Software-defined networking (SDN) and software-defined flash (SDF) have ...
Data compression and decompression have become vital components of big-d...
Graph Neural Networks (GNNs) are emerging as a powerful tool for learnin...
Graph neural networks (GNNs) have shown high potential for a variety of
...
Relational graph neural networks (RGNNs) are graph neural networks (GNNs...
We present a GPU solution for exact maximal clique enumeration (MCE) tha...
Assigning qualified, unbiased and interested reviewers to paper submissi...
A good speaker not only needs to be correct, but also has the ability to...
In this paper, we propose Descriptive Knowledge Graph (DKG) - an open an...
Graphics Processing Units (GPUs) have traditionally relied on the host C...
Dynamic parallelism on GPUs allows GPU threads to dynamically launch oth...
Graph Neural Networks (GNNs) have shown success in learning from
graph-s...
With the society's growing adoption of machine learning (ML) and deep
le...
Relations between entities can be represented by different instances, e....
We propose to measure fine-grained domain relevance - the degree that a ...
Counting k-cliques in a graph is an important problem in graph analysis ...
Graph Convolutional Networks (GCNs) are increasingly adopted in large-sc...
With the increasing adoption of graph neural networks (GNNs) in the mach...
COVID-19 has fundamentally disrupted the way we live. Government bodies,...
MPI derived datatypes are an abstraction that simplifies handling of
non...
We study the problem of concept induction in visual reasoning, i.e.,
ide...
High quality AI solutions require joint optimization of AI algorithms, s...
We introduce and study semantic capacity of terms. For example, the sema...
Existing FPGA-based DNN accelerators typically fall into two design
para...
We present a vision for the Erudite architecture that redefines the comp...
This paper presents GPU performance optimization and scaling results for...
Modern analytics and recommendation systems are increasingly based on gr...
High quality AI solutions require joint optimization of AI algorithms an...
Learning segmentation from synthetic data and adapting to real data can
...
We consider the problem of unsupervised domain adaptation for semantic
s...
Deep Learning (DL) innovations are being introduced at a rapid pace. How...
Machine Learning (ML) and Deep Learning (DL) innovations are being intro...
The wide adoption of deep neural networks has been accompanied by
ever-i...
The current Deep Learning (DL) landscape is fast-paced and is rife with
...
The past few years have seen a surge of applying Deep Learning (DL) mode...
The past few years have seen a surge of applying Deep Learning (DL) mode...
The past few years have seen a surge of applying Deep Learning (DL) mode...
The rapidly growing demands for powerful AI algorithms in many applicati...
As Deep Learning (DL) models have been increasingly used in latency-sens...
As Deep Learning (DL) models have been increasingly used in latency-sens...
Finding the right reviewers to assess the quality of conference submissi...
Developing object detection and tracking on resource-constrained embedde...
Multi-scale context module and single-stage encoder-decoder structure ar...
The world sees a proliferation of machine learning/deep learning (ML) mo...
There has been a rapid proliferation of machine learning/deep learning (...
Unlike traditional PCIe-based FPGA accelerators, heterogeneous SoC-FPGA
...
Developing artificial intelligence (AI) at the edge is always challengin...
This study began with a research project, called DISCvR, conducted at th...
Developing deep learning models for resource-constrained Internet-of-Thi...
An increasingly complex and diverse collection of Machine Learning(ML) m...