motion Planning Under Uncertainty for Drones Using Filtered Reinforcement Learning

Are Deep Neural Networks SMARTer than Second Graders?

[CVPR 2023] EVAL: Explainable Video Anomaly Localization

[MERL Seminar Series Spring 2023] Learning and Dynamical Systems

[MERL Seminar Series Spring 2023] Investigating Multi-Agent Reinforcement Learning for Grid-Interactive Smart Communities using CityLearn

[MERL Seminar Series Spring 2023] Pitfalls and Opportunities in Interpretable Machine Learning

[MERL Seminar Series Spring 2023] Neural Implicit Flow

[MERL Seminar Series Spring 2023] Towards Complex Language in Partially Observed Environments

[MERL Seminar Series Spring 2022] Hybrid robotics and implicit learning

Toshiaki Koike-Akino Gives Seminar Talk at IEEE Boston Photonics

[MERL Seminar Series Spring 2022] RLMPC: An Ideal Combination of Formal Optimal Control and Reinforcement Learning?

[MERL Seminar Series Spring 2022] Self-Supervised Scene Representation Learning

[MERL Seminar Series Spring 2022] Learning Speech Representations with Multimodal Self-Supervision

[MERL Seminar Series Spring 2022] Extreme optics design as a large-scale optimization problem

HealthCam: A system for non-contact monitoring of vital signs

[MERL Seminar Series 2021] Harnessing machine learning to build better Earth system models for climate projection

[MERL Seminar Series 2021] Deep probabilistic regression

[MERL Seminar Series 2021] Learning to See by Moving: Self-supervising 3D scene representations for perception, control, and visual reasoning

Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models

Application of Deep Learning for Nanophotonic Device Design (Invited)

Towards Human-Level Learning of Complex Physical Puzzles

Tactile-RL for Insertion: Generalization to Objects of Unknown Geometry

Scene-Aware Interaction Technology

Action Detection Using A Deep Recurrent Neural Network

MERL Research on Autonomous Vehicles

Semantic Scene Labeling

Obstacle Detection

Deep Hierarchical Parsing for Semantic Segmentation

Global Local Face Upsampling Network
Software & Data Downloads

Software & Data Downloads

MERL is making Machine Learning software and data available to the research community:

Retrieval-Augmented Neural Field for HRTF Upsampling and Personalization (ranf-hrtf)
Radar dEtection TRansformer (RETR)
Meta-Learning State Space Models (MetaLIC)
MEL-PETs Joint-Context Attack for LLM Privacy Challenge (melpets-llmpc2024-red-team)
MEL-PETs Defense for LLM Privacy Challenge (melpets-llmpc2024-blue-team)
Explainable Video Anomaly Localization (EVAL)

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