Tootfinder

Fast Bilateral Teleoperation and Imitation Learning Using Sensorless Force Control via Accurate Dynamics Model
Koki Yamane, Yunhan Li, Masashi Konosu, Koki Inami, Junji Oaki, Sho Sakaino, Toshiaki Tsuji
https://arxiv.org/abs/2507.06174

Fast Bilateral Teleoperation and Imitation Learning Using Sensorless Force Control via Accurate Dynamics Model
In recent years, the advancement of imitation learning has led to increased interest in teleoperating low-cost manipulators to collect demonstration data. However, most existing systems rely on unilateral control, which only transmits target position values. While this approach is easy to implement and suitable for slow, non-contact tasks, it struggles with fast or contact-rich operations due to the absence of force feedback. This work demonstrates that fast teleoperation with force feedback is…

Causal Clustering for Conditional Average Treatment Effects Estimation and Subgroup Discovery
Zilong Wang, Turgay Ayer, Shihao Yang
https://arxiv.org/abs/2509.05775 https://

Causal Clustering for Conditional Average Treatment Effects Estimation and Subgroup Discovery
Estimating heterogeneous treatment effects is critical in domains such as personalized medicine, resource allocation, and policy evaluation. A central challenge lies in identifying subpopulations that respond differently to interventions, thereby enabling more targeted and effective decision-making. While clustering methods are well-studied in unsupervised learning, their integration with causal inference remains limited. We propose a novel framework that clusters individuals based on estimated…

Physics-Guided Dual Implicit Neural Representations for Source Separation
Yuan Ni, Zhantao Chen, Alexander N. Petsch, Edmund Xu, Cheng Peng, Alexander I. Kolesnikov, Sugata Chowdhury, Arun Bansil, Jana B. Thayer, Joshua J. Turner
https://arxiv.org/abs/2507.05249

Physics-Guided Dual Implicit Neural Representations for Source Separation
Significant challenges exist in efficient data analysis of most advanced experimental and observational techniques because the collected signals often include unwanted contributions--such as background and signal distortions--that can obscure the physically relevant information of interest. To address this, we have developed a self-supervised machine-learning approach for source separation using a dual implicit neural representation framework that jointly trains two neural networks: one for app…

Tesserae: Scalable Placement Policies for Deep Learning Workloads
Song Bian, Saurabh Agarwal, Md. Tareq Mahmood, Shivaram Venkataraman
https://arxiv.org/abs/2508.04953 https://

Tesserae: Scalable Placement Policies for Deep Learning Workloads
Training deep learning (DL) models has become a dominant workload in data-centers and improving resource utilization is a key goal of DL cluster schedulers. In order to do this, schedulers typically incorporate placement policies that govern where jobs are placed on the cluster. Existing placement policies are either designed as ad-hoc heuristics or incorporated as constraints within a complex optimization problem and thus either suffer from suboptimal performance or poor scalability. Our key i…

When Deep Learning Fails: Limitations of Recurrent Models on Stroke-Based Handwriting for Alzheimer's Disease Detection
Emanuele Nardone, Tiziana D'Alessandro, Francesco Fontanella, Claudio De Stefano
https://arxiv.org/abs/2508.03773

When Deep Learning Fails: Limitations of Recurrent Models on Stroke-Based Handwriting for Alzheimer's Disease Detection
Alzheimer's disease detection requires expensive neuroimaging or invasive procedures, limiting accessibility. This study explores whether deep learning can enable non-invasive Alzheimer's disease detection through handwriting analysis. Using a dataset of 34 distinct handwriting tasks collected from healthy controls and Alzheimer's disease patients, we evaluate and compare three recurrent neural architectures (LSTM, GRU, RNN) against traditional machine learning models. A crucial distinction of …

Contrastive clustering based on regular equivalence for influential node identification in complex networks
Yanmei Hu, Yihang Wu, Bing Sun, Xue Yue, Biao Cai, Xiangtao Li, Yang Chen
https://arxiv.org/abs/2509.02609

Contrastive clustering based on regular equivalence for influential node identification in complex networks
Identifying influential nodes in complex networks is a fundamental task in network analysis with wide-ranging applications across domains. While deep learning has advanced node influence detection, existing supervised approaches remain constrained by their reliance on labeled data, limiting their applicability in real-world scenarios where labels are scarce or unavailable. While contrastive learning demonstrates significant potential for performance enhancement, existing approaches predominantl…

Reinforcement Learning in MDPs with Information-Ordered Policies
Zhongjun Zhang, Shipra Agrawal, Ilan Lobel, Sean R. Sinclair, Christina Lee Yu
https://arxiv.org/abs/2508.03904 …

Reinforcement Learning in MDPs with Information-Ordered Policies
We propose an epoch-based reinforcement learning algorithm for infinite-horizon average-cost Markov decision processes (MDPs) that leverages a partial order over a policy class. In this structure, $π' \leq π$ if data collected under $π$ can be used to estimate the performance of $π'$, enabling counterfactual inference without additional environment interaction. Leveraging this partial order, we show that our algorithm achieves a regret bound of $O(\sqrt{w \log(|Θ|) T})$, where $w…

Q-Learning--Driven Adaptive Rewiring for Cooperative Control in Heterogeneous Networks
Yi-Ning Weng, Hsuan-Wei Lee
https://arxiv.org/abs/2509.01057 https://

Q-Learning--Driven Adaptive Rewiring for Cooperative Control in Heterogeneous Networks
Cooperation emergence in multi-agent systems represents a fundamental statistical physics problem where microscopic learning rules drive macroscopic collective behavior transitions. We propose a Q-learning-based variant of adaptive rewiring that builds on mechanisms studied in the literature. This method combines temporal difference learning with network restructuring so that agents can optimize strategies and social connections based on interaction histories. Through neighbor-specific Q-learni…

Decentralized Pliable Index Coding For Federated Learning In Intelligent Transportation Systems
Sadina Kadakkottiri, Narisetty Harish, Nujoom Sageer Karat, Deepthi Paramel Pattathil, Balaji Sundar Rajan
https://arxiv.org/abs/2507.00643

Decentralized Pliable Index Coding For Federated Learning In Intelligent Transportation Systems
Federated Learning is a promising option for data privacy and security in ITS, because it allows edge devices, Road Side Units (RSUs), and Central Server (CS) to jointly train the machine learning model. Since RSU collects data from the vehicles passing through its range, the local data of each RSU will have a non-IID distribution, which adversely affects the convergence speed and accuracy of FL training. Generating synthetic data locally at individual nodes, followed by data shuffling among th…

RotaTouille: Rotation Equivariant Deep Learning for Contours
Odin Hoff Gardaa, Nello Blaser
https://arxiv.org/abs/2508.16359 https://arxiv.org/pdf/2508.163…

RotaTouille: Rotation Equivariant Deep Learning for Contours
Contours or closed planar curves are common in many domains. For example, they appear as object boundaries in computer vision, isolines in meteorology, and the orbits of rotating machinery. In many cases when learning from contour data, planar rotations of the input will result in correspondingly rotated outputs. It is therefore desirable that deep learning models be rotationally equivariant. In addition, contours are typically represented as an ordered sequence of edge points, where the choice…