The main theme of research in Bot Intelligence Group (BIG) is to develop robotic intelligence ranging from the low-level autonomy to the high-level cognitive abilities. We aim to develop robots that ...
Abstract: This paper proposes a compact and low-power mixed-signal approach for implementing a convolutional operator in the charge domain. The circuit integrates a voltage divider with selector ...
Abstract: Quantization is a popular way of increasing the speed and lowering the memory usage of Convolution Neural Networks (CNNs). When labelled training data is available, network weights and ...
This repository contains the implementation of AdaptConv for point cloud analysis. Adaptive Graph Convolution (AdaptConv) is a point cloud convolution operator presented in our ICCV2021 paper. If you ...
This is a PyTorch implementation of the spline-based convolution operator of SplineCNN, as described in our paper: Matthias Fey, Jan Eric Lenssen, Frank Weichert, Heinrich Müller: SplineCNN: Fast ...
A team at Stanford has shown that large language models can automatically generate highly efficient GPU kernels, sometimes outperforming the standard functions found in the popular machine learning ...
Introduction: Accurate measurement of myocardial blood flow (MBF) and flow reserve (MFR) in dynamic 13N-ammonia PET myocardial perfusion imaging (MPI) depends on effective motion correction (MC) of ...
Event-based cameras are bio-inspired vision sensors that mimic the sparse and asynchronous activation of the animal retina, offering advantages such as low latency and low computational load in ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results