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 work receives IEEE Transactions on Automation Science and Engineering Best New Application Paper Award from IEEE Robotics and Automation Society
  Date: May 19, 2025
  Awarded to: Yehan Ma, Yebin Wang, Stefano Di Cairano, Toshiaki Koike-Akino, Jianlin Guo, Philip Orlik, Xinping Guan and Chenyang Lu
  MERL Contacts: Stefano Di Cairano; Jianlin Guo; Toshiaki Koike-Akino; Philip V. Orlik; Yebin Wang
  Research Areas: Communications, Control, Machine Learning
  Brief
  - The paper “Smart Actuation for End-Edge Industrial Control Systems”, co-authored by MERL intern Yehan Ma, MERL researchers Yebin Wang, Stefano Di Cairano, Toshiaki Koike-Akino, Jianlin Guo, and Philip Orlik, and academic collaborators Xinping Guan and Chenyang Lu, was recognized as the Best New Application Paper of the IEEE Transactions on Automation Science and Engineering (T-ASE), for "a new industrial automation solution that ensures safety operation through coordinated co-design of edge model predictive control and local actuation".
    
    The award recognizes the best application paper published in T-ASE over the previous calendar year, for the significance of new applications, technical merit, originality, potential impact on the field, and clarity of presentation.
- 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.
See All Awards for Machine Learning
News & Events
- NEWS Toshiaki Koike-Akino to give a tutorial talk at ISIT 2025 Quantum Hackathon
  Date: June 22, 2025
  Where: IEEE International Symposium on Information Theory (ISIT)
  MERL Contact: Toshiaki Koike-Akino
  Research Areas: Artificial Intelligence, Communications, Data Analytics, Machine Learning, Optimization, Signal Processing, Human-Computer Interaction, Information Security
  Brief
  - Toshiaki Koike-Akino is invited to present a tutorial talk at IEEE ISIT 2025 Quantum Hackathon, to be held at Ann Arbor, Michigan, USA. The talk, entitled "Emerging Quantum AI Technology", will discuss the recent trends, challenges, and applications of quantum artificial intelligence (QAI) technologies.
    
    The ISIT 2025 Quantum Hackathon invites participants to explore the intersection of quantum computing and information theory. Participants will work with quantum simulators, available quantum hardware, and state-of-the-art development kits to create innovative solutions that connect quantum advancements with challenges in communication and signal processing.
    
    The IEEE International Symposium on Information Theory (ISIT) is the flagship conference of the IEEE Information Theory Society. The symposium centers around the presentation in all of the areas of information theory, including source and channel coding, communication theory and systems, cryptography and security, detection and estimation, networks, pattern recognition and learning, statistics, stochastic processes and complexity, and signal processing.
- NEWS MERL Papers and Workshops at CVPR 2025
  Date: June 11, 2025 - June 15, 2025
  Where: Nashville, TN, USA
  MERL Contacts: Matthew Brand; Moitreya Chatterjee; Anoop Cherian; François Germain; Michael J. Jones; Toshiaki Koike-Akino; Jing Liu; Suhas Lohit; Tim K. Marks; Pedro Miraldo; Kuan-Chuan Peng; Naoko Sawada; Pu (Perry) Wang; Ye Wang
  Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Signal Processing, Speech & Audio
  Brief
  - MERL researchers are presenting 2 conference papers, co-organizing two workshops, and presenting 7 workshop papers at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2025 conference, which will be held in Nashville, TN, USA from June 11-15, 2025. CVPR is one of the most prestigious and competitive international conferences in the area of computer vision. Details of MERL contributions are provided below:
    
    Main Conference Papers:
    
    1. "UWAV: Uncertainty-weighted Weakly-supervised Audio-Visual Video Parsing" by Y.H. Lai, J. Ebbers, Y. F. Wang, F. Germain, M. J. Jones, M. Chatterjee
    
    This work deals with the task of weakly‑supervised Audio-Visual Video Parsing (AVVP) and proposes a novel, uncertainty-aware algorithm called UWAV towards that end. UWAV works by producing more reliable segment‑level pseudo‑labels while explicitly weighting each label by its prediction uncertainty. This uncertainty‑aware training, combined with a feature‑mixup regularization scheme, promotes inter‑segment consistency in the pseudo-labels. As a result, UWAV achieves state‑of‑the‑art performance on two AVVP datasets across multiple metrics, demonstrating both effectiveness and strong generalizability.
    
    Paper: https://www.merl.com/publications/TR2025-072
    
    2. "TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection" by Y. G. Jung, J. Park, J. Yoon, K.-C. Peng, W. Kim, A. B. J. Teoh, and O. Camps.
    
    This work tackles unsupervised anomaly detection in complex scenarios where normal data is noisy and has an unknown, imbalanced class distribution. Existing models face a trade-off between robustness to noise and performance on rare (tail) classes. To address this, the authors propose TailSampler, which estimates class sizes from embedding similarities to isolate tail samples. Using TailSampler, they develop TailedCore, a memory-based model that effectively captures tail class features while remaining noise-robust, outperforming state-of-the-art methods in extensive evaluations.
    
    paper: https://www.merl.com/publications/TR2025-077
    
    MERL Co-Organized Workshops:
    
    1. Multimodal Algorithmic Reasoning (MAR) Workshop, organized by A. Cherian, K.-C. Peng, S. Lohit, H. Zhou, K. Smith, L. Xue, T. K. Marks, and J. Tenenbaum.
    
    Workshop link: https://marworkshop.github.io/cvpr25/
    
    2. The 6th Workshop on Fair, Data-Efficient, and Trusted Computer Vision, organized by N. Ratha, S. Karanam, Z. Wu, M. Vatsa, R. Singh, K.-C. Peng, M. Merler, and K. Varshney.
    
    Workshop link: https://fadetrcv.github.io/2025/
    
    Workshop Papers:
    
    1. "FreBIS: Frequency-Based Stratification for Neural Implicit Surface Representations" by N. Sawada, P. Miraldo, S. Lohit, T.K. Marks, and M. Chatterjee (Oral)
    
    With their ability to model object surfaces in a scene as a continuous function, neural implicit surface reconstruction methods have made remarkable strides recently, especially over classical 3D surface reconstruction methods, such as those that use voxels or point clouds. Towards this end, we propose FreBIS - a neural implicit‑surface framework that avoids overloading a single encoder with every surface detail. It divides a scene into several frequency bands and assigns a dedicated encoder (or group of encoders) to each band, then enforces complementary feature learning through a redundancy‑aware weighting module. Swapping this frequency‑stratified stack into an off‑the‑shelf reconstruction pipeline markedly boosts 3D surface accuracy and view‑consistent rendering on the challenging BlendedMVS dataset.
    
    paper: https://www.merl.com/publications/TR2025-074
    
    2. "Multimodal 3D Object Detection on Unseen Domains" by D. Hegde, S. Lohit, K.-C. Peng, M. J. Jones, and V. M. Patel.
    
    LiDAR-based object detection models often suffer performance drops when deployed in unseen environments due to biases in data properties like point density and object size. Unlike domain adaptation methods that rely on access to target data, this work tackles the more realistic setting of domain generalization without test-time samples. We propose CLIX3D, a multimodal framework that uses both LiDAR and image data along with supervised contrastive learning to align same-class features across domains and improve robustness. CLIX3D achieves state-of-the-art performance across various domain shifts in 3D object detection.
    
    paper: https://www.merl.com/publications/TR2025-078
    
    3. "Improving Open-World Object Localization by Discovering Background" by A. Singh, M. J. Jones, K.-C. Peng, M. Chatterjee, A. Cherian, and E. Learned-Miller.
    
    This work tackles open-world object localization, aiming to detect both seen and unseen object classes using limited labeled training data. While prior methods focus on object characterization, this approach introduces background information to improve objectness learning. The proposed framework identifies low-information, non-discriminative image regions as background and trains the model to avoid generating object proposals there. Experiments on standard benchmarks show that this method significantly outperforms previous state-of-the-art approaches.
    
    paper: https://www.merl.com/publications/TR2025-058
    
    4. "PF3Det: A Prompted Foundation Feature Assisted Visual LiDAR 3D Detector" by K. Li, T. Zhang, K.-C. Peng, and G. Wang.
    
    This work addresses challenges in 3D object detection for autonomous driving by improving the fusion of LiDAR and camera data, which is often hindered by domain gaps and limited labeled data. Leveraging advances in foundation models and prompt engineering, the authors propose PF3Det, a multi-modal detector that uses foundation model encoders and soft prompts to enhance feature fusion. PF3Det achieves strong performance even with limited training data. It sets new state-of-the-art results on the nuScenes dataset, improving NDS by 1.19% and mAP by 2.42%.
    
    paper: https://www.merl.com/publications/TR2025-076
    
    5. "Noise Consistency Regularization for Improved Subject-Driven Image Synthesis" by Y. Ni., S. Wen, P. Konius, A. Cherian
    
    Fine-tuning Stable Diffusion enables subject-driven image synthesis by adapting the model to generate images containing specific subjects. However, existing fine-tuning methods suffer from two key issues: underfitting, where the model fails to reliably capture subject identity, and overfitting, where it memorizes the subject image and reduces background diversity. To address these challenges, two auxiliary consistency losses are porposed for diffusion fine-tuning. First, a prior consistency regularization loss ensures that the predicted diffusion noise for prior (non- subject) images remains consistent with that of the pretrained model, improving fidelity. Second, a subject consistency regularization loss enhances the fine-tuned model’s robustness to multiplicative noise modulated latent code, helping to preserve subject identity while improving diversity. Our experimental results demonstrate the effectiveness of our approach in terms of image diversity, outperforming DreamBooth in terms of CLIP scores, background variation, and overall visual quality.
    
    paper: https://www.merl.com/publications/TR2025-073
    
    6. "LatentLLM: Attention-Aware Joint Tensor Compression" by T. Koike-Akino, X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand
    
    We propose a new framework to convert a large foundation model such as large language models (LLMs)/large multi- modal models (LMMs) into a reduced-dimension latent structure. Our method uses a global attention-aware joint tensor decomposition to significantly improve the model efficiency. We show the benefit on several benchmark including multi-modal reasoning tasks.
    
    paper: https://www.merl.com/publications/TR2025-075
    
    7. "TuneComp: Joint Fine-Tuning and Compression for Large Foundation Models" by T. Koike-Akino, X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand
    
    To reduce model size during post-training, compression methods, including knowledge distillation, low-rank approximation, and pruning, are often applied after fine- tuning the model. However, sequential fine-tuning and compression sacrifices performance, while creating a larger than necessary model as an intermediate step. In this work, we aim to reduce this gap, by directly constructing a smaller model while guided by the downstream task. We propose to jointly fine-tune and compress the model by gradually distilling it to a pruned low-rank structure. Experiments demonstrate that joint fine-tuning and compression significantly outperforms other sequential compression methods.
    
    paper: https://www.merl.com/publications/TR2025-079
See All News & Events for Machine Learning
Research Highlights
Internships
- EA0151: Internship - Physics-informed machine learning
  
  MERL is looking for a self-motivated intern to work on physics-informed machine learning with application to electric machine condition monitoring and predictive maintenance. The ideal candidate would be a Ph.D. student in electrical engineering or computer science with solid research background in electric machines, signal processing, and machine learning. Proficiency in Python and Matlab is required. The intern is expected to collaborate with MERL researchers to build machine learning model for multi-modal data analysis, prepare technical reports, and draft manuscripts for scientific publications. The total duration is anticipated to be 3-6 months. The start date is flexible. This internship requires work that can only be done at MERL.
- 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.
- CI0082: Internship - Quantum AI
  
  MERL is excited to announce an internship opportunity in the field of Quantum Machine Learning (QML) and Quantum AI (QAI). We are seeking a highly motivated and talented individual to join our research team. This is an exciting opportunity to make a real impact in the field of quantum computing and AI, with the aim of publishing at leading research venues.
  Responsibilities:
  - Conduct cutting-edge research in quantum machine learning.
  - Collaborate with a team of experts in quantum computing, deep learning, and signal processing.
  - Develop and implement algorithms using PyTorch and PennyLane.
  - Publish research results at leading research venues.
  Qualifications:
  - Currently pursuing a PhD or a post-graduate researcher in a relevant field.
  - Strong background and solid publication records in quantum computing, deep learning, and signal processing.
  - Proficient programming skills in PyTorch and PennyLane are highly desirable.
  What We Offer:
  - An opportunity to work on groundbreaking research in a leading research lab.
  - Collaboration with a team of experienced researchers.
  - A stimulating and supportive work environment.
  If you are passionate about quantum machine learning and meet the above qualifications, we encourage you to apply. Please submit your resume and a brief cover letter detailing your research experience and interests. Join us at MERL and contribute to the future of quantum machine learning!
See All Internships for Machine Learning
Openings
See All Openings at MERL
Recent Publications
- Santra, A., Wang, P., Shaker, G., Mysore, B.S., Dolmans, G., Chen, Y., Shariati, N., Pandharipande, A., "Machine Learning-Powered Radio Frequency Sensing: A Review", IEEE Sensors Journal, June 2025.
  BibTeX TR2025-099 PDF
  - @article{Santra2025jun,
  - author = {Santra, Avik and Wang, Pu and Shaker, George and Mysore, Bhavani Shankar and Dolmans, Guido and Chen, Yan and Shariati, Negin and Pandharipande, Ashish},
  - title = {{Machine Learning-Powered Radio Frequency Sensing: A Review}},
  - journal = {IEEE Sensors Journal},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-099}
  - }
- Ma, H., Zhang, T., Li, N., Di Cairano, S., Wang, Y., "A Hierarchical Approach for Tractor-trailer Motion Planning Using Graph Search and Reinforcement Learning", European Control Conference (ECC), June 2025.
  BibTeX TR2025-093 PDF
  - @inproceedings{Ma2025jun,
  - author = {Ma, Haitong and Zhang, Tianpeng and Li, Na and {Di Cairano}, Stefano and Wang, Yebin},
  - title = {{A Hierarchical Approach for Tractor-trailer Motion Planning Using Graph Search and Reinforcement Learning}},
  - booktitle = {European Control Conference (ECC)},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-093}
  - }
- Wang, R., Wang, Y., Liu, J., Koike-Akino, T., "Quantum Diffusion Models for Few-Shot Learning", ICAD, June 2025.
  BibTeX TR2025-095 PDF
  - @inproceedings{Wang2025jun2,
  - author = {Wang, Ruhan and Wang, Ye and Liu, Jing and Koike-Akino, Toshiaki},
  - title = {{Quantum Diffusion Models for Few-Shot Learning}},
  - booktitle = {ICAD},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-095}
  - }
- Sun, S., Wang, Y., Koike-Akino, T., Yamamoto, T., Sakamoto, Y., Wang, B., "Image-based Deep Learning Models for Electric Motors", International Conference on the Computation of Electromagnetic Fields (COMPUMAG), June 2025.
  BibTeX TR2025-088 PDF
  - @inproceedings{Sun2025jun,
  - author = {Sun, Siyuan and Wang, Ye and Koike-Akino, Toshiaki and Yamamoto, Tatsuya and Sakamoto, Yusuke and Wang, Bingnan},
  - title = {{Image-based Deep Learning Models for Electric Motors}},
  - booktitle = {International Conference on the Computation of Electromagnetic Fields (COMPUMAG)},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-088}
  - }
- Li, X., Wang, Y., Ozbay, K., Jiang, Z.-P., "Physics-informed Machine Learning with Heuristic Feedback Control Layer for Autonomous Vehicle Control", IEEE Intelligent Vehicles Symposium (IV), June 2025.
  BibTeX TR2025-087 PDF
  - @inproceedings{Li2025jun2,
  - author = {Li, Xianning and Wang, Yebin and Ozbay, Kaan and Jiang, Zhong-Ping},
  - title = {{Physics-informed Machine Learning with Heuristic Feedback Control Layer for Autonomous Vehicle Control}},
  - booktitle = {IEEE Intelligent Vehicles Symposium (IV)},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-087}
  - }
- Masuyama, Y., "Single- and Multi-Channel Speech Enhancement and Separation for Far-Field Conversation Recognition," Tech. Rep. TR2025-097, Jelinek Summer Workshop on Speech and Language Technology (JSALT), June 2025.
  BibTeX TR2025-097 PDF
  - @techreport{Masuyama2025jun,
  - author = {{{Masuyama, Yoshiki}}},
  - title = {{{Single- and Multi-Channel Speech Enhancement and Separation for Far-Field Conversation Recognition}}},
  - institution = {Jelinek Summer Workshop on Speech and Language Technology (JSALT)},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-097}
  - }
- Guo, P., Liu, D., Wang, B., Wang, Y., Inoue, H., Kanemaru, M., "Deep Generalized Canonical Correlation Analysis for Motor Fault Diagnosis", 2025 IEEE Industry Applications Society Annual Meeting (IAS), June 2025.
  BibTeX TR2025-085 PDF
  - @inproceedings{Guo2025jun2,
  - author = {Guo, Peikun and Liu, Dehong and Wang, Bingnan and Wang, Yebin and Inoue, Hiroshi and Kanemaru, Makoto},
  - title = {{Deep Generalized Canonical Correlation Analysis for Motor Fault Diagnosis}},
  - booktitle = {2025 IEEE Industry Applications Society Annual Meeting (IAS)},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-085}
  - }
- Chen, X., Liu, J., Wang, Y., Brand, M., Wang, P., Koike-Akino, T., "TuneComp: Joint Fine-Tuning and Compression for Large Foundation Models", IEEE Conference on Computer Vision and Pattern Recognition (CVPR) workshop on Efficient and On-Device Generation, June 2025.
  BibTeX TR2025-079 PDF
  - @inproceedings{Chen2025jun,
  - author = {Chen, Xiangyu and Liu, Jing and Wang, Ye and Brand, Matthew and Wang, Pu and Koike-Akino, Toshiaki},
  - title = {{TuneComp: Joint Fine-Tuning and Compression for Large Foundation Models}},
  - booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR) workshop on Efficient and On-Device Generation},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-079}
  - }
See All Publications for Machine Learning
Videos

[CVPR 2025] TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection

FDPP: Fine-tune Diffusion Policy with Human Preference

[MERL Seminar Series Spring 2025] Red Teaming AI Agents in-the-wild: Revealing Deployment Vulnerabilities

[MERL Seminar Series Spring 2025] The Emergence of Generalizability and Semantic Low-Dim Subspaces in Diffusion Models

[MERL Seminar Series Spring 2025] Amplifying human performance in combinatorial competitive programming

[WACV 2025] Towards Zero-shot 3D Anomaly Localization

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

Semantic Scene Labeling

Obstacle Detection

MERL Research on Autonomous Vehicles

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:

Group Representation Networks (G-RepsNets)
Self-Monitored Inference-Time INtervention for Generative Music Transformers (SMITIN)
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)

See All Downloads