Search Results for author:
Found 11 papers, 3 papers with code
Multi-label Node Classification On Graph-Structured Data
As our second contribution, we define homophily and Cross-Class Neighborhood Similarity for the multi-label scenario and provide a thorough analyses of the collected $9$ multi-label datasets.
ECG Heartbeat classification using deep transfer learning with Convolutional Neural Network and STFT technique
In this paper, we propose a deep transfer learning framework that is aimed to perform classification on a small size training dataset.
Quarkyonic Matter Equation of State in Beta-Equilibrium
Quarkyonic stellar models are able to satisfy observed mass and radius constraints with a wide range of model parameters, avoiding the obligatory fine-tuning of conventional hybrid star models, including requiring the transition density to be very close to the nuclear saturation density.
High Energy Astrophysical Phenomena Nuclear Theory
Deep Multimodal Learning: An Effective Method for Video Classification
Our model combines both visual and audio information on both video and frame level and received great result.
PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation
Recently, 3D understanding research sheds light on extracting features from point cloud directly, which requires effective shape pattern description of point clouds.
Monocular Depth Estimation with Augmented Ordinal Depth Relationships
In this paper, we propose to improve the performance of metric depth estimation with relative depths collected from stereo movie videos using existing stereo matching algorithm.
Episodic Memory Deep Q-Networks
Reinforcement learning (RL) algorithms have made huge progress in recent years by leveraging the power of deep neural networks (DNN).
Relative Depth Order Estimation Using Multi-scale Densely Connected Convolutional Networks
We study the problem of estimating the relative depth order of point pairs in a monocular image.
A General Framework for Robust Testing and Confidence Regions in High-Dimensional Quantile Regression
Our de-biasing procedure does not require solving the $L_1$-penalized composite quantile regression.
A Likelihood Ratio Framework for High Dimensional Semiparametric Regression
(i) We develop a regularized statistical chromatography approach to infer the parameter of interest under the proposed semiparametric generalized linear model without the need of estimating the unknown base measure function.
Mode Estimation for High Dimensional Discrete Tree Graphical Models
Though the mode finding problem is generally intractable in high dimensions, this paper unveils that, if the distribution can be approximated well by a tree graphical model, mode characterization is significantly easier.
