The rapid growth of memory and computation requirements of large languag...
In recent years, blockchain technology has introduced decentralized fina...
How to get insights from relational data streams in a timely manner is a...
Vertical Federated Learning (VFL) is a crucial paradigm for training mac...
Graph dynamic random walks (GDRWs) have recently emerged as a powerful
p...
As various forms of fraud proliferate on Ethereum, it is imperative to
s...
To efficiently perform inference with neural networks, the underlying te...
As societal concerns on data privacy recently increase, we have witnesse...
As the pump-and-dump schemes (P Ds) proliferate in the cryptocurrency ...
Continuous subgraph matching (CSM) algorithms find the occurrences of a ...
The use of FPGAs for efficient graph processing has attracted significan...
Multi-tenant machine learning services have become emerging data-intensi...
The development of cloud infrastructures inspires the emergence of
cloud...
As random walk is a powerful tool in many graph processing, mining and
l...
As the privacy of machine learning has drawn increasing attention, feder...
In this paper, we propose ThundeRiNG, a resource-efficient and
high-thro...
FPGAs have become emerging computing infrastructures for accelerating
ap...
Federated learning enables multiple parties to collaboratively train a
m...
As large graph processing emerges, we observe a costly fork-processing
p...
We study the hop-constrained s-t path enumeration (HcPE) problem, which ...
Speech separation is an important problem in speech processing, which ta...
Machine learning services have been emerging in many data-intensive
appl...
Reachability query is a fundamental problem on graphs, which has been
ex...
Non-parallel many-to-many voice conversion is recently attract-ing huge
...
Non-Volatile Main Memories (NVMMs) have recently emerged as promising
te...
Federated learning enables multiple parties to collaboratively learn a m...
This paper presents and characterizes an Open Application Repository for...
Data stream processing systems (DSPSs) enable users to express and run s...
The Gradient Boosting Decision Tree (GBDT) is a popular machine learning...
Gradient Boosting Decision Trees (GBDTs) have become very successful in
...
Support Vector Machines (SVMs) can solve structured multi-output learnin...
Federated learning has been a hot research area in enabling the collabor...
Federated learning systems enable the collaborative training of machine
...
Transactional state management relieves users from managing state consis...
We introduce BriskStream, an in-memory data stream processing system (DS...
Graph is a well known data structure to represent the associated
relatio...
GPU (graphics processing unit) has been used for many data-intensive
app...
With the explosive increase of big data in industry and academic fields,...
Modern DRAM architectures allow a number of low-power states on individu...