- 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.
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- 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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- 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.
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- 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.
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- Date: July 10, 2024 - July 12, 2024
Where: Toronto, Canada
MERL Contacts: Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; Christopher R. Laughman; Arvind Raghunathan; Abraham P. Vinod; Yebin Wang; Avishai Weiss
Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics
Brief - MERL researchers presented 9 papers at the recently concluded American Control Conference (ACC) 2024 in Toronto, Canada. The papers covered a wide range of topics including data-driven spatial monitoring using heterogenous robots, aircraft approach management near airports, computation fluid dynamics-based motion planning for drones facing winds, trajectory planning for coordinated monitoring using a team of drones and a ground carrier vehicle, ensemble Kalman smoothing-based model predictive control for motion planning for autonomous vehicles, system identification for Lithium-ion batteries, physics-constrained deep Kalman filters for vapor compression systems, switched reference governors for constrained systems, and distributed road-map monitoring using onboard sensors.
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, Abraham Vinod served as a panelist at the Student Networking Event at the conference. The student networking event provides an opportunity for all interested students to network with professionals working in industry, academia, and national laboratories during a structured event, and encourages their continued participation as the future leaders in the field.
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- Date: June 13, 2024
Where: IEEE International Conference on Communications (ICC)
MERL Contacts: Jianlin Guo; Philip V. Orlik; Kieran Parsons; Pu (Perry) Wang
Research Areas: Communications, Machine Learning, Signal Processing
Brief - Jianlin Guo delivered a keynote titled "Private IoT Networks" in the IEEE International Conference on Communications (ICC) 2024 Workshop "Industrial Private 5G-and-Beyond Wireless Networks", held in Denver, Colorado from June 9-13. The ICC is one of two IEEE Communications Society’s flagship conferences.
Abstract: With the advent of private 5G-and-Beyond communication technologies, private IoT networks have been emerging. In private IoT networks, network owners have full control on the network resource management. However, to fully realize private IoT networks, the upper layer technologies need to be developed as well. This keynote presents machine learning based anomaly detection in manufacturing systems, innovative multipath TCP technologies over heterogeneous wireless IoT networks, novel channel resource scheduling in private 5G networks and efficient wireless coexistence of the heterogeneous wireless systems.
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- Date: May 13, 2024 - May 17, 2024
Where: Yokohama, Japan
MERL Contacts: Anoop Cherian; Radu Corcodel; Stefano Di Cairano; Chiori Hori; Siddarth Jain; Devesh K. Jha; Jonathan Le Roux; Diego Romeres; William S. Yerazunis
Research Areas: Artificial Intelligence, Machine Learning, Optimization, Robotics, Speech & Audio
Brief - MERL made significant contributions to both the organization and the technical program of the International Conference on Robotics and Automation (ICRA) 2024, which was held in Yokohama, Japan from May 13th to May 17th.
MERL was a Bronze sponsor of the conference, and exhibited a live robotic demonstration, which attracted a large audience. The demonstration showcased an Autonomous Robotic Assembly technology executed on MELCO's Assista robot arm and was the collaborative effort of the Optimization and Robotics Team together with the Advanced Technology department at Mitsubishi Electric.
MERL researchers from the Optimization and Robotics, Speech & Audio, and Control for Autonomy teams also presented 8 papers and 2 invited talks covering topics on robotic assembly, applications of LLMs to robotics, human robot interaction, safe and robust path planning for autonomous drones, transfer learning, perception and tactile sensing.
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- Date: May 22, 2024
MERL Contact: Toshiaki Koike-Akino
Research Areas: Artificial Intelligence, Machine Learning
Brief - Toshiaki Koike-Akino is invited to present a seminar talk at EPFL, Switzerland. The talk, entitled "Post-Deep Learning: Emerging Quantum AI Technology", will discuss the recent trends, challenges, and applications of quantum machine learning (QML) technologies. The seminar is organized by Prof. Volkan Cevher and Prof. Giovanni De Micheli. The event invites students, researchers, scholars and professors through EPFL departments including School of Engineering, Communication Science, Life Science, Machine Learning and AI Center.
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- Date: June 17, 2024 - June 21, 2024
Where: Seattle, WA
MERL Contacts: Petros T. Boufounos; Moitreya Chatterjee; Anoop Cherian; Michael J. Jones; Toshiaki Koike-Akino; Jonathan Le Roux; Suhas Lohit; Tim K. Marks; Pedro Miraldo; Jing Liu; Kuan-Chuan Peng; Pu (Perry) Wang; Ye Wang; Matthew Brand
Research Areas: Artificial Intelligence, Computational Sensing, Computer Vision, Machine Learning, Speech & Audio
Brief - MERL researchers are presenting 5 conference papers, 3 workshop papers, and are co-organizing two workshops at the CVPR 2024 conference, which will be held in Seattle, June 17-21. CVPR is one of the most prestigious and competitive international conferences in computer vision. Details of MERL contributions are provided below.
CVPR Conference Papers:
1. "TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models" by H. Ni, B. Egger, S. Lohit, A. Cherian, Y. Wang, T. Koike-Akino, S. X. Huang, and T. K. Marks
This work enables a pretrained text-to-video (T2V) diffusion model to be additionally conditioned on an input image (first video frame), yielding a text+image to video (TI2V) model. Other than using the pretrained T2V model, our method requires no ("zero") training or fine-tuning. The paper uses a "repeat-and-slide" method and diffusion resampling to synthesize videos from a given starting image and text describing the video content.
Paper: https://www.merl.com/publications/TR2024-059
Project page: https://merl.com/research/highlights/TI2V-Zero
2. "Long-Tailed Anomaly Detection with Learnable Class Names" by C.-H. Ho, K.-C. Peng, and N. Vasconcelos
This work aims to identify defects across various classes without relying on hard-coded class names. We introduce the concept of long-tailed anomaly detection, addressing challenges like class imbalance and dataset variability. Our proposed method combines reconstruction and semantic modules, learning pseudo-class names and utilizing a variational autoencoder for feature synthesis to improve performance in long-tailed datasets, outperforming existing methods in experiments.
Paper: https://www.merl.com/publications/TR2024-040
3. "Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-aware Spatio-Temporal Sampling" by X. Liu, Y-W. Tai, C-T. Tang, P. Miraldo, S. Lohit, and M. Chatterjee
This work presents a new strategy for rendering dynamic scenes from novel viewpoints. Our approach is based on stratifying the scene into regions based on the extent of motion of the region, which is automatically determined. Regions with higher motion are permitted a denser spatio-temporal sampling strategy for more faithful rendering of the scene. Additionally, to the best of our knowledge, ours is the first work to enable tracking of objects in the scene from novel views - based on the preferences of a user, provided by a click.
Paper: https://www.merl.com/publications/TR2024-042
4. "SIRA: Scalable Inter-frame Relation and Association for Radar Perception" by R. Yataka, P. Wang, P. T. Boufounos, and R. Takahashi
Overcoming the limitations on radar feature extraction such as low spatial resolution, multipath reflection, and motion blurs, this paper proposes SIRA (Scalable Inter-frame Relation and Association) for scalable radar perception with two designs: 1) extended temporal relation, generalizing the existing temporal relation layer from two frames to multiple inter-frames with temporally regrouped window attention for scalability; and 2) motion consistency track with a pseudo-tracklet generated from observational data for better object association.
Paper: https://www.merl.com/publications/TR2024-041
5. "RILA: Reflective and Imaginative Language Agent for Zero-Shot Semantic Audio-Visual Navigation" by Z. Yang, J. Liu, P. Chen, A. Cherian, T. K. Marks, J. L. Roux, and C. Gan
We leverage Large Language Models (LLM) for zero-shot semantic audio visual navigation. Specifically, by employing multi-modal models to process sensory data, we instruct an LLM-based planner to actively explore the environment by adaptively evaluating and dismissing inaccurate perceptual descriptions.
Paper: https://www.merl.com/publications/TR2024-043
CVPR Workshop Papers:
1. "CoLa-SDF: Controllable Latent StyleSDF for Disentangled 3D Face Generation" by R. Dey, B. Egger, V. Boddeti, Y. Wang, and T. K. Marks
This paper proposes a new method for generating 3D faces and rendering them to images by combining the controllability of nonlinear 3DMMs with the high fidelity of implicit 3D GANs. Inspired by StyleSDF, our model uses a similar architecture but enforces the latent space to match the interpretable and physical parameters of the nonlinear 3D morphable model MOST-GAN.
Paper: https://www.merl.com/publications/TR2024-045
2. “Tracklet-based Explainable Video Anomaly Localization” by A. Singh, M. J. Jones, and E. Learned-Miller
This paper describes a new method for localizing anomalous activity in video of a scene given sample videos of normal activity from the same scene. The method is based on detecting and tracking objects in the scene and estimating high-level attributes of the objects such as their location, size, short-term trajectory and object class. These high-level attributes can then be used to detect unusual activity as well as to provide a human-understandable explanation for what is unusual about the activity.
Paper: https://www.merl.com/publications/TR2024-057
MERL co-organized workshops:
1. "Multimodal Algorithmic Reasoning Workshop" by A. Cherian, K-C. Peng, S. Lohit, M. Chatterjee, H. Zhou, K. Smith, T. K. Marks, J. Mathissen, and J. Tenenbaum
Workshop link: https://marworkshop.github.io/cvpr24/index.html
2. "The 5th Workshop on Fair, Data-Efficient, and Trusted Computer Vision" by K-C. Peng, et al.
Workshop link: https://fadetrcv.github.io/2024/
3. "SuperLoRA: Parameter-Efficient Unified Adaptation for Large Vision Models" by X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand, G. Wang, and T. Koike-Akino
This paper proposes a generalized framework called SuperLoRA that unifies and extends different variants of low-rank adaptation (LoRA). Introducing new options with grouping, folding, shuffling, projection, and tensor decomposition, SuperLoRA offers high flexibility and demonstrates superior performance up to 10-fold gain in parameter efficiency for transfer learning tasks.
Paper: https://www.merl.com/publications/TR2024-062
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- Date: April 9, 2024
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Dynamical Systems, Machine Learning, Optimization, Robotics
Brief - Diego Romeres, Principal Research Scientist and Team Leader in the Optimization and Robotics Team, was invited to speak as a guest lecturer in the seminar series on "AI in Action" in the Department of Management and Engineering, at the University of Padua.
The talk, entitled "Machine Learning for Robotics and Automation" described MERL's recent research on machine learning and model-based reinforcement learning applied to robotics and automation.
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- Date: April 12, 2024
MERL Contact: Saviz Mowlavi
Research Areas: Control, Dynamical Systems, Machine Learning, Optimization
Brief - Saviz Mowlavi was invited to present remotely at the Computational and Applied Mathematics seminar series in the Department of Mathematics at North Carolina State University.
The talk, entitled "Model-based and data-driven prediction and control of spatio-temporal systems", described the use of temporal smoothness to regularize the training of fast surrogate models for PDEs, user-friendly methods for PDE-constrained optimization, and efficient strategies for learning feedback controllers for PDEs.
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- Date: December 9, 2024 - December 15, 2024
Where: NeurIPS 2024
MERL Contact: Devesh K. Jha
Research Areas: Artificial Intelligence, Machine Learning
Brief - Devesh Jha, a Principal Research Scientist in the Optimization & Intelligent Robtics team, has been appointed as an area chair for Conference on Neural Information Processing Systems (NeurIPS) 2024. NeurIPS is the premier Machine Learning (ML) and Artificial Intelligence (AI) conference that includes invited talks, demonstrations, symposia, and oral and poster presentations of refereed papers.
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- Date: March 20, 2024
Where: Austin, TX
MERL Contact: Ankush Chakrabarty
Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization
Brief - Ankush Chakrabarty, Principal Research Scientist in the Multiphysical Systems Team, was invited to speak as a guest lecturer in the seminar series on "Occupant-Centric Grid Interactive Buildings" in the Department of Civil, Architectural and Environmental Engineering (CAEE) at the University of Texas at Austin.
The talk, entitled "Deep Generative Networks and Fine-Tuning for Net-Zero Energy Buildings" described lessons learned from MERL's recent research on generative models for building simulation and control, along with meta-learning for on-the-fly fine-tuning to adapt and optimize energy expenditure.
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- Date: January 1, 2024
Awarded to: Jonathan Le Roux
MERL Contact: Jonathan Le Roux
Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
Brief - MERL Distinguished Scientist and Speech & Audio Senior Team Leader Jonathan Le Roux has been elevated to IEEE Fellow, effective January 2024, "for contributions to multi-source speech and audio processing."
Mitsubishi Electric celebrated Dr. Le Roux's elevation and that of another researcher from the company, Dr. Shumpei Kameyama, with a worldwide news release on February 15.
Dr. Jonathan Le Roux has made fundamental contributions to the field of multi-speaker speech processing, especially to the areas of speech separation and multi-speaker end-to-end automatic speech recognition (ASR). His contributions constituted a major advance in realizing a practically usable solution to the cocktail party problem, enabling machines to replicate humans’ ability to concentrate on a specific sound source, such as a certain speaker within a complex acoustic scene—a long-standing challenge in the speech signal processing community. Additionally, he has made key contributions to the measures used for training and evaluating audio source separation methods, developing several new objective functions to improve the training of deep neural networks for speech enhancement, and analyzing the impact of metrics used to evaluate the signal reconstruction quality. Dr. Le Roux’s technical contributions have been crucial in promoting the widespread adoption of multi-speaker separation and end-to-end ASR technologies across various applications, including smart speakers, teleconferencing systems, hearables, and mobile devices.
IEEE Fellow is the highest grade of membership of the IEEE. It honors members with an outstanding record of technical achievements, contributing importantly to the advancement or application of engineering, science and technology, and bringing significant value to society. Each year, following a rigorous evaluation procedure, the IEEE Fellow Committee recommends a select group of recipients for elevation to IEEE Fellow. Less than 0.1% of voting members are selected annually for this member grade elevation.
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- Date: December 15, 2023
Awarded to: Lingfeng Sun, Devesh K. Jha, Chiori Hori, Siddharth Jain, Radu Corcodel, Xinghao Zhu, Masayoshi Tomizuka and Diego Romeres
MERL Contacts: Radu Corcodel; Chiori Hori; Siddarth Jain; Devesh K. Jha; Diego Romeres
Research Areas: Artificial Intelligence, Machine Learning, Robotics
Brief - MERL Researchers received an "Honorable Mention award" at the Workshop on Instruction Tuning and Instruction Following at the NeurIPS 2023 conference in New Orleans. The workshop was on the topic of instruction tuning and Instruction following for Large Language Models (LLMs). MERL researchers presented their work on interactive planning using LLMs for partially observable robotic tasks during the oral presentation session at the workshop.
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- Date: December 16, 2023
Awarded to: Zexu Pan, Gordon Wichern, Yoshiki Masuyama, Francois Germain, Sameer Khurana, Chiori Hori, and Jonathan Le Roux
MERL Contacts: François Germain; Chiori Hori; Sameer Khurana; 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 12 teams in the 2nd COG-MHEAR Audio-Visual Speech Enhancement Challenge (AVSE). The team was led by Zexu Pan, and also included Gordon Wichern, Yoshiki Masuyama, Francois Germain, Sameer Khurana, Chiori Hori, and Jonathan Le Roux.
The AVSE challenge aims to design better speech enhancement systems by harnessing the visual aspects of speech (such as lip movements and gestures) in a manner similar to the brain’s multi-modal integration strategies. MERL’s system was a scenario-aware audio-visual TF-GridNet, that incorporates the face recording of a target speaker as a conditioning factor and also recognizes whether the predominant interference signal is speech or background noise. In addition to outperforming all competing systems in terms of objective metrics by a wide margin, in a listening test, MERL’s model achieved the best overall word intelligibility score of 84.54%, compared to 57.56% for the baseline and 80.41% for the next best team. The Fisher’s least significant difference (LSD) was 2.14%, indicating that our model offered statistically significant speech intelligibility improvements compared to all other systems.
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- Date: November 14, 2023
Where: Istanbul, Turkey
MERL Contact: Ankush Chakrabarty
Research Areas: Control, Data Analytics, Machine Learning, Multi-Physical Modeling, Optimization
Brief - Ankush Chakrabarty, Principal Research Scientist in the Multiphysical Systems team at MERL, served as Co-Chair at the 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities (BALANCES'23). The workshop places spotlights on two different IEA EBC Annexes: the Annex 81 - Data-Driven Smart Buildings and Annex 82 - Energy Flexible Buildings Towards Resilient Low Carbon Energy Systems.
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- Date: November 28, 2023 - November 30, 2023
Where: Virtual
MERL Contacts: Toshiaki Koike-Akino; Pu (Perry) Wang
Research Areas: Artificial Intelligence, Communications, Computational Sensing, Machine Learning, Signal Processing
Brief - On November 28, 2023, MERL researchers Toshiaki Koike-Akino and Pu (Perry) Wang will give a 3-hour tutorial presentation at the first IEEE Virtual Conference on Communications (VCC). The talk, titled "Post-Deep Learning Era: Emerging Quantum Machine Learning for Sensing and Communications," addresses recent trends, challenges, and advances in sensing and communications. P. Wang presents use cases, industry trends, signal processing, and deep learning for Wi-Fi integrated sensing and communications (ISAC), while T. Koike-Akino discusses the future of deep learning, giving a comprehensive overview of artificial intelligence (AI) technologies, natural computing, emerging quantum AI, and their diverse applications. The tutorial is conducted virtually.
IEEE VCC is a new fully virtual conference launched from the IEEE Communications Society, gathering researchers from academia and industry who are unable to travel but wish to present their recent scientific results and engage in conducive interactive discussions with fellow researchers working in their fields. It is designed to resolve potential hardship such as pandemic restrictions, visa issues, travel problems, or financial difficulties.
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- Date: September 26, 2023
Where: Virtual
MERL Contact: Anoop Cherian
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
Brief - Anoop Cherian, a Senior Principal Research Scientist in the Computer Vision team at MERL, gave a podcast interview with award-winning journalist, Deborah Yao. Deborah is the editor of AI Business -- a leading content platform for artificial intelligence and its applications in the real world, delivering its readers up-to-the-minute insights into how AI technologies are currently affecting the global economy and society. The podcast was based on the recent research that Anoop and his colleagues did at MERL with his collaborators at MIT; this research attempts to objectively answer the pertinent question: are current deep neural networks smarter than second graders? The podcast discusses shortcomings in the recent artificial general intelligence systems with regard to their capabilities for knowledge abstraction, learning, and generalization, which are brought out by this research.
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- Date: November 1, 2023
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Machine Learning, Robotics
Brief - Principal Research Scientist and Team Leader Diego Romeres gave an invited talk with title 'Applications of Machine Learning to Robotics' in the Machine Learning graduate course at Bentley University. The presentation focused mainly on Reinforcement Learning research applied to robotics. The audience consisted mostly of Master’s in Business Analytics (MSBA) students and students in the MBA w/ Business Analytics Concentration program.
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- Date: January 23, 2023 - November 4, 2023
Where: International Symposium of Music Information Retrieval (ISMR)
MERL Contacts: Jonathan Le Roux; Gordon Wichern
Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
Brief - MERL Speech & Audio team members Gordon Wichern and Jonathan Le Roux co-organized the 2023 Sound Demixing Challenge along with researchers from Sony, Moises AI, Audioshake, and Meta.
The SDX2023 Challenge was hosted on the AI Crowd platform and had a prize pool of $42,000 distributed to the winning teams across two tracks: Music Demixing and Cinematic Sound Demixing. A unique aspect of this challenge was the ability to test the audio source separation models developed by challenge participants on non-public songs from Sony Music Entertainment Japan for the music demixing track, and movie soundtracks from Sony Pictures for the cinematic sound demixing track. The challenge ran from January 23rd to May 1st, 2023, and had 884 participants distributed across 68 teams submitting 2828 source separation models. The winners will be announced at the SDX2023 Workshop, which will take place as a satellite event at the International Symposium of Music Information Retrieval (ISMR) in Milan, Italy on November 4, 2023.
MERL’s contribution to SDX2023 focused mainly on the cinematic demixing track. In addition to sponsoring the prizes awarded to the winning teams for that track, the baseline system and initial training data were MERL’s Cocktail Fork separation model and Divide and Remaster dataset, respectively. MERL researchers also contributed to a Town Hall kicking off the challenge, co-authored a scientific paper describing the challenge outcomes, and co-organized the SDX2023 Workshop.
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- Date: October 2, 2023 - October 6, 2023
Where: Paris/France
MERL Contacts: Moitreya Chatterjee; Anoop Cherian; Michael J. Jones; Toshiaki Koike-Akino; Suhas Lohit; Tim K. Marks; Pedro Miraldo; Kuan-Chuan Peng; Ye Wang
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
Brief - MERL researchers are presenting 4 papers and organizing the VLAR-SMART-101 workshop at the ICCV 2023 conference, which will be held in Paris, France October 2-6. ICCV is one of the most prestigious and competitive international conferences in computer vision. Details are provided below.
1. Conference paper: “Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image Synthesis,” by Nithin Gopalakrishnan Nair, Anoop Cherian, Suhas Lohit, Ye Wang, Toshiaki Koike-Akino, Vishal Patel, and Tim K. Marks
Conditional generative models typically demand large annotated training sets to achieve high-quality synthesis. As a result, there has been significant interest in plug-and-play generation, i.e., using a pre-defined model to guide the generative process. In this paper, we introduce Steered Diffusion, a generalized framework for fine-grained photorealistic zero-shot conditional image generation using a diffusion model trained for unconditional generation. The key idea is to steer the image generation of the diffusion model during inference via designing a loss using a pre-trained inverse model that characterizes the conditional task. Our model shows clear qualitative and quantitative improvements over state-of-the-art diffusion-based plug-and-play models, while adding negligible computational cost.
2. Conference paper: "BANSAC: A dynamic BAyesian Network for adaptive SAmple Consensus," by Valter Piedade and Pedro Miraldo
We derive a dynamic Bayesian network that updates individual data points' inlier scores while iterating RANSAC. At each iteration, we apply weighted sampling using the updated scores. Our method works with or without prior data point scorings. In addition, we use the updated inlier/outlier scoring for deriving a new stopping criterion for the RANSAC loop. Our method outperforms the baselines in accuracy while needing less computational time.
3. Conference paper: "Robust Frame-to-Frame Camera Rotation Estimation in Crowded Scenes," by Fabien Delattre, David Dirnfeld, Phat Nguyen, Stephen Scarano, Michael J. Jones, Pedro Miraldo, and Erik Learned-Miller
We present a novel approach to estimating camera rotation in crowded, real-world scenes captured using a handheld monocular video camera. Our method uses a novel generalization of the Hough transform on SO3 to efficiently find the camera rotation most compatible with the optical flow. Because the setting is not addressed well by other data sets, we provide a new dataset and benchmark, with high-accuracy and rigorously annotated ground truth on 17 video sequences. Our method is more accurate by almost 40 percent than the next best method.
4. Workshop paper: "Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection" by Manish Sharma*, Moitreya Chatterjee*, Kuan-Chuan Peng, Suhas Lohit, and Michael Jones
While state-of-the-art object detection methods for RGB images have reached some level of maturity, the same is not true for Infrared (IR) images. The primary bottleneck towards bridging this gap is the lack of sufficient labeled training data in the IR images. Towards addressing this issue, we present TensorFact, a novel tensor decomposition method which splits the convolution kernels of a CNN into low-rank factor matrices with fewer parameters. This compressed network is first pre-trained on RGB images and then augmented with only a few parameters. This augmented network is then trained on IR images, while freezing the weights trained on RGB. This prevents it from over-fitting, allowing it to generalize better. Experiments show that our method outperforms state-of-the-art.
5. “Vision-and-Language Algorithmic Reasoning (VLAR) Workshop and SMART-101 Challenge” by Anoop Cherian, Kuan-Chuan Peng, Suhas Lohit, Tim K. Marks, Ram Ramrakhya, Honglu Zhou, Kevin A. Smith, Joanna Matthiesen, and Joshua B. Tenenbaum
MERL researchers along with researchers from MIT, GeorgiaTech, Math Kangaroo USA, and Rutgers University are jointly organizing a workshop on vision-and-language algorithmic reasoning at ICCV 2023 and conducting a challenge based on the SMART-101 puzzles described in the paper: Are Deep Neural Networks SMARTer than Second Graders?. A focus of this workshop is to bring together outstanding faculty/researchers working at the intersections of vision, language, and cognition to provide their opinions on the recent breakthroughs in large language models and artificial general intelligence, as well as showcase their cutting edge research that could inspire the audience to search for the missing pieces in our quest towards solving the puzzle of artificial intelligence.
Workshop link: https://wvlar.github.io/iccv23/
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- Date: August 25, 2023
Awarded to: Alberto Dalla Libera, Niccolo' Turcato, Giulio Giacomuzzo, Ruggero Carli, Diego Romeres
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Machine Learning, Robotics
Brief - A joint team consisting of members of University of Padua and MERL ranked 1st in the IJCAI2023 Challenge "Al Olympics With RealAlGym: Is Al Ready for Athletic Intelligence in the Real World?". The team was composed by MERL researcher Diego Romeres and a team from University Padua (UniPD) consisting of Alberto Dalla Libera, Ph.D., Ph.D. Candidates: Niccolò Turcato, Giulio Giacomuzzo and Prof. Ruggero Carli from University of Padua.
The International Joint Conference on Artificial Intelligence (IJCAI) is a premier gathering for AI researchers and organizes several competitions. This year the competition CC7 "AI Olympics With RealAIGym: Is AI Ready for Athletic Intelligence in the Real World?" consisted of two stages: simulation and real-robot experiments on two under-actuated robotic systems. The two robotics systems were treated as separate tracks and one final winner was selected for each track based on specific performance criteria in the control tasks.
The UniPD-MERL team competed and won in both tracks. The team's system made strong use of a Model-based Reinforcement Learning algorithm called (MC-PILCO) that we recently published in the journal IEEE Transaction on Robotics.
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- Date: July 9, 2023 - July 14, 2023
MERL Contacts: Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Christopher R. Laughman; Diego Romeres; Abraham P. Vinod
Research Areas: Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics
Brief - MERL researchers presented 9 papers and organized 2 invited/workshop sessions at the 2023 IFAC World Congress held in Yokohama, JP.
MERL's contributions covered topics including decision-making for autonomous vehicles, statistical and learning-based estimation for GNSS and energy systems, impedance control for delta robots, learning for system identification of rigid body dynamics and time-varying systems, and meta-learning for deep state-space modeling using data from similar systems. The invited session (MERL co-organizer: Ankush Chakrabarty) was on the topic of “Estimation and observer design: theory and applications” and the workshop (MERL co-organizer: Karl Berntorp) was on “Gaussian Process Learning for Systems and Control”.
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- Date: July 11, 2023
Where: Daegu, Korea
MERL Contacts: Siddarth Jain; Devesh K. Jha; Arvind Raghunathan
Research Areas: Artificial Intelligence, Machine Learning, Robotics
Brief - MERL researchers presented 3 papers at the 19th edition of Robotics:Science and Systems Conference in Daegu, Korea. RSS is the flagship conference of the RSS foundation and is run as a single track conference presenting a limited number of high-quality papers. This year the main conference had a total of 112 papers presented. MERL researchers presented 2 papers in the main conference on planning and perception for dexterous manipulation. Another paper was presented in a workshop of learning for dexterous manipulation. More details can be found here https://roboticsconference.org.
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