The power and flexibility of Optimal Transport (OT) have pervaded a wide...
In this paper, we address the problem of unsupervised Domain Adaptation....
In this work, we seek to exploit the deep structure of multi-modal data ...
We introduce in this paper a new statistical perspective, exploiting the...
Based on its great successes in inference and denosing tasks, Dictionary...
We propose a deep structure encoder using the recently introduced Volter...
Robust Subspace Recovery (RoSuRe) algorithm was recently introduced as a...
With rapid development of socio-economics, the task of discovering funct...
On account of its many successes in inference tasks and denoising
applic...
The importance of inference in Machine Learning (ML) has led to an explo...
We investigate the widely encountered problem of detecting communities i...
We present a novel adversarial framework for training deep belief networ...
We propose a new approach to Generative Adversarial Networks (GANs) to
a...
Zero-shot learning (ZSL) which aims to recognize unseen object classes b...
Zero-shot learning (ZSL) has been widely researched and achieved a great...
This paper presents a data driven approach to multi-modal fusion, where
...
Discriminative Dictionary Learning (DL) methods have been widely advocat...
A discriminative structured analysis dictionary is proposed for the
clas...
We propose a computationally efficient and high-performance classificati...
Parametric approaches to Learning, such as deep learning (DL), are highl...
Deep dictionary learning seeks multiple dictionaries at different image
...
This paper presents a method of learning deep AND-OR Grammar (AOG) netwo...
We introduce the idea of Data Readiness Level (DRL) to measure the relat...
We propose a geometric model-free causality measurebased on multivariate...
In this paper, we propose a novel lower dimensional representation of a ...