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Found 9 papers, 2 papers with code
Deep learning reveals the common spectrum underlying multiple brain disorders in youth and elders from brain functional networks
Brain disorders in the early and late life of humans potentially share pathological alterations in brain functions.
Distribution Estimation of Contaminated Data via DNN-based MoM-GANs
This paper studies the distribution estimation of contaminated data by the MoM-GAN method, which combines generative adversarial net (GAN) and median-of-mean (MoM) estimation.
Statistical Guarantees for Approximate Stationary Points of Simple Neural Networks
Since statistical guarantees for neural networks are usually restricted to global optima of intricate objective functions, it is not clear whether these theories really explain the performances of actual outputs of neural-network pipelines.
TSTG I: Single-Particle and Many-Body Hamiltonians and Hidden Non-local Symmetries of Trilayer Moiré Systems with and without Displacement Field
We derive the Hamiltonian for trilayer moir\'e systems with the Coulomb interaction projected onto the bands near the charge neutrality point.
Strongly Correlated Electrons Mesoscale and Nanoscale Physics Materials Science
Statistical Guarantees for Regularized Neural Networks
Neural networks have become standard tools in the analysis of data, but they lack comprehensive mathematical theories.
Tuning-free ridge estimators for high-dimensional generalized linear models
Ridge estimators regularize the squared Euclidean lengths of parameters.
Aggregating Knockoffs for False Discovery Rate Control with an Application to Gut Microbiome Data
We support our method both in theory and simulations, and we show that it can lead to new discoveries on microbiome data from the American Gut Project.
Methodology Quantitative Methods Applications
Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning
To this end, in this paper, we propose a novel end-to-end data-driven model based on Convolutional Neural Network (CNN), namely Person-Job Fit Neural Network (PJFNN), for matching a talent qualification to the requirements of a job.
Recruitment Market Trend Analysis with Sequential Latent Variable Models
To this end, in this paper, we propose a new research paradigm for recruitment market analysis by leveraging unsupervised learning techniques for automatically discovering recruitment market trends based on large-scale recruitment data.
