In this paper, we measure the linear separability of hidden layer output...
The random forest (RF) algorithm has become a very popular prediction me...
Conditional inference on joint textual and visual clues is a multi-modal...
Training a Large Visual Language Model (LVLM) from scratch, like GPT-4, ...
Graph dynamic random walks (GDRWs) have recently emerged as a powerful
p...
In domains where agents interact strategically, game theory is applied w...
Spatiotemporal traffic data imputation is of great significance in
intel...
The problem of broad practical interest in spatiotemporal data analysis,...
Bidirectional Encoder Representations from Transformers or
BERT <cit.> h...
Influence maximization aims to select k most-influential vertices or see...
We solve a fundamental challenge in semiconductor IC design: the fast an...
Modern time series datasets are often high-dimensional, incomplete/spars...
The use of FPGAs for efficient graph processing has attracted significan...
Mining the latent intentions from large volumes of natural language inpu...
Mining the latent intentions from large volumes of natural language inpu...
In this paper, we propose ThundeRiNG, a resource-efficient and
high-thro...
FPGAs have become emerging computing infrastructures for accelerating
ap...
Spatiotemporal traffic time series (e.g., traffic volume/speed) collecte...
Missing value problem in spatiotemporal traffic data has long been a
cha...
Time series prediction has been a long-standing research topic and an
es...
Discovering patterns and detecting anomalies in individual travel behavi...
Sparsity and missing data problems are very common in spatiotemporal tra...
Large-scale and multidimensional spatiotemporal data sets are becoming
u...
In this paper, we present an integrated human-in-the-loop simulation par...
Graph is a well known data structure to represent the associated
relatio...
Convolutional Neural Networks(CNNs) are complex systems. They are traine...