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- Deep learning (3)
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Articles 1 - 17 of 17
Full-Text Articles in Physical Sciences and Mathematics
Audio-Driven Character Animation, Yang Zhou
Audio-Driven Character Animation, Yang Zhou
Doctoral Dissertations
Generating believable character animations is a fundamentally important problem in the field of computer graphics and computer vision. It also has a diverse set of applications ranging from entertainment (e.g., films, games), medicine (e.g., facial therapy and prosthetics), mixed reality, and education (e.g., language/speech training and cyber-assistants). All these applications are all empowered by the ability to model and animate characters convincingly (human or non-human). Existing key-framing or performance capture approaches used for creating animations, especially facial animations, are either laborious or hard to edit. In particular, producing expressive animations from input speech automatically remains an open challenge. In this …
Understanding Of Visual Domains Via The Lens Of Natural Language, Chenyun Wu
Understanding Of Visual Domains Via The Lens Of Natural Language, Chenyun Wu
Doctoral Dissertations
A joint understanding of vision and language can enable intelligent systems to perceive, act, and communicate with humans for a wide range of applications. For example, they can assist a human to navigate in an environment, edit the content of an image through natural language commands, or search through image collections using natural language queries. In this thesis, we aim to improve our understanding of visual domains through the lens of natural language. We specifically look into (1) images of categories within a fine-grained taxonomy such as species of birds or variants of aircraft, (2) images of textures that describe …
Learning From Limited Labeled Data For Visual Recognition, Jong-Chyi Su
Learning From Limited Labeled Data For Visual Recognition, Jong-Chyi Su
Doctoral Dissertations
Recent advances in computer vision are in part due to the widespread use of deep neural networks. However, training deep networks require enormous amounts of labeled data which can be a bottleneck. In this thesis, we propose several approaches to mitigate this in the context of modern deep networks and computer vision tasks. While transfer learning is an effective strategy for natural image tasks where large labeled datasets such as ImageNet are available, it is less effective for distant domains such as medical images and 3D shapes. Chapter 2 focuses on transfer learning from natural image representations to other modalities. …
Deep Learning Models For Irregularly Sampled And Incomplete Time Series, Satya Narayan Shukla
Deep Learning Models For Irregularly Sampled And Incomplete Time Series, Satya Narayan Shukla
Doctoral Dissertations
Irregularly sampled time series data arise naturally in many application domains including biology, ecology, climate science, astronomy, geology, finance, and health. Such data present fundamental challenges to many classical models from machine learning and statistics. The first challenge with modeling such data is the presence of variable time gaps between the observation time points. The second challenge is that the dimensionality of the inputs can be different for different data cases. This occurs naturally due to the fact that different data cases are likely to include different numbers of observations. The third challenge is that different irregularly sampled instances have …
Social Measurement And Causal Inference With Text, Katherine A. Keith
Social Measurement And Causal Inference With Text, Katherine A. Keith
Doctoral Dissertations
The digital age has dramatically increased access to large-scale collections of digitized text documents. These corpora include, for example, digital traces from social media, decades of archived news reports, and transcripts of spoken interactions in political, legal, and economic spheres. For social scientists, this new widespread data availability has potential for improved quantitative analysis of relationships between language use and human thought, actions, and societal structure. However, the large-scale nature of these collections means that traditional manual approaches to analyzing content are extremely costly and do not scale. Furthermore, incorporating unstructured text data into quantitative analysis is difficult due to …
3d Shape Understanding And Generation, Matheus Gadelha
3d Shape Understanding And Generation, Matheus Gadelha
Doctoral Dissertations
In recent years, Machine Learning techniques have revolutionized solutions to longstanding image-based problems, like image classification, generation, semantic segmentation, object detection and many others. However, if we want to be able to build agents that can successfully interact with the real world, those techniques need to be capable of reasoning about the world as it truly is: a tridimensional space. There are two main challenges while handling 3D information in machine learning models. First, it is not clear what is the best 3D representation. For images, convolutional neural networks (CNNs) operating on raster images yield the best results in virtually …
History Modeling For Conversational Information Retrieval, Chen Qu
History Modeling For Conversational Information Retrieval, Chen Qu
Doctoral Dissertations
Conversational search is an embodiment of an iterative and interactive approach to information retrieval (IR) that has been studied for decades. Due to the recent rise of intelligent personal assistants, such as Siri, Alexa, AliMe, Cortana, and Google Assistant, a growing part of the population is moving their information-seeking activities to voice- or text-based conversational interfaces. One of the major challenges of conversational search is to leverage the conversation history to understand and fulfill the users' information needs. In this dissertation work, we investigate history modeling approaches for conversational information retrieval. We start from history modeling for user intent prediction. …
Neural Approaches To Feedback In Information Retrieval, Keping Bi
Neural Approaches To Feedback In Information Retrieval, Keping Bi
Doctoral Dissertations
Relevance feedback on search results indicates users' search intent and preferences. Extensive studies have shown that incorporating relevance feedback (RF) on the top k (usually 10) ranked results significantly improves the performance of re-ranking. However, most existing research on user feedback focuses on words-based retrieval models. Recently, neural retrieval models have shown their efficacy in capturing relevance matching in retrieval but little research has been conducted on neural approaches to feedback. This leads us to study different aspects of feedback with neural approaches in the dissertation. RF techniques are seldom used in real search scenarios since they can require significant …
Pattern Formation And Phase Transition Of Connectivity In Two Dimensions, Arman Mohseni Kabir
Pattern Formation And Phase Transition Of Connectivity In Two Dimensions, Arman Mohseni Kabir
Doctoral Dissertations
This dissertation is devoted to the study and analysis of different types of emergent behavior in physical systems. Emergence is a phenomenon that has fascinated researchers from various fields of science and engineering. From the emergence of global pandemics to the formation of reaction-diffusion patterns, the main feature that connects all these diverse systems is the appearance of a complex global structure as a result of collective interactions of simple underlying components. This dissertation will focus on two types of emergence in physical systems: emergence of long-range connectivity in networks and emergence and analysis of complex patterns. The most prominent …
Safe And Practical Machine Learning, Stephen J. Giguere
Safe And Practical Machine Learning, Stephen J. Giguere
Doctoral Dissertations
As increasingly sensitive decision making problems become automated using models trained by machine learning algorithms, it is important for machine learning researchers to design training algorithms that provide assurance that the models they produce will be well behaved. While significant progress has been made toward designing safe machine learning algorithms, there are several obstacles that prevent these strategies from being useful in practice. In this defense, I will highlight two of these challenges, and provide methods and results demonstrating that they can be overcome. First, for many applications, the user must be able to easily specify general and potentially complex …
Geometric Representation Learning, Luke Vilnis
Geometric Representation Learning, Luke Vilnis
Doctoral Dissertations
Vector embedding models are a cornerstone of modern machine learning methods for knowledge representation and reasoning. These methods aim to turn semantic questions into geometric questions by learning representations of concepts and other domain objects in a lower-dimensional vector space. In that spirit, this work advocates for density- and region-based representation learning. Embedding domain elements as geometric objects beyond a single point enables us to naturally represent breadth and polysemy, make asymmetric comparisons, answer complex queries, and provides a strong inductive bias when labeled data is scarce. We present a model for word representation using Gaussian densities, enabling asymmetric entailment …
Concentration Inequalities In The Wild: Case Studies In Blockchain & Reinforcement Learning, A. Pinar Ozisik
Concentration Inequalities In The Wild: Case Studies In Blockchain & Reinforcement Learning, A. Pinar Ozisik
Doctoral Dissertations
Concentration inequalities (CIs) are a powerful tool that provide probability bounds on how a random variable deviates from its expectation. In this dissertation, first I describe a blockchain protocol that I have developed, called Graphene, which uses CIs to provide probabilistic guarantees on performance. Second, I analyze the extent to which CIs are robust when the assumptions they require are violated, using Reinforcement Learning (RL) as the domain. Graphene is a method for interactive set reconciliation among peers in blockchains and related distributed systems. Through the novel combination of a Bloom filter and an Invertible Bloom Lookup Table, Graphene uses …
Compact Representations Of Uncertainty In Clustering, Craig Stuart Greenberg
Compact Representations Of Uncertainty In Clustering, Craig Stuart Greenberg
Doctoral Dissertations
Flat clustering and hierarchical clustering are two fundamental tasks, often used to discover meaningful structures in data, such as subtypes of cancer, phylogenetic relationships, taxonomies of concepts, and cascades of particle decays in particle physics. When multiple clusterings of the data are possible, it is useful to represent uncertainty in clustering through various probabilistic quantities, such as the distribution over partitions or tree structures, and the marginal probabilities of subpartitions or subtrees. Many compact representations exist for structured prediction problems, enabling the efficient computation of probability distributions, e.g., a trellis structure and corresponding Forward-Backward algorithm for Markov models that model …
Utilizing Graph Structure For Machine Learning, Stefan Dernbach
Utilizing Graph Structure For Machine Learning, Stefan Dernbach
Doctoral Dissertations
The information age has led to an explosion in the size and availability of data. This data often exhibits graph-structure that is either explicitly defined, as in the web of a social network, or is implicitly defined and can be determined by measuring similarity between objects. Utilizing this graph-structure allows for the design of machine learning algorithms that reflect not only the attributes of individual objects but their relationships to every other object in the domain as well. This thesis investigates three machine learning problems and proposes novel methods that leverage the graph-structure inherent in the tasks. Quantum walk neural …
Neural Methods For Answer Passage Retrieval Over Sparse Collections, Daniel Cohen
Neural Methods For Answer Passage Retrieval Over Sparse Collections, Daniel Cohen
Doctoral Dissertations
Recent advances in machine learning have allowed information retrieval (IR) techniques to advance beyond the stage of handcrafting domain specific features. Specifically, deep neural models incorporate varying levels of features to learn whether a document answers the information need of a query. However, these neural models rely on a large number of parameters to successfully learn a relation between a query and a relevant document.
This reliance on a large number of parameters, combined with the current methods of optimization relying on small updates necessitates numerous samples to allow the neural model to converge on an effective relevance function. This …
Sociolinguistically Driven Approaches For Just Natural Language Processing, Su Lin Blodgett
Sociolinguistically Driven Approaches For Just Natural Language Processing, Su Lin Blodgett
Doctoral Dissertations
Natural language processing (NLP) systems are now ubiquitous. Yet the benefits of these language technologies do not accrue evenly to all users, and indeed they can be harmful; NLP systems reproduce stereotypes, prevent speakers of non-standard language varieties from participating fully in public discourse, and re-inscribe historical patterns of linguistic stigmatization and discrimination. How harms arise in NLP systems, and who is harmed by them, can only be understood at the intersection of work on NLP, fairness and justice in machine learning, and the relationships between language and social justice. In this thesis, we propose to address two questions at …
Quantifying The Impact Of Non-Stationarity In Reinforcement Learning-Based Traffic Signal Control, Lucas N. Alegre, Ana L.C. Bazzan, Bruno C. Da Silva
Quantifying The Impact Of Non-Stationarity In Reinforcement Learning-Based Traffic Signal Control, Lucas N. Alegre, Ana L.C. Bazzan, Bruno C. Da Silva
Computer Science Department Faculty Publication Series
In reinforcement learning (RL), dealing with non-stationarity is a challenging issue. However, some domains such as traffic optimization are inherently non-stationary. Causes for and effects of this are manifold. In particular, when dealing with traffic signal controls, addressing non-stationarity is key since traffic conditions change over time and as a function of traffic control decisions taken in other parts of a network. In this paper we analyze the effects that different sources of non-stationarity have in a network of traffic signals, in which each signal is modeled as a learning agent. More precisely, we study both the effects of changing …