A Survey on Nonconvex Regularization-Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning [PDF]
In the past decade, sparse and low-rank recovery has drawn much attention in many areas such as signal/image processing, statistics, bioinformatics, and machine learning.
Fei Wen +3 more
doaj +2 more sources
Flexagon: A Multi-dataflow Sparse-Sparse Matrix Multiplication Accelerator for Efficient DNN Processing [PDF]
Sparsity is a growing trend in modern DNN models. Existing Sparse-Sparse Matrix Multiplication (SpMSpM) accelerators are tailored to a particular SpMSpM dataflow (i.e., Inner Product, Outer Product or Gustavson's), which determines their overall ...
Francisco Munoz-Mart'inez +5 more
semanticscholar +1 more source
Energy-Efficient Cell-Free Massive MIMO Through Sparse Large-Scale Fading Processing [PDF]
Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception.
Shuaifei Chen +4 more
semanticscholar +1 more source
Active flow control such as the use of a plasma actuator has been gathering much attention. Its effectiveness in flow separation control has been investigated experimentally and numerically.1) However, the capability during highspeed airflow is limited ...
Naoki Kanda +4 more
semanticscholar +1 more source
VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking [PDF]
3D object detectors usually rely on hand-crafted proxies, e.g., anchors or centers, and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be densified and processed by dense prediction heads, which inevitably costs extra ...
Yukang Chen +4 more
semanticscholar +1 more source
Sparse multiscale gaussian process regression [PDF]
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their computations on a set of m basis functions that are the covariance function of the g.p. with one of its two inputs fixed. We generalise this for the case of Gaussian covariance function, by basing our computations on m Gaussian basis functions with ...
Walder, Christian +2 more
openaire +3 more sources
SPLATNet: Sparse Lattice Networks for Point Cloud Processing [PDF]
We present a network architecture for processing point clouds that directly operates on a collection of points represented as a sparse set of samples in a high-dimensional lattice.
Hang Su +6 more
semanticscholar +1 more source
Sparse Signal Processing for Grant-Free Massive Connectivity: A Future Paradigm for Random Access Protocols in the Internet of Things [PDF]
The next wave of wireless technologies will proliferate in connecting sensors, machines, and robots for myriad new applications, thereby creating the fabric for the Internet of Things (IoT).
Liang Liu +5 more
semanticscholar +1 more source
Sparse Modeling for Image and Vision Processing [PDF]
In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a ...
Ecole Normale Supérieure +9 more
core +6 more sources
Current Developments of Sparse Microwave Imaging
The sparse microwave imaging combines the sparse signal processing theory with radar imaging to obtain new theory, new system, and new methodology of microwave imaging.
Wu Yi-rong +5 more
doaj +1 more source

