Search
Now showing items 1-6 of 6
Convex and Non-convex Optimization Methods for Machine Learning
(2019-08-01)
This dissertation is concerned with modeling fundamental and challenging machine learning tasks as convex/non-convex optimization problems and designing a mechanism that could solve them in a cost and time-effective manner. ...
UNSUPERVISED DOMAIN ADAPTATION WITH DEEP NEURAL NETWORKS
(2022-05-04)
Deep neural networks (DNNs) demonstrate unprecedented achievements on various machine learning problems and applications. However, such impressive performance heavily relies on massive amounts of labeled data which requires ...
DOMAIN ADAPTIVE TRANSFER LEARNING FOR VISUAL CLASSIFICATION
(2021-08-16)
Deep Neural Networks have made a significant impact on many computer vision applications with large-scale labeled datasets. However, in many applications, it is expensive and time-consuming to gather large-scale labeled ...
LEARNING TRANSFERABLE META-POLICIES FOR HIERARCHICAL TASK DECOMPOSITION AND PLANNING COMPOSITION
(2019-12-16)
In real world scenarios where situated agents are faced with dynamic, high-dimensional, partially observable environments with action and reward uncertainty, the traditional states space Reinforcement Learning (RL) becomes ...
Neural Image and Video Understanding
(2017-08-25)
Even though recent works on neural architectures have shown promising results at tasks like image recognition, object detection, playing Atari games, etc., learning a mapping from a visual space to a language space or vice ...
Classification of Alzheimer's Disease via Vision Transformer: Classification of Alzheimer's Disease via Vision Transformer
(ACM, 2022-07-11)
Deep models are powerful in capturing the complex and non-linear relationship buried in brain imaging data. However, the huge number of parameters in deep models can easily overfit given limited imaging data samples. In ...