Search Results for author:
Found 12 papers, 8 papers with code
Label Information Enhanced Fraud Detection against Low Homophily in Graphs
Then, we combine the group aggregation and the learnable encodings into a Transformer encoder to capture the semantic information.
Sparse and Low-Rank High-Order Tensor Regression via Parallel Proximal Method
Recently, tensor data (or multidimensional array) have been generated in many modern applications, such as functional magnetic resonance imaging (fMRI) in neuroscience and videos in video analysis.
Fast and Scalable Estimator for Sparse and Unit-Rank Higher-Order Regression Models
Because tensor data appear more and more frequently in various scientific researches and real-world applications, analyzing the relationship between tensor features and the univariate outcome becomes an elementary task in many fields.
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
We consider the problem of including additional knowledge in estimating sparse Gaussian graphical models (sGGMs) from aggregated samples, arising often in bioinformatics and neuroimaging applications.
Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure
We focus on the problem of estimating the change in the dependency structures of two $p$-dimensional Gaussian Graphical models (GGMs).
A Constrained, Weighted-L1 Minimization Approach for Joint Discovery of Heterogeneous Neural Connectivity Graphs
Determining functional brain connectivity is crucial to understanding the brain and neural differences underlying disorders such as autism.
GaKCo: a Fast GApped k-mer string Kernel using COunting
This is because current gk-SK uses a trie-based algorithm to calculate co-occurrence of mismatched substrings resulting in a time cost proportional to $O(\Sigma^{M})$.
DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples
By identifying and removing unnecessary features in a DNN model, DeepCloak limits the capacity an attacker can use generating adversarial samples and therefore increase the robustness against such inputs.
A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models
Estimating multiple sparse Gaussian Graphical Models (sGGMs) jointly for many related tasks (large $K$) under a high-dimensional (large $p$) situation is an important task.
A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Examples
Most machine learning classifiers, including deep neural networks, are vulnerable to adversarial examples.
Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks
In this paper, we propose a toolkit called the Deep Motif Dashboard (DeMo Dashboard) which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification.
A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models
Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several contexts.
