research
∙
07/19/2023
Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar Datasets
Active learning improves the performance of machine learning methods by ...
share
research
∙
10/31/2022
Active Learning of Non-semantic Speech Tasks with Pretrained Models
Pretraining neural networks with massive unlabeled datasets has become p...
research
∙
04/19/2022
Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs
Learning neural ODEs often requires solving very stiff ODE systems, prim...
research
∙
03/31/2022
Graph-based Active Learning for Semi-supervised Classification of SAR Data
We present a novel method for classification of Synthetic Aperture Radar...
research
∙
12/12/2021
Efficient and Reliable Overlay Networks for Decentralized Federated Learning
We propose near-optimal overlay networks based on d-regular expander gra...
research
∙
10/14/2021
Model-Change Active Learning in Graph-Based Semi-Supervised Learning
Active learning in semi-supervised classification involves introducing a...
research
∙
10/10/2021
An Analysis of COVID-19 Knowledge Graph Construction and Applications
The construction and application of knowledge graphs have seen a rapid i...
research
∙
10/10/2021
Heavy Ball Neural Ordinary Differential Equations
We propose heavy ball neural ordinary differential equations (HBNODEs), ...
research
∙
05/22/2021
Post-Radiotherapy PET Image Outcome Prediction by Deep Learning under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application
This paper develops a method of biologically guided deep learning for po...
research
∙
07/25/2020
Posterior Consistency of Semi-Supervised Regression on Graphs
Graph-based semi-supervised regression (SSR) is the problem of estimatin...
research
∙
07/21/2020
Efficient Graph-Based Active Learning with Probit Likelihood via Gaussian Approximations
We present a novel adaptation of active learning to graph-based semi-sup...
research
∙
06/12/2020
MomentumRNN: Integrating Momentum into Recurrent Neural Networks
Designing deep neural networks is an art that often involves an expensiv...
research
∙
03/02/2020
Sparsity Meets Robustness: Channel Pruning for the Feynman-Kac Formalism Principled Robust Deep Neural Nets
Deep neural nets (DNNs) compression is crucial for adaptation to mobile ...
research
∙
02/24/2020
Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent
Stochastic gradient descent (SGD) with constant momentum and its variant...
research
∙
04/19/2019
Semi-Supervised First-Person Activity Recognition in Body-Worn Video
Body-worn cameras are now commonly used for logging daily life, sports, ...
research
∙
02/13/2019
A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting
We study epidemic forecasting on real-world health data by a graph-struc...
research
∙
11/15/2018
Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction
There is often latent network structure in spatial and temporal data and...
research
∙
09/23/2018
Adversarial Defense via Data Dependent Activation Function and Total Variation Minimization
We improve the robustness of deep neural nets to adversarial attacks by ...
research
∙
06/07/2018
Stochastic Block Models are a Discrete Surface Tension
Networks, which represent agents and interactions between them, arise in...
research
∙
04/02/2018
Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data
We present a generic framework for spatio-temporal (ST) data modeling, a...
research
∙
11/23/2017
Deep Learning for Real-Time Crime Forecasting and its Ternarization
Real-time crime forecasting is important. However, accurate prediction o...
research
∙
07/28/2017
Simplified Energy Landscape for Modularity Using Total Variation
Networks capture pairwise interactions between entities and are frequent...
research
∙
07/09/2017
Deep Learning for Real Time Crime Forecasting
Accurate real time crime prediction is a fundamental issue for public sa...
research
∙
03/26/2017
Uncertainty Quantification in the Classification of High Dimensional Data
Classification of high dimensional data finds wide-ranging applications....
research
∙
01/05/2017
Crime Topic Modeling
The classification of crime into discrete categories entails a massive l...
research
∙
02/15/2013
Multiclass Data Segmentation using Diffuse Interface Methods on Graphs
We present two graph-based algorithms for multiclass segmentation of hig...
research
∙
11/30/2012
