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Found 10 papers, 1 papers with code
Motion-Invariant Variational Auto-Encoding of Brain Structural Connectomes
We applied the proposed model to data from the Adolescent Brain Cognitive Development (ABCD) study and the Human Connectome Project (HCP) to investigate how our motion-invariant connectomes facilitate understanding of the brain network and its relationship with cognition.
Robust Flow-based Conformal Inference (FCI) with Statistical Guarantee
Our approach is applicable and robust when the testing data is contaminated.
DVE: Dynamic Variational Embeddings with Applications in Recommender Systems
Embedding is a useful technique to project a high-dimensional feature into a low-dimensional space, and it has many successful applications including link prediction, node classification and natural language processing.
Exploration-Exploitation Motivated Variational Auto-Encoder for Recommender Systems
Recent years have witnessed rapid developments on collaborative filtering techniques for improving the performance of recommender systems due to the growing need of companies to help users discover new and relevant items.
Domain Adaptive Bootstrap Aggregating
When there is a distributional shift between data used to train a predictive algorithm and current data, performance can suffer.
Auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets
In this paper, building on recent advances in deep learning, we develop a nonlinear latent factor model to characterize the population distribution of brain graphs and infer the relationships between brain structural connectomes and human traits.
Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression
In this paper, we propose a random projection approach to estimate variance in kernel ridge regression.
Early Stopping for Nonparametric Testing
Early stopping of iterative algorithms is an algorithmic regularization method to avoid over-fitting in estimation and classification.
How Many Machines Can We Use in Parallel Computing for Kernel Ridge Regression?
It is worth noting that the upper bounds of the number of machines are proven to be un-improvable (upto a logarithmic factor) in two important cases: smoothing spline regression and Gaussian RKHS regression.
Nonparametric Testing under Random Projection
A common challenge in nonparametric inference is its high computational complexity when data volume is large.
