The article presents a new neural network structure called PPNet, designed for end-to-end near-optimal path planning. Traditional path planners, such as sampling-based ones, often have slow convergence to the optimal…
Continue readingThe article discusses a system that uses tactile information to estimate the rotation angle of a grasped object when slippage occurs during robotic manipulation tasks. The system uses a neural…
Continue readingThis research paper explores the use of connectionist techniques in phonetic speech recognition with strong latency constraints. The study’s aim is to enhance telephone conversation for hearing-impaired people by creating…
Continue readingThe authors discuss the increasing prevalence of neuro-symbolic programs, which combine traditional code with machine learning components, emphasizing the need for their verification. They identify the ’embedding gap’, a lack…
Continue readingThe paper introduces a new approach (CBNNTAP) that addresses the complexities and challenges presented by ocean currents when optimizing target assignment and motion planning for a multi-unmanned underwater vehicle (UUV)…
Continue readingThe article presents TRIPS (Trilinear Point Splatting), a novel approach for point-based radiance field rendering. It combines the techniques of Gaussian Splatting and ADOP to overcome their individual shortcomings and…
Continue readingThe article discusses a novel approach to enhance zero-shot multi-speaker text-to-speech (TTS) systems. These systems aim to replicate a chosen speaker’s voice without additional fine-tuning. However, they often struggle to…
Continue readingThe research paper presents a novel uncertainty modeling framework for self-explaining neural networks. The framework aims to enhance the interpretability of deep neural networks (DNNs), which are often seen as…
Continue readingThe article explores the generalization error curve of kernel regression methods. The authors provide a full characterization of these curves for kernel gradient descent and other spectral algorithms. They aim…
Continue readingThe article presents a new approach to geometric neural networks (GNNs), inspired by quantum physics. Traditional GNNs, which model physical systems as dynamic 3D many-body point clouds, are limited in…
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