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.
Quick Links
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Researchers
Toshiaki
Koike-Akino
Ye
Wang
Jonathan
Le Roux
Ankush
Chakrabarty
Anoop
Cherian
Gordon
Wichern
Tim K.
Marks
Michael J.
Jones
Philip V.
Orlik
Kieran
Parsons
Stefano
Di Cairano
Christopher R.
Laughman
Daniel N.
Nikovski
Devesh K.
Jha
Pu
(Perry)
WangDiego
Romeres
Chiori
Hori
Bingnan
Wang
Suhas
Lohit
Jing
Liu
Yebin
Wang
Hassan
Mansour
Matthew
Brand
Petros T.
Boufounos
François
Germain
Moitreya
Chatterjee
Kuan-Chuan
Peng
Arvind
Raghunathan
Abraham P.
Vinod
Vedang M.
Deshpande
Jianlin
Guo
Siddarth
Jain
Scott A.
Bortoff
Pedro
Miraldo
Hongtao
Qiao
William S.
Yerazunis
Radu
Corcodel
Chungwei
Lin
Yoshiki
Masuyama
Saviz
Mowlavi
Dehong
Liu
Hongbo
Sun
Wataru
Tsujita
Joshua
Rapp
Ryo
Aihara
Yanting
Ma
Anthony
Vetro
Jinyun
Zhang
Wael H.
Ali
Purnanand
Elango
Abraham
Goldsmith
Alexander
Schperberg
Avishai
Weiss
Kenji
Inomata
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Awards
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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 SecurityBrief- 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.
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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, RoboticsBrief- 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.
- 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.
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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 & AudioBrief- 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.
- 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.
See All Awards for Machine Learning -
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News & Events
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NEWS Suhas Lohit presents invited talk at Boston Symmetry Day 2025 Date: March 31, 2025
Where: Northeastern University, Boston, MA
MERL Contact: Suhas Lohit
Research Areas: Artificial Intelligence, Computer Vision, Machine LearningBrief- MERL researcher Suhas Lohit was an invited speaker at Boston Symmetry Day, held at Northeastern University. Boston Symmetry Day, an annual workshop organized by researchers at MIT and Northeastern, brought together attendees interested in symmetry-informed machine learning and its applications. Suhas' talk, titled “Efficiency for Equivariance, and Efficiency through Equivariance” discussed recent MERL works that show how to build general and efficient equivariant neural networks, and how equivariance can be utilized in self-supervised learning to yield improved 3D object detection. The abstract and slides can be found in the link below.
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EVENT MERL Contributes to ICASSP 2025 Date: Sunday, April 6, 2025 - Friday, April 11, 2025
Location: Hyderabad, India
MERL Contacts: Wael H. Ali; Petros T. Boufounos; Radu Corcodel; François Germain; Chiori Hori; Siddarth Jain; Devesh K. Jha; Toshiaki Koike-Akino; Jonathan Le Roux; Yanting Ma; Hassan Mansour; Yoshiki Masuyama; Joshua Rapp; Diego Romeres; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern
Research Areas: Artificial Intelligence, Communications, Computational Sensing, Electronic and Photonic Devices, Machine Learning, Robotics, Signal Processing, Speech & AudioBrief- MERL has made numerous contributions to both the organization and technical program of ICASSP 2025, which is being held in Hyderabad, India from April 6-11, 2025.
Sponsorship
MERL is proud to be a Silver Patron of the conference and will participate in the student job fair on Thursday, April 10. Please join this session to learn more about employment opportunities at MERL, including openings for research scientists, post-docs, and interns.
MERL is pleased to be the sponsor of two IEEE Awards that will be presented at the conference. We congratulate Prof. Björn Erik Ottersten, the recipient of the 2025 IEEE Fourier Award for Signal Processing, and Prof. Shrikanth Narayanan, the recipient of the 2025 IEEE James L. Flanagan Speech and Audio Processing Award. Both awards will be presented in-person at ICASSP by Anthony Vetro, MERL President & CEO.
Technical Program
MERL is presenting 15 papers in the main conference on a wide range of topics including source separation, sound event detection, sound anomaly detection, speaker diarization, music generation, robot action generation from video, indoor airflow imaging, WiFi sensing, Doppler single-photon Lidar, optical coherence tomography, and radar imaging. Another paper on spatial audio will be presented at the Generative Data Augmentation for Real-World Signal Processing Applications (GenDA) Satellite Workshop.
MERL Researchers Petros Boufounos and Hassan Mansour will present a Tutorial on “Computational Methods in Radar Imaging” in the afternoon of Monday, April 7.
Petros Boufounos will also be giving an industry talk on Thursday April 10 at 12pm, on “A Physics-Informed Approach to Sensing".
About ICASSP
ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event has been attracting more than 4000 participants each year.
- MERL has made numerous contributions to both the organization and technical program of ICASSP 2025, which is being held in Hyderabad, India from April 6-11, 2025.
See All News & Events for Machine Learning -
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Research Highlights
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PS-NeuS: A Probability-guided Sampler for Neural Implicit Surface Rendering -
Quantum AI Technology -
TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models -
Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-Aware Spatio-Temporal Sampling -
Steered Diffusion -
Sustainable AI -
Edge-Assisted Internet of Vehicles for Smart Mobility -
Robust Machine Learning -
mmWave Beam-SNR Fingerprinting (mmBSF) -
Video Anomaly Detection -
Biosignal Processing for Human-Machine Interaction -
MERL Shopping Dataset -
Task-aware Unified Source Separation - Audio Examples
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Internships
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OR0115: Internship - Whole-body dexterous manipulation
MERL is looking for a highly motivated individual to work on whole-body dexterous manipulation. The research will develop robot motor skills for whole-body, dexterous manipulation using optimization and/or learning algorithms. The ideal candidate should have experience in either one or multiple of the following topics: Optimization Algorithms for contact systems, Reinforcement Learning, control through contacts, and Behavioral cloning. Senior PhD students in robotics and engineering with a focus on contact-rich manipulation are encouraged to apply. Prior experience working with physical robotic systems (and vision and tactile sensors) is required as results need to be implemented on a physical hardware. Good coding skills in Python ML libraries like PyTorch etc. and/or relevant Optimization packages is required. A successful internship will result in submission of results to a peer-reviewed robotics journal in collaboration with MERL researchers. The expected duration of internship is 4-5 months with start date in May/June 2025. This internship is preferred to be onsite at MERL.
Required Specific Experience
- Prior experience working with physical hardware system is required.
- Prior publication experience in robotics venues like ICRA,RSS, CoRL.
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ST0096: Internship - Multimodal Tracking and Imaging
MERL is seeking a motivated intern to assist in developing hardware and algorithms for multimodal imaging applications. The project involves integration of radar, camera, and depth sensors in a variety of sensing scenarios. The ideal candidate should have experience with FMCW radar and/or depth sensing, and be fluent in Python and scripting methods. Familiarity with optical tracking of humans and experience with hardware prototyping is desired. Good knowledge of computational imaging and/or radar imaging methods is a plus.
Required Specific Experience
- Experience with Python and Python Deep Learning Frameworks.
- Experience with FMCW radar and/or Depth Sensors.
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ST0105: Internship - Surrogate Modeling for Sound Propagation
MERL is seeking a motivated and qualified individual to work on fast surrogate models for sound emission and propagation from complex vibrating structures, with applications in HVAC noise reduction. The ideal candidate will be a PhD student in engineering or related fields with a solid background in frequency-domain acoustic modeling and numerical techniques for partial differential equations (PDEs). Preferred skills include knowledge of the boundary element method (BEM), data-driven modeling, and physics-informed machine learning. Publication of the results obtained during the internship is expected. The duration is expected to be at least 3 months with a flexible start date.
See All Internships for Machine Learning -
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Openings
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EA0042: Research Scientist - Control & Learning
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CI0130: Postdoctoral Research Fellow - Artificial General Intelligence (AGI)
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CA0093: Research Scientist - Control for Autonomous Systems
See All Openings at MERL -
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Recent Publications
- "Visually Robust Adversarial Imitation Learning from Videos with Contrastive Learning", IEEE International Conference on Robotics and Automation (ICRA), May 2025.BibTeX TR2025-065 PDF
- @inproceedings{Giammarino2025may,
- author = {Giammarino, Vittorio and Queeney, James and Paschalidis, Ioannis Ch.},
- title = {{Visually Robust Adversarial Imitation Learning from Videos with Contrastive Learning}},
- booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
- year = 2025,
- month = may,
- url = {https://www.merl.com/publications/TR2025-065}
- }
, - "Motor Fault Detection with a Hybrid Physics-based and Data-Driven Method", IEEE International Electric Machines and Drives Conference (IEMDC), May 2025.BibTeX TR2025-062 PDF
- @inproceedings{Ji2025may,
- author = {Ji, Dai-Yan and Wang, Bingnan and Inoue, Hiroshi and Kanemaru, Makoto},
- title = {{Motor Fault Detection with a Hybrid Physics-based and Data-Driven Method}},
- booktitle = {IEEE International Electric Machines and Drives Conference (IEMDC)},
- year = 2025,
- month = may,
- url = {https://www.merl.com/publications/TR2025-062}
- }
, - "Electric Motor Cogging Torque Prediction with Vision Transformer Models", IEEE International Electric Machines and Drives Conference (IEMDC), May 2025.BibTeX TR2025-059 PDF
- @inproceedings{Sun2025may,
- author = {Sun, Siyuan and Wang, Ye and Koike-Akino, Toshiaki and Yamamoto, Tatsuya and Sakamoto, Yusuke and Wang, Bingnan},
- title = {{Electric Motor Cogging Torque Prediction with Vision Transformer Models}},
- booktitle = {IEEE International Electric Machines and Drives Conference (IEMDC)},
- year = 2025,
- month = may,
- url = {https://www.merl.com/publications/TR2025-059}
- }
, - "Improving Open-World Object Localization by Discovering Background", CVPR Workshop on Domain Generalization: Evolution, Breakthroughs and Future Horizon, May 2025.BibTeX TR2025-058 PDF
- @inproceedings{Singh2025may,
- author = {Singh, Ashish and Jones, Michael J. and Peng, Kuan-Chuan and Chatterjee, Moitreya and Cherian, Anoop and Learned-Miller, Erik},
- title = {{Improving Open-World Object Localization by Discovering Background}},
- booktitle = {CVPR Workshop on Domain Generalization: Evolution, Breakthroughs and Future Horizon},
- year = 2025,
- month = may,
- url = {https://www.merl.com/publications/TR2025-058}
- }
, - "G-RepsNet: A Lightweight Construction of Equivariant Net- works for Arbitrary Matrix Groups", Transactions on Machine Learning Research (TMLR), May 2025.BibTeX TR2025-056 PDF Software
- @article{Basu2025may,
- author = {Basu, Sourya and Lohit, Suhas and Brand, Matthew},
- title = {{G-RepsNet: A Lightweight Construction of Equivariant Net- works for Arbitrary Matrix Groups}},
- journal = {Transactions on Machine Learning Research (TMLR)},
- year = 2025,
- month = may,
- url = {https://www.merl.com/publications/TR2025-056}
- }
, - "An End-to-End Integration of Speech Separation and Recognition with Self-Supervised Learning Representation", Computer Speech & Language, DOI: 10.1016/j.csl.2025.101813, Vol. 95, pp. 101813, May 2025.BibTeX TR2025-054 PDF
- @article{Masuyama2025may,
- author = {Masuyama, Yoshiki and Chang, Xuankai and Zhang, Wangyou and Cornell, Samuele and Wang, Zhong-Qiu and Ono, Nobutaka and Qian, Yanmin and Watanabe, Shinji},
- title = {{An End-to-End Integration of Speech Separation and Recognition with Self-Supervised Learning Representation}},
- journal = {Computer Speech \& Language},
- year = 2025,
- volume = 95,
- pages = 101813,
- month = may,
- doi = {10.1016/j.csl.2025.101813},
- issn = {0885-2308},
- url = {https://www.merl.com/publications/TR2025-054}
- }
, - "FDPP: Fine-tune Diffusion Policy with Human Preference", IEEE International Conference on Robotics and Automation (ICRA), May 2025.BibTeX TR2025-053 PDF Video
- @inproceedings{Chen2025may,
- author = {Chen, Yuxin and Jha, Devesh K. and Tomizuka, Masayoshi and Romeres, Diego},
- title = {{FDPP: Fine-tune Diffusion Policy with Human Preference}},
- booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
- year = 2025,
- month = may,
- url = {https://www.merl.com/publications/TR2025-053}
- }
, - "Quantum-PEFT: Ultra Parameter-Efficient Fine-Tuning", International Conference on Learning Representations (ICLR), April 2025.BibTeX TR2025-051 PDF
- @inproceedings{Koike-Akino2025apr,
- author = {Koike-Akino, Toshiaki and Tonin,Francesco and Wu,Yongtao and Wu,Frank Zhengqing and Candogan,Leyla Naz and Cevher, Volkan},
- title = {{Quantum-PEFT: Ultra Parameter-Efficient Fine-Tuning}},
- booktitle = {International Conference on Learning Representations (ICLR)},
- year = 2025,
- month = apr,
- url = {https://www.merl.com/publications/TR2025-051}
- }
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- "Visually Robust Adversarial Imitation Learning from Videos with Contrastive Learning", IEEE International Conference on Robotics and Automation (ICRA), May 2025.
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Videos
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Software & Data Downloads
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MEL-PETs Joint-Context Attack for LLM Privacy Challenge -
MEL-PETs Defense for LLM Privacy Challenge -
ComplexVAD Dataset -
Learned Born Operator for Reflection Tomographic Imaging -
Generalization in Deep RL with a Robust Adaptation Module -
Group Representation Networks -
Stabilizing Subject Transfer in EEG Classification with Divergence Estimation -
Retrieval-Augmented Neural Field for HRTF Upsampling and Personalization -
Self-Monitored Inference-Time INtervention for Generative Music Transformers -
Radar dEtection TRansformer -
Millimeter-wave Multi-View Radar Dataset -
Gear Extensions of Neural Radiance Fields -
Long-Tailed Anomaly Detection Dataset -
Target-Speaker SEParation -
Pixel-Grounded Prototypical Part Networks -
Steered Diffusion -
BAyesian Network for adaptive SAmple Consensus -
Meta-Learning State Space Models -
Explainable Video Anomaly Localization -
Simple Multimodal Algorithmic Reasoning Task Dataset -
Partial Group Convolutional Neural Networks -
SOurce-free Cross-modal KnowledgE Transfer -
Audio-Visual-Language Embodied Navigation in 3D Environments -
Nonparametric Score Estimators -
3D MOrphable STyleGAN -
Instance Segmentation GAN -
Audio Visual Scene-Graph Segmentor -
Generalized One-class Discriminative Subspaces -
Hierarchical Musical Instrument Separation -
Generating Visual Dynamics from Sound and Context -
Adversarially-Contrastive Optimal Transport -
Online Feature Extractor Network -
MotionNet -
FoldingNet++ -
Quasi-Newton Trust Region Policy Optimization -
Landmarks’ Location, Uncertainty, and Visibility Likelihood -
Robust Iterative Data Estimation -
Gradient-based Nikaido-Isoda -
Circular Maze Environment -
Discriminative Subspace Pooling -
Kernel Correlation Network -
Fast Resampling on Point Clouds via Graphs -
FoldingNet -
Deep Category-Aware Semantic Edge Detection -
MERL Shopping Dataset
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