Generative processes that involve solving differential equations, such a...
As a promising technique for high-mobility wireless communications,
orth...
Motivated by the striking ability of transformers for in-context learnin...
Memory-safety bugs introduce critical software-security issues. Rust pro...
With the Deep Neural Networks (DNNs) as a powerful function approximator...
Fuzzy similarity join is an important database operator widely used in
p...
Conventional wisdom in the sampling literature, backed by a popular diff...
During a pandemic, contact tracing is an essential tool to drive down th...
Named entity recognition (NER) is a critical step in modern search query...
Deep neural networks (DNNs) demonstrate great success in classification
...
Spiking Neural Networks (SNNs) contain more biology-realistic structures...
Spiking Neural Networks (SNNs) have incorporated more biologically-plaus...
In this paper, we tackle the problem of answering multi-dimensional rang...
Knowledge-graph-based reasoning has drawn a lot of attention due to its
...
Answering complex questions involving multiple entities and relations is...
We prove quantitative convergence rates at which discrete Langevin-like
...
We formulate gradient-based Markov chain Monte Carlo (MCMC) sampling as
...
In this paper, we establish a generalization of the classical Central Li...
We study the problem of sampling from a distribution where the negative
...
The fifth generation (5G) wireless communications brag both high spectru...
We study the underdamped Langevin diffusion when the log of the target
d...
Langevin diffusion is a commonly used tool for sampling from a given
dis...
We consider first order gradient methods for effectively optimizing a
co...
Given a weighted graph with N vertices, consider a real-valued regressio...