Search Results for author:
Found 11 papers, 2 papers with code
AlzhiNet: Traversing from 2DCNN to 3DCNN, Towards Early Detection and Diagnosis of Alzheimer's Disease
Alzheimer's disease (AD) is a progressive neurodegenerative disorder with increasing prevalence among the aging population, necessitating early and accurate diagnosis for effective disease management.
Information Prebuilt Recurrent Reconstruction Network for Video Super-Resolution
By integrating sufficient information from the front of the video to build the hidden state needed for the initially recurrent unit to help restore the earlier frames, the information prebuilt network balances the input information difference at different time steps.
Convergent Policy Optimization for Safe Reinforcement Learning
We study the safe reinforcement learning problem with nonlinear function approximation, where policy optimization is formulated as a constrained optimization problem with both the objective and the constraint being nonconvex functions.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
An Improved Deep Belief Network Model for Road Safety Analyses
In this paper, we attempt to demonstrate the potential of this new model for crash prediction through two case studies including a collision data set from 800 km stretch of Highway 401 and other highways in Ontario, Canada.
Provable Gaussian Embedding with One Observation
In this paper, we study the Gaussian embedding model and develop the first theoretical results for exponential family embedding models.
Learning Influence-Receptivity Network Structure with Guarantee
Specifically, we endow each node with two node-topic vectors: an influence vector that measures how influential/authoritative they are on each topic; and a receptivity vector that measures how receptive/susceptible they are to each topic.
Simultaneous Parameter Learning and Bi-Clustering for Multi-Response Models
We consider multi-response and multitask regression models, where the parameter matrix to be estimated is expected to have an unknown grouping structure.
Recovery of simultaneous low rank and two-way sparse coefficient matrices, a nonconvex approach
We show linear convergence of the iterates obtained by GDT to a region within statistical error of an optimal solution.
Multitask Learning using Task Clustering with Applications to Predictive Modeling and GWAS of Plant Varieties
Inferring predictive maps between multiple input and multiple output variables or tasks has innumerable applications in data science.
Estimation of a Low-rank Topic-Based Model for Information Cascades
We consider the problem of estimating the latent structure of a social network based on the observed information diffusion events, or cascades, where the observations for a given cascade consist of only the timestamps of infection for infected nodes but not the source of the infection.
Statistical Inference for Pairwise Graphical Models Using Score Matching
As a result, there is a large body of literature focused on consistent model selection.
