Chinmay Pendse | Searcharxiv

arXiv2026

Parameter-Efficient Deep Learning for Ultrasound-Based Human-Machine Interfaces

Ultrasound (US) has emerged as a promising modality for Human-Machine Interfaces (HMIs), with recent research efforts exploring its potential for Hand Pose Estimation (HPE). A reliable solution to this problem could introduce interfaces with simultaneous support for up to 23 degrees of freedom encompassing all hand and wrist kinematics, thereby allowing far richer and more intuitive interaction strategies. Despite these promising results, a systematic comparison of models, input modalities and training strategies is missing from the literature. Moreover, there is only one publicly available dataset, namely the Ultrasound Adaptive Prosthetic Control (Ultra-Pro) dataset, enabling reproducible benchmarking and iterative model development. In this paper, we compare the performance of six different deep learning models, selected based on diverse criteria, on this benchmark. We demonstrate that, by using a step learning rate scheduler and the envelope of the RF signals as input modality, our 4-layer deep UDACNN surpasses XceptionTime's performance by $2.28$ percentage points while featuring $87.52\%$ fewer parameters. This result ($77.72\%$) constitutes an absolute improvement of $0.88\%$ from previously reported baselines. According to our findings, the appropriate combination of model, preprocessing and training algorithm is crucial for optimizing HMI performance.

cs.HC↗

arXiv2025

Evaluating and Learning Robust Bandit Policies Under Uncertain Causal Mechanisms

Causal graphical models can encode large amounts structural knowledge, both from the background knowledge of domain experts and the structural knowledge discovered from randomized experiments or observational data. However, though we may know the general structure of causal relationships, we often do not know the exact causal mechanisms. In this work, we propose a causal multi-armed bandit evaluation and learning algorithm that can reason effectively despite uncertainty over conditional probability distributions. Further, we show how conditional independence testing can be used to choose variables for modeling. We find that the structural equation model (SEM) approach gives more accurate evaluations compared to traditional approaches, particularly as the range of possible causal mechanisms grows. Further, the SEM approach learns low-variance policies, and it learns an optimal policy, assuming the model is sufficiently well-specified. Traditional approaches can converge to local extrema or fail to converge at all.

cs.LG↗