Less data, more knowledge: Building next generation semantic communication networks
Semantic communication is viewed as a revolutionary paradigm that can potentially
transform how we design and operate wireless communication systems. However, despite a …
transform how we design and operate wireless communication systems. However, despite a …
Learning linear causal representations from interventions under general nonlinear mixing
We study the problem of learning causal representations from unknown, latent interventions
in a general setting, where the latent distribution is Gaussian but the mixing function is …
in a general setting, where the latent distribution is Gaussian but the mixing function is …
Improving diffusion-based image synthesis with context prediction
Diffusion models are a new class of generative models, and have dramatically promoted
image generation with unprecedented quality and diversity. Existing diffusion models mainly …
image generation with unprecedented quality and diversity. Existing diffusion models mainly …
A tutorial on derivative-free policy learning methods for interpretable controller representations
This paper provides a tutorial overview of recent advances in learning control policy
representations for complex systems. We focus on control policies that are determined by …
representations for complex systems. We focus on control policies that are determined by …
Solving partial differential equations using deep learning and physical constraints
Y Guo, X Cao, B Liu, M Gao - Applied Sciences, 2020 - mdpi.com
The various studies of partial differential equations (PDEs) are hot topics of mathematical
research. Among them, solving PDEs is a very important and difficult task. Since many …
research. Among them, solving PDEs is a very important and difficult task. Since many …
Integration of programmable diffraction with digital neural networks
Optical imaging and sensing systems based on diffractive elements have seen massive
advances over the last several decades. Earlier generations of diffractive optical processors …
advances over the last several decades. Earlier generations of diffractive optical processors …
An error analysis of generative adversarial networks for learning distributions
This paper studies how well generative adversarial networks (GANs) learn probability
distributions from finite samples. Our main results establish the convergence rates of GANs …
distributions from finite samples. Our main results establish the convergence rates of GANs …
Estimation of the extent of the vulnerability of agriculture to climate change using analytical and deep-learning methods: a case study in Jammu, Kashmir, and Ladakh
Climate stress poses a threat to the agricultural sector, which is vital for both the economy
and livelihoods in general. Quantifying its risk to food security, livelihoods, and sustainability …
and livelihoods in general. Quantifying its risk to food security, livelihoods, and sustainability …
An optimal transport approach to estimating causal effects via nonlinear difference-in-differences
We propose a nonlinear difference-in-differences (DiD) method to estimate multivariate
counterfactual distributions in classical treatment and control study designs with …
counterfactual distributions in classical treatment and control study designs with …
A mathematical interpretation of autoregressive generative pre-trained transformer and self-supervised learning
M Lee - Mathematics, 2023 - mdpi.com
In this paper, we present a rigorous mathematical examination of generative pre-trained
transformer (GPT) models and their autoregressive self-supervised learning mechanisms …
transformer (GPT) models and their autoregressive self-supervised learning mechanisms …