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
Gordon
Wichern
Anoop
Cherian
Tim K.
Marks
Pu
(Perry)
Wang
Michael J.
Jones
Christopher R.
Laughman
Kieran
Parsons
Stefano
Di Cairano
Jing
Liu
Philip V.
Orlik
Daniel N.
Nikovski
Suhas
Lohit
Chiori
Hori
Bingnan
Wang
Yebin
Wang
Matthew
Brand
Hassan
Mansour
Yoshiki
Masuyama
Petros T.
Boufounos
Kuan-Chuan
Peng
Moitreya
Chatterjee
Abraham P.
Vinod
Pedro
Miraldo
Arvind
Raghunathan
Vedang M.
Deshpande
Jianlin
Guo
Siddarth
Jain
Saviz
Mowlavi
Hongtao
Qiao
Scott A.
Bortoff
Radu
Corcodel
William S.
Yerazunis
Chungwei
Lin
Dehong
Liu
Hongbo
Sun
Joshua
Rapp
Nobuyuki
Yoshikawa
Wael H.
Ali
Christoph
Boeddeker
Yanting
Ma
Lalit
Manam
Alexander
Schperberg
Anthony
Vetro
Jinyun
Zhang
Purnanand
Elango
Abraham
Goldsmith
Kaen
Kogashi
Zhaolin
Ren
Kei
Suzuki
Avishai
Weiss
Kenji
Inomata
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Awards
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AWARD MERL team wins the Generative Data Augmentation of Room Acoustics (GenDARA) 2025 Challenge Date: April 7, 2025
Awarded to: Christopher Ick, Gordon Wichern, Yoshiki Masuyama, François G. Germain, and Jonathan Le Roux
MERL Contacts: Jonathan Le Roux; Yoshiki Masuyama; Gordon Wichern
Research Areas: Artificial Intelligence, Machine Learning, Speech & AudioBrief- MERL's Speech & Audio team ranked 1st out of 3 teams in the Generative Data Augmentation of Room Acoustics (GenDARA) 2025 Challenge, which focused on “generating room impulse responses (RIRs) to supplement a small set of measured examples and using the augmented data to train speaker distance estimation (SDE) models". The team was led by MERL intern Christopher Ick, and also included Gordon Wichern, Yoshiki Masuyama, François G. Germain, and Jonathan Le Roux.
The GenDARA Challenge was organized as part of the Generative Data Augmentation (GenDA) workshop at the 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2025), and held on April 7, 2025 in Hyderabad, India. Yoshiki Masuyama presented the team's method, "Data Augmentation Using Neural Acoustic Fields With Retrieval-Augmented Pre-training".
The GenDARA challenge aims to promote the use of generative AI to synthesize RIRs from limited room data, as collecting or simulating RIR datasets at scale remains a significant challenge due to high costs and trade-offs between accuracy and computational efficiency. The challenge asked participants to first develop RIR generation systems capable of expanding a sparse set of labeled room impulse responses by generating RIRs at new source–receiver positions. They were then tasked with using this augmented dataset to train speaker distance estimation systems. Ranking was determined by the overall performance on the downstream SDE task. MERL’s approach to the GenDARA challenge centered on a geometry-aware neural acoustic field model that was first pre-trained on a large external RIR dataset to learn generalizable mappings from 3D room geometry to room impulse responses. For each challenge room, the model was then adapted or fine-tuned using the small number of provided RIRs, enabling high-fidelity generation of RIRs at unseen source–receiver locations. These augmented RIR sets were subsequently used to train the SDE system, improving speaker distance estimation by providing richer and more diverse acoustic training data.
- MERL's Speech & Audio team ranked 1st out of 3 teams in the Generative Data Augmentation of Room Acoustics (GenDARA) 2025 Challenge, which focused on “generating room impulse responses (RIRs) to supplement a small set of measured examples and using the augmented data to train speaker distance estimation (SDE) models". The team was led by MERL intern Christopher Ick, and also included Gordon Wichern, Yoshiki Masuyama, François G. Germain, and Jonathan Le Roux.
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AWARD Mitsubishi Electric Team Wins Awards at GalFer Contest Date: June 23, 2025
Awarded to: Bingnan Wang, Tatsuya Yamamoto, Yusuke Sakamoto, Siyuan Sun, Toshiaki Koike-Akino, and Ye Wang
MERL Contacts: Toshiaki Koike-Akino; Bingnan Wang; Ye Wang
Research Areas: Machine Learning, Multi-Physical Modeling, OptimizationBrief- The MELSUR (Mitsubishi Electric SURrogate) team, consisting of a group of MERL and Mitsubishi Electric researchers, ranked first in two out of three categories in the GalFer Contest.
The GalFer (Galileo Ferraris) contest aims to compare the accuracy and efficiency of data-driven methodologies for the multi-physics simulation of traction electric machines. A total of 26 teams worldwide participated in the contest, which consists of three categories. The MELSUR team, including MERL staff Bingnan Wang, Toshiaki Koike-Akino, Ye Wang, MERL intern Siyuan Sun, Mitsubishi Electric researchers Tatsuya Yamamoto and Yusuke Sakamoto, ranked first for the category of "Novelty" and "Interpolation". The results were announced during an award ceremony at the COMPUMAG 2025 conference in Naples, Italy.
- The MELSUR (Mitsubishi Electric SURrogate) team, consisting of a group of MERL and Mitsubishi Electric researchers, ranked first in two out of three categories in the GalFer Contest.
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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 LearningBrief- 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.
- 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".
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News & Events
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NEWS MERL Presents 7 Papers and 2 Workshops at CVPR 2026 Date: June 3, 2026 - June 7, 2026
Where: Colorado Convention Center, Denver, Colorado
MERL Contacts: Moitreya Chatterjee; Anoop Cherian; Kaen Kogashi; Suhas Lohit; Lalit Manam; Tim K. Marks; Pedro Miraldo; Kuan-Chuan Peng
Research Areas: Artificial Intelligence, Computer Vision, Machine LearningBrief- MERL researchers are proud to present 7 papers, including two highlight papers (top 3.6% of submissions), and 2 workshops at CVPR 2026. CVPR, taking place from June 3-7 in Denver, CO, USA, is a premier international conference in computer vision.
Papers with MERL Authors:
1. Point4Cast: Streaming Dynamic Scene Reconstruction and Forecasting by Xinhang Liu, Pedro Miraldo, Suhas Lohit, Huaizu Jiang, Naoko Sawada, Yu-Wing Tai, Chi-Keung Tang, and Moitreya Chatterjee (Highlight Paper)
2. Parallel Rigidity Matters for Bundle Adjustment by Lalit Manam and Venu Govindu (Highlight Paper)
3. Revisiting Monocular SLAM with Spatio-Temporal Scene Modeling by Valter Piedade, Lalit Manam, Masashi Yamazaki, and Pedro Miraldo
4. AssemblyBench: Physics-Aware Assembly of Complex Industrial Objects by Danrui Li, Jiahao Zhang, Bernhard Egger, Moitreya Chatterjee, Suhas Lohit, Tim K. Marks, and Anoop Cherian
5. LASER: Layer-wise Scale Alignment for Training-Free Streaming 4D Reconstruction by Tianye Ding, Yiming Xie, Yiqing Liang, Moitreya Chatterjee, Pedro Miraldo, and Huaizu Jiang
6. SoREL: Soft-Label Refurbishment with Ensemble Learning for Noisy Long-Tailed Classification by Jun-Wei Hsieh, Ying-Hsuan Wu, Yi-Kuan Hsieh, Xin Li, Kuan-Chuan Peng, Ming-Ching Chang (CVPR Findings paper)
7. MMHOI: Complex 3D Multi-Human-Object Interaction Understanding by Kaen Kogashi and Anoop Cherian (PhysHuman Workshop paper)
Workshops Co-Organized by MERL:
1. Multimodal Algorithmic Reasoning Workshop by Anoop Cherian, Suhas Lohit, Kuan-Chuan Peng, Honglu Zhou, Kevin Smith, and Josh Tenenbaum
2. The Third Workshop on Anomaly Detection with Foundation Models by Kuan-Chuan Peng, Ying Zhao, and Abhishek Aich
- MERL researchers are proud to present 7 papers, including two highlight papers (top 3.6% of submissions), and 2 workshops at CVPR 2026. CVPR, taking place from June 3-7 in Denver, CO, USA, is a premier international conference in computer vision.
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EVENT MERL Contributes to ICASSP 2026 Date: Monday, May 4, 2026 - , May 8, 2026
Location: Barcelona, Spain
MERL Contacts: Wael H. Ali; Petros T. Boufounos; Chiori Hori; Jonathan Le Roux; Yanting Ma; Hassan Mansour; Yoshiki Masuyama; Joshua Rapp; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern
Research Areas: Artificial Intelligence, Computational Sensing, Computer Vision, Machine Learning, Optimization, Signal Processing, Speech & AudioBrief- MERL has made numerous contributions to both the organization and technical program of ICASSP 2026, which is being held in Barcelona, Spain from May 4-8, 2026.
Sponsorship
MERL is proud to be a Silver Patron of the conference and will participate in the student job fair on Thursday, May 7. Please join this session to learn more about employment opportunities at MERL, including openings for research scientists, post-docs, and interns. MERL Distinguished Research Scientists Petros T. Boufounos and Jonathan Le Roux will also present a spotlight session on MERL’s research in signal processing on Tuesday, May 5 at 13:05. Finally, MERL will sponsor a photo booth on Thursday, May 7 and Friday, May 8, where ICASSP participants can take professional photos with friends and colleagues, which will be emailed to them.
MERL is also pleased to be the sponsor of two IEEE Awards that will be presented at the conference. We congratulate Prof. Nasir Ahmed, the recipient of the 2026 IEEE Fourier Award for Signal Processing, and Dr. Alex Acero, the recipient of the 2026 IEEE James L. Flanagan Speech and Audio Processing Award.
Technical Program
MERL is presenting 8 papers in the main conference on a wide range of topics including source separation, spatial audio, neural audio codecs, radar-based pose estimation, camera-based airflow sensing, radar array processing, and optimization. Another paper on neural speech codecs will be presented at the Low-Resource Audio Codec (LRAC) Satellite Workshop. MERL researchers will also present two articles published in IEEE Open Journal of Signal Processing (OJSP) on music source separation and head-related transfer function (HRTF) modeling. Finally, Speech and Audio Team members Yoshiki Masuyama and Jonathan Le Roux co-organized a Special Session on Neural Spatial Audio Processing, which will feature six oral presentations.
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 attracts more than 4000 participants each year.
- MERL has made numerous contributions to both the organization and technical program of ICASSP 2026, which is being held in Barcelona, Spain from May 4-8, 2026.
See All News & Events for Machine Learning -
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Research Highlights
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Point4Cast: Streaming Dynamic Scene Reconstruction and Forecasting -
AssemblyBench: Physics-Aware Assembly of Complex Industrial Objects -
SLAM-MER: Revisiting Monocular SLAM with Spatio-Temporal Scene Modeling -
Parallel Rigidity Matters for Bundle Adjustment -
LLMPhy: Parameter-Identifiable Physical Reasoning Combining Large Language Models and Physics Engines -
SAC-GNC: SAmple Consensus for adaptive Graduated Non-Convexity -
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 -
Private, Secure, and Reliable Artificial Intelligence -
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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CV0075: Internship - Multimodal Embodied AI
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CV0225: Internship - Reconstruction/Novel View Synthesis of Dynamic Scenes
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SA0302: Internship - Audio Processing for Moving Sounds
See All Internships for Machine Learning -
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Openings
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MS0268: Research Scientist - Multiphysical Systems
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CI0177: Postdoctoral Research Fellow - Agentic AI
See All Openings at MERL -
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Recent Publications
- , "LLawCo: Learning Laws of Cooperation for Modeling Embodied Multi-Agent Behavior", International Conference on Machine Learning (ICML), June 2026.BibTeX TR2026-081 PDF
- @inproceedings{Zhou2026jun,
- author = {Zhou, Qinhong and Gan, Chuang and Cherian, Anoop},
- title = {{LLawCo: Learning Laws of Cooperation for Modeling Embodied Multi-Agent Behavior}},
- booktitle = {International Conference on Machine Learning (ICML)},
- year = 2026,
- month = jun,
- url = {https://www.merl.com/publications/TR2026-081}
- }
- , "Data-driven Spatial Classification using Multi-Arm Bandits for Monitoring with Energy-Constrained Mobile Robots", IEEE Transactions on Control Systems Technology, June 2026.BibTeX TR2026-078 PDF Video
- @article{Lin2026jun,
- author = {Lin, Xiaoshan and Nayak, Siddharth and {Di Cairano}, Stefano and Vinod, Abraham P.},
- title = {{Data-driven Spatial Classification using Multi-Arm Bandits for Monitoring with Energy-Constrained Mobile Robots}},
- journal = {IEEE Transactions on Control Systems Technology},
- year = 2026,
- month = jun,
- url = {https://www.merl.com/publications/TR2026-078}
- }
- , "SoREL: Soft-Label Refurbishment with Ensemble Learning for Noisy Long-Tailed Classification", CVPR Findings, June 2026.BibTeX TR2026-075 PDF
- @inproceedings{Hsieh2026jun2,
- author = {Hsieh, Jun-Wei and Wu, Ying-Hsuan and Hsieh, Yi-Kuan and Li, Xin and Peng, Kuan-Chuan and Chang, Ming-Ching},
- title = {{SoREL: Soft-Label Refurbishment with Ensemble Learning for Noisy Long-Tailed Classification}},
- booktitle = {CVPR Findings},
- year = 2026,
- month = jun,
- url = {https://www.merl.com/publications/TR2026-075}
- }
- , "SoREL: Soft-Label Refurbishment with Ensemble Learning for Noisy Long-Tailed Classification Supplementary Material", CVPR Findings, June 2026.BibTeX TR2026-074 PDF
- @inproceedings{Hsieh2026jun,
- author = {Hsieh, Jun-Wei and Wu, Ying-Hsuan and Hsieh, Yi-Kuan and Li, Xin and Peng, Kuan-Chuan and Chang, Ming-Ching},
- title = {{SoREL: Soft-Label Refurbishment with Ensemble Learning for Noisy Long-Tailed Classification Supplementary Material}},
- booktitle = {CVPR Findings},
- year = 2026,
- month = jun,
- url = {https://www.merl.com/publications/TR2026-074}
- }
- , "AssemblyBench: Physics-Aware Assembly of Complex Industrial Objects", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2026.BibTeX TR2026-076 PDF Video Data Software
- @inproceedings{Li2026jun,
- author = {Li, Danrui and Zhang, Jiahao and Egger, Bernhard and Chatterjee, Moitreya and Lohit, Suhas and Marks, Tim K. and Cherian, Anoop},
- title = {{AssemblyBench: Physics-Aware Assembly of Complex Industrial Objects}},
- booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
- year = 2026,
- month = jun,
- url = {https://www.merl.com/publications/TR2026-076}
- }
- , "Point4Cast: Streaming Dynamic Scene Reconstruction and Forecasting", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2026.BibTeX TR2026-077 PDF
- @inproceedings{Liu2026jun,
- author = {Liu, Xinhang and Miraldo, Pedro and Lohit, Suhas and Jiang, Huaizu and Sawada, Naoko and Tai, Yu-Wing and Tang, Chi-Keung and Chatterjee, Moitreya},
- title = {{Point4Cast: Streaming Dynamic Scene Reconstruction and Forecasting}},
- booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
- year = 2026,
- month = jun,
- url = {https://www.merl.com/publications/TR2026-077}
- }
- , "Safe Whole-Body Loco-Manipulation via Combined Model and Learning-based Control", IEEE International Conference on Robotics and Automation (ICRA), June 2026.BibTeX TR2026-072 PDF Video
- @inproceedings{Schperberg2026jun,
- author = {Schperberg, Alexander and Wang, Yeping and {Di Cairano}, Stefano},
- title = {{Safe Whole-Body Loco-Manipulation via Combined Model and Learning-based Control}},
- booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
- year = 2026,
- month = jun,
- url = {https://www.merl.com/publications/TR2026-072}
- }
- , "Multi-Hop IoT Network Fault Detection Using Spatio-Temporal Graph Neural Network", IEEE International Conference on Communications Workshops (ICC), May 2026.BibTeX TR2026-059 PDF
- @inproceedings{Lakha2026may,
- author = {Lakha, Bishal and Guo, Jianlin and Parsons, Kieran and Sumi, Takenori and Nagai, Yukimasa and Serra, Edoardo},
- title = {{Multi-Hop IoT Network Fault Detection Using Spatio-Temporal Graph Neural Network}},
- booktitle = {IEEE International Conference on Communications Workshops (ICC)},
- year = 2026,
- month = may,
- url = {https://www.merl.com/publications/TR2026-059}
- }
- , "LLawCo: Learning Laws of Cooperation for Modeling Embodied Multi-Agent Behavior", International Conference on Machine Learning (ICML), June 2026.
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Videos
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Software & Data Downloads
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Physics-Aware Assembly of Complex Industrial Objects -
Mitsubishi Electric Research framework for visual SLAM -
Parameter-Identifiable Physical Reasoning Combining Large Language Models and Physics Engines -
MMHOI Dataset: Modeling Complex 3D Multi-Human Multi-Object Interactions -
Embracing Cacophony -
Subject- and Dataset-Aware Neural Field for HRTF Modeling -
Radar-based 3D Pose Estimation using Transformer -
Open Vocabulary Attribute Detection Dataset -
multi-view Radar object dEtection with 3D bounding boX diffusiOn -
SAmple Consensus for Adaptive Graduated Non-Convexity -
Long-Tailed Online Anomaly Detection dataset -
Group Representation Networks -
Stabilizing Subject Transfer in EEG Classification with Divergence Estimation -
Task-Aware Unified Source Separation -
Local Density-Based Anomaly Score Normalization for Domain Generalization -
Retrieval-Augmented Neural Field for HRTF Upsampling and Personalization -
ComplexVAD Dataset -
Self-Monitored Inference-Time INtervention for Generative Music Transformers -
MEL-PETs Defense for LLM Privacy Challenge -
MEL-PETs Joint-Context Attack for LLM Privacy Challenge -
Radar dEtection TRansformer -
Millimeter-wave Multi-View Radar Dataset -
Zero-Shot Image Conditioning for Text-to-Video Diffusion Models -
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 -
Learned Born Operator for Reflection Tomographic Imaging -
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 -
Generalization in Deep RL with a Robust Adaptation Module -
Understanding Dynamic Compute Allocation in Recurrent Transformers
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