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Articles 1 - 14 of 14
Full-Text Articles in Physical Sciences and Mathematics
Reasoning About User Feedback Under Identity Uncertainty In Knowledge Base Construction, Ariel Kobren
Reasoning About User Feedback Under Identity Uncertainty In Knowledge Base Construction, Ariel Kobren
Doctoral Dissertations
Intelligent, automated systems that are intertwined with everyday life---such as Google Search and virtual assistants like Amazon’s Alexa or Apple’s Siri---are often powered in part by knowledge bases (KBs), i.e., structured data repositories of entities, their attributes, and the relationships among them. Despite a wealth of research focused on automated KB construction methods, KBs are inevitably imperfect, with errors stemming from various points in the construction pipeline. Making matters more challenging, new data is created daily and must be integrated with existing KBs so that they remain up-to-date. As the primary consumers of KBs, human users have tremendous potential to …
Understanding The Dynamic Visual World: From Motion To Semantics, Huaizu Jiang
Understanding The Dynamic Visual World: From Motion To Semantics, Huaizu Jiang
Doctoral Dissertations
We live in a dynamic world, which is continuously in motion. Perceiving and interpreting the dynamic surroundings is an essential capability for an intelligent agent. Human beings have the remarkable capability to learn from limited data, with partial or little annotation, in sharp contrast to computational perception models that rely on large-scale, manually labeled data. Reliance on strongly supervised models with manually labeled data inherently prohibits us from modeling the dynamic visual world, as manual annotations are tedious, expensive, and not scalable, especially if we would like to solve multiple scene understanding tasks at the same time. Even worse, in …
Deep Neural Networks For 3d Processing And High-Dimensional Filtering, Hang Su
Deep Neural Networks For 3d Processing And High-Dimensional Filtering, Hang Su
Doctoral Dissertations
Deep neural networks (DNN) have seen tremendous success in the past few years, advancing state of the art in many AI areas by significant margins. Part of the success can be attributed to the wide adoption of convolutional filters. These filters can effectively capture the invariance in data, leading to faster training and more compact representations, and at the same can leverage efficient parallel implementations on modern hardware. Since convolution operates on regularly structured grids, it is a particularly good fit for texts and images where there are inherent rigid 1D or 2D structures. However, extending DNNs to 3D or …
Learning From Irregularly-Sampled Time Series, Steven Cheng-Xian Li
Learning From Irregularly-Sampled Time Series, Steven Cheng-Xian Li
Doctoral Dissertations
Irregularly-sampled time series are characterized by non-uniform time intervals between successive measurements. Such time series naturally occur in application areas including climate science, ecology, biology, and medicine. Irregular sampling poses a great challenge for modeling this type of data as there can be substantial uncertainty about the values of the underlying temporal processes. Moreover, different time series are not necessarily synchronized or of the same length, which makes it difficult to deal with using standard machine learning methods that assume fixed-dimensional data spaces. The goal of this thesis is to develop scalable probabilistic tools for modeling a large collection of …
Integrating Recognition And Decision Making To Close The Interaction Loop For Autonomous Systems, Richard Freedman
Integrating Recognition And Decision Making To Close The Interaction Loop For Autonomous Systems, Richard Freedman
Doctoral Dissertations
Intelligent systems are becoming increasingly ubiquitous in daily life. Mobile devices are providing machine-generated support to users, robots are "coming out of their cages" in manufacturing to interact with co-workers, and cars with various degrees of self-driving capabilities operate amongst pedestrians and the driver. However, these interactive intelligent systems' effectiveness depends on their understanding and recognition of human activities and goals, as well as their responses to people in a timely manner. The average person does not follow instructions step-by-step or act in a formulaic manner, but instead varies the order of actions and timing when performing a given task. …
Improving Reinforcement Learning Techniques By Leveraging Prior Experience, Francisco M. Garcia
Improving Reinforcement Learning Techniques By Leveraging Prior Experience, Francisco M. Garcia
Doctoral Dissertations
In this dissertation we develop techniques to leverage prior knowledge for improving the learning speed of existing reinforcement learning (RL) algorithms. RL systems can be expensive to train, which limits its applicability when a large number of agents need to be trained to solve a large number of tasks; a situation that often occurs in industry and is often ignored in the RL literature. In this thesis, we develop three methods to leverage the experience obtained from solving a small number of tasks to improve an agent's ability to learn on new tasks the agent might face in the future. …
Improving Visual Recognition With Unlabeled Data, Aruni Roy Chowdhury
Improving Visual Recognition With Unlabeled Data, Aruni Roy Chowdhury
Doctoral Dissertations
The success of deep neural networks has resulted in computer vision systems that obtain high accuracy on a wide variety of tasks such as image classification, object detection, semantic segmentation, etc. However, most state-of-the-art vision systems are dependent upon large amounts of labeled training data, which is not a scalable solution in the long run. This work focuses on improving existing models for visual object recognition and detection without being dependent on such large-scale human-annotated data. We first show how large numbers of hard examples (cases where an existing model makes a mistake) can be obtained automatically from unlabeled video …
Intelligent Tutoring Systems, Pedagogical Agent Design, And Hispanic English Language Learners, Danielle Allessio
Intelligent Tutoring Systems, Pedagogical Agent Design, And Hispanic English Language Learners, Danielle Allessio
Doctoral Dissertations
According to the most recent data from the National Center of Education Statistics (NCES) there were approximately 5 million English Language Learners (ELLs) in the U.S. public schools in the Fall of 2016, representing about 10% of the student population (2019). Spanish is the primary language for most ELL students, by a large margin. As a group, ELLs have faced a deeply rooted and persistent math achievement gap (U.S. Department of Education, 2015). Despite research indicating that intelligent tutors and animated pedagogical agents enhance learning, many tutors are not designed with ELLs in mind. As a result, Hispanic ELL students …
Dynamic Composition Of Functions For Modular Learning, Clemens Gb Rosenbaum
Dynamic Composition Of Functions For Modular Learning, Clemens Gb Rosenbaum
Doctoral Dissertations
Compositionality is useful to reduce the complexity of machine learning models and increase their generalization capabilities, because new problems can be linked to the composition of existing solutions. Recent work has shown that compositional approaches can offer substantial benefits over a wide variety of tasks, from multi-task learning over visual question-answering to natural language inference, among others. A key variant is functional compositionality, where a meta-learner composes different (trainable) functions into complex machine learning models. In this thesis, I generalize existing approaches to functional compositionality under the umbrella of the routing paradigm, where trainable arbitrary functions are 'stacked' to form …
Higher-Order Representations For Visual Recognition, Tsung-Yu Lin
Higher-Order Representations For Visual Recognition, Tsung-Yu Lin
Doctoral Dissertations
In this thesis, we present a simple and effective architecture called Bilinear Convolutional Neural Networks (B-CNNs). These networks represent an image as a pooled outer product of features derived from two CNNs and capture localized feature interactions in a translationally invariant manner. B-CNNs generalize classical orderless texture-based image models such as bag-of-visual-words and Fisher vector representations. However, unlike prior work, they can be trained in an end-to-end manner. In the experiments, we demonstrate that these representations generalize well to novel domains by fine-tuning and achieve excellent results on fine-grained, texture and scene recognition tasks. The visualization of fine-tuned convolutional filters …
Learning Latent Characteristics Of Data And Models Using Item Response Theory, John P. Lalor
Learning Latent Characteristics Of Data And Models Using Item Response Theory, John P. Lalor
Doctoral Dissertations
A supervised machine learning model is trained with a large set of labeled training data, and evaluated on a smaller but still large set of test data. Especially with deep neural networks (DNNs), the complexity of the model requires that an extremely large data set is collected to prevent overfitting. It is often the case that these models do not take into account specific attributes of the training set examples, but instead treat each equally in the process of model training. This is due to the fact that it is difficult to model latent traits of individual examples at the …
Improving Face Clustering In Videos, Souyoung Jin
Improving Face Clustering In Videos, Souyoung Jin
Doctoral Dissertations
Human faces represent not only a challenging recognition problem for computer vision, but are also an important source of information about identity, intent, and state of mind. These properties make the analysis of faces important not just as algorithmic challenges, but as a gateway to developing computer vision methods that can better follow the intent and goals of human beings. In this thesis, we are interested in face clustering in videos. Given a raw video, with no caption or annotation, we want to group all detected faces by their identity. We address three problems in the area of face clustering …
Optimization And Training Of Generational Garbage Collectors, Nicholas Jacek
Optimization And Training Of Generational Garbage Collectors, Nicholas Jacek
Doctoral Dissertations
Garbage collectors are nearly ubiquitous in modern programming languages, and we want to minimize the cost they impose in terms of time and space. Generally, a collector waits until its space is full and then performs a collection to reclaim needed memory. However, this is not the only option; a collection could be performed early when some free space remains. For copying collectors, which are what we consider here, the system must traverse the graph of live objects and copy them, so the cost of a collection is proportional to the volume of objects that are live. Since this value …
Motion Segmentation - Segmentation Of Independently Moving Objects In Video, Pia Katalin Bideau
Motion Segmentation - Segmentation Of Independently Moving Objects In Video, Pia Katalin Bideau
Doctoral Dissertations
The ability to recognize motion is one of the most important functions of our visual system. Motion allows us both to recognize objects and to get a better understanding of the 3D world in which we are moving. Because of its importance, motion is used to answer a wide variety of fundamental questions in computer vision such as: (1) Which objects are moving independently in the world? (2) Which objects are close and which objects are far away? (3) How is the camera moving?
My work addresses the problem of moving object segmentation in unconstrained videos. I developed a probabilistic …