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Articles 1 - 30 of 123
Full-Text Articles in Artificial Intelligence and Robotics
Extracting Dnn Architectures Via Runtime Profiling On Mobile Gpus, Dong Hyub Kim
Extracting Dnn Architectures Via Runtime Profiling On Mobile Gpus, Dong Hyub Kim
Masters Theses
Due to significant investment, research, and development efforts over the past decade, deep neural networks (DNNs) have achieved notable advancements in classification and regression domains. As a result, DNNs are considered valuable intellectual property for artificial intelligence providers. Prior work has demonstrated highly effective model extraction attacks which steal a DNN, dismantling the provider’s business model and paving the way for unethical or malicious activities, such as misuse of personal data, safety risks in critical systems, or spreading misinformation. This thesis explores the feasibility of model extraction attacks on mobile devices using aggregated runtime profiles as a side-channel to leak …
Policy Gradient Methods: Analysis, Misconceptions, And Improvements, Christopher P. Nota
Policy Gradient Methods: Analysis, Misconceptions, And Improvements, Christopher P. Nota
Doctoral Dissertations
Policy gradient methods are a class of reinforcement learning algorithms that optimize a parametric policy by maximizing an objective function that directly measures the performance of the policy. Despite being used in many high-profile applications of reinforcement learning, the conventional use of policy gradient methods in practice deviates from existing theory. This thesis presents a comprehensive mathematical analysis of policy gradient methods, uncovering misconceptions and suggesting novel solutions to improve their performance. We first demonstrate that the update rule used by most policy gradient methods does not correspond to the gradient of any objective function due to the way the …
Multi-Slam Systems For Fault-Tolerant Simultaneous Localization And Mapping, Samer Nashed
Multi-Slam Systems For Fault-Tolerant Simultaneous Localization And Mapping, Samer Nashed
Doctoral Dissertations
Mobile robots need accurate, high fidelity models of their operating environments in order to complete their tasks safely and efficiently. Generating these models is most often done via Simultaneous Localization and Mapping (SLAM), a paradigm where the robot alternatively estimates the most up-to-date model of the environment and its position relative to this model as it acquires new information from its sensors over time. Because robots operate in many different environments with different compute, memory, sensing, and form constraints, the nature and quality of information available to individual instances of different SLAM systems varies substantially. `One-size-fits-all' solutions are thus exceedingly …
Towards Robust Long-Form Text Generation Systems, Kalpesh Krishna
Towards Robust Long-Form Text Generation Systems, Kalpesh Krishna
Doctoral Dissertations
Text generation is an important emerging AI technology that has seen significant research advances in recent years. Due to its closeness to how humans communicate, mastering text generation technology can unlock several important applications such as intelligent chat-bots, creative writing assistance, or newer applications like task-agnostic few-shot learning. Most recently, the rapid scaling of large language models (LLMs) has resulted in systems like ChatGPT, capable of generating fluent, coherent and human-like text. However, despite their remarkable capabilities, LLMs still suffer from several limitations, particularly when generating long-form text. In particular, (1) long-form generated text is filled with factual inconsistencies to …
Quantifying And Enhancing The Security Of Federated Learning, Virat Vishnu Shejwalkar
Quantifying And Enhancing The Security Of Federated Learning, Virat Vishnu Shejwalkar
Doctoral Dissertations
Federated learning is an emerging distributed learning paradigm that allows multiple users to collaboratively train a joint machine learning model without having to share their private data with any third party. Due to many of its attractive properties, federated learning has received significant attention from academia as well as industry and now powers major applications, e.g., Google's Gboard and Assistant, Apple's Siri, Owkin's health diagnostics, etc. However, federated learning is yet to see widespread adoption due to a number of challenges. One such challenge is its susceptibility to poisoning by malicious users who aim to manipulate the joint machine learning …
Learning To See With Minimal Human Supervision, Zezhou Cheng
Learning To See With Minimal Human Supervision, Zezhou Cheng
Doctoral Dissertations
Deep learning has significantly advanced computer vision in the past decade, paving the way for practical applications such as facial recognition and autonomous driving. However, current techniques depend heavily on human supervision, limiting their broader deployment. This dissertation tackles this problem by introducing algorithms and theories to minimize human supervision in three key areas: data, annotations, and neural network architectures, in the context of various visual understanding tasks such as object detection, image restoration, and 3D generation. First, we present self-supervised learning algorithms to handle in-the-wild images and videos that traditionally require time-consuming manual curation and labeling. We demonstrate that …
Foundations Of Node Representation Learning, Sudhanshu Chanpuriya
Foundations Of Node Representation Learning, Sudhanshu Chanpuriya
Doctoral Dissertations
Low-dimensional node representations, also called node embeddings, are a cornerstone in the modeling and analysis of complex networks. In recent years, advances in deep learning have spurred development of novel neural network-inspired methods for learning node representations which have largely surpassed classical 'spectral' embeddings in performance. Yet little work asks the central questions of this thesis: Why do these novel deep methods outperform their classical predecessors, and what are their limitations? We pursue several paths to answering these questions. To further our understanding of deep embedding methods, we explore their relationship with spectral methods, which are better understood, and show …
Bayesian Structural Causal Inference With Probabilistic Programming, Sam A. Witty
Bayesian Structural Causal Inference With Probabilistic Programming, Sam A. Witty
Doctoral Dissertations
Reasoning about causal relationships is central to the human experience. This evokes a natural question in our pursuit of human-like artificial intelligence: how might we imbue intelligent systems with similar causal reasoning capabilities? Better yet, how might we imbue intelligent systems with the ability to learn cause and effect relationships from observation and experimentation? Unfortunately, reasoning about cause and effect requires more than just data: it also requires partial knowledge about data generating mechanisms. Given this need, our task then as computational scientists is to design data structures for representing partial causal knowledge, and algorithms for updating that knowledge in …
Effective And Efficient Transfer Learning In The Era Of Large Language Models, Tu Vu
Effective And Efficient Transfer Learning In The Era Of Large Language Models, Tu Vu
Doctoral Dissertations
Substantial progress has been made in the field of natural language processing (NLP) due to the advent of large language models (LLMs)—deep neural networks with millions or billions of parameters pre-trained on large amounts of unlabeled data. However, these models have common weaknesses, including degenerate performance in data-scarce scenarios, and substantial computational resource requirements. This thesis aims to develop methods to address these limitations for improved applicability and performance of LLMs in resource-constrained settings with limited data and/or computational resources. To address the need for labeled data in data-scarce scenarios, I present two methods, in Chapter 2 and Chapter 3, …
Graph Representation Learning With Box Embeddings, Dongxu Zhang
Graph Representation Learning With Box Embeddings, Dongxu Zhang
Doctoral Dissertations
Graphs are ubiquitous data structures, present in many machine-learning tasks, such as link prediction of products and node classification of scientific papers. As gradient descent drives the training of most modern machine learning architectures, the ability to encode graph-structured data using a differentiable representation is essential to make use of this data. Most approaches encode graph structure in Euclidean space, however, it is non-trivial to model directed edges. The naive solution is to represent each node using a separate "source" and "target" vector, however, this can decouple the representation, making it harder for the model to capture information within longer …
An Introspective Approach For Competence-Aware Autonomy, Connor Basich
An Introspective Approach For Competence-Aware Autonomy, Connor Basich
Doctoral Dissertations
Building and deploying autonomous systems in the open world has long been a goal of both the artificial intelligence (AI) and robotics communities. From autonomous driving, to health care, to office assistance, these systems have the potential to transform society and alter our everyday lives. The open world, however, presents numerous challenges that question the typical assumptions made by the models and frameworks often used in contemporary AI and robotics. Systems in the open world are faced with an unconstrained and non-stationary environment with a range of heterogeneous actors that is too complex to be modeled in its entirety. Moreover, …
Rigorous Experimentation For Reinforcement Learning, Scott M. Jordan
Rigorous Experimentation For Reinforcement Learning, Scott M. Jordan
Doctoral Dissertations
Scientific fields make advancements by leveraging the knowledge created by others to push the boundary of understanding. The primary tool in many fields for generating knowledge is empirical experimentation. Although common, generating accurate knowledge from empirical experiments is often challenging due to inherent randomness in execution and confounding variables that can obscure the correct interpretation of the results. As such, researchers must hold themselves and others to a high degree of rigor when designing experiments. Unfortunately, most reinforcement learning (RL) experiments lack this rigor, making the knowledge generated from experiments dubious. This dissertation proposes methods to address central issues in …
Learning From Sequential User Data: Models And Sample-Efficient Algorithms, Aritra Ghosh
Learning From Sequential User Data: Models And Sample-Efficient Algorithms, Aritra Ghosh
Doctoral Dissertations
Recent advances in deep learning have made learning representation from ever-growing datasets possible in the domain of vision, natural language processing (NLP), and robotics, among others. However, deep networks are notoriously data-hungry; for example, training language models with attention mechanisms sometimes requires trillions of parameters and tokens. In contrast, we can often access a limited number of samples in many tasks. It is crucial to learn models from these `limited' datasets. Learning with limited datasets can take several forms. In this thesis, we study how to select data samples sequentially such that downstream task performance is maximized. Moreover, we study …
Labeled Modules In Programs That Evolve, Anil K. Saini
Labeled Modules In Programs That Evolve, Anil K. Saini
Doctoral Dissertations
Multiple methods have been developed for Inductive Program Synthesis, i.e., synthesizing programs consistent with a set of input-output examples. One such method is genetic programming, which searches for programs with desirable properties from the space of all possible programs through an iterated process of variation and selection that is inspired by natural evolution. Genetic programming has been successful in solving problems from multiple domains. These problems are often challenging because of the range of data types and control structures they require to be solved. Nonetheless, there are many programming problems that are routinely solved by human programmers that cannot be …
Low Resource Language Understanding In Voice Assistants, Subendhu Rongali
Low Resource Language Understanding In Voice Assistants, Subendhu Rongali
Doctoral Dissertations
Voice assistants such as Amazon Alexa, Apple Siri, and Google Assistant have become ubiquitous. They rely on spoken language understanding, which typically consists of an Automatic Speech Recognition (ASR) component and a Natural Language Understanding (NLU) component. ASR takes user speech as input and generates a text transcription. NLU takes the text transcription as input and generates a semantic parse to identify the requested actions, called intents (play music, turn on lights, etc.) and any relevant entities, called slots (which song to play? which lights to turn on?).
These components require massive amounts of training data to achieve good performance. …
Neural Approaches For Language-Agnostic Search And Recommendation, Hamed Rezanejad Asl Bonab
Neural Approaches For Language-Agnostic Search And Recommendation, Hamed Rezanejad Asl Bonab
Doctoral Dissertations
There are significant efforts toward developing better neural approaches for information retrieval problems. However, the vast majority of these studies are conducted using English-only data. In fact, trends and statistics of non-English content and users on the Internet show exponential growth and that novel information retrieval systems need to be language-agnostic; they need to bridge the language barrier between users and content, leverage data from high-resource settings for lower-resourced settings, and be able to extend to new languages and local markets easily. To this end, we focus on search and recommendation as two vital components of information systems. We explore …
Answer Similarity Grouping And Diversification In Question Answering Systems, Lakshmi Nair Vikraman
Answer Similarity Grouping And Diversification In Question Answering Systems, Lakshmi Nair Vikraman
Doctoral Dissertations
The rise in popularity of mobile and voice search has led to a shift in IR from document to passage retrieval for non-factoid questions. Various datasets such as MSMarco, as well as efficient retrieval models have been developed to identify single best answer passages for this task. However, such models do not specifically address questions which could have multiple or alternative answers. In this dissertation, we focus on this new research area that involves studying answer passage relationships and how this could be applied to passage retrieval tasks. We first create a high quality dataset for the answer passage similarity …
Approximate Bayesian Deep Learning For Resource-Constrained Environments, Meet Prakash Vadera
Approximate Bayesian Deep Learning For Resource-Constrained Environments, Meet Prakash Vadera
Doctoral Dissertations
Deep learning models have shown promising results in areas including computer vision, natural language processing, speech recognition, and more. However, existing point estimation-based training methods for these models may result in predictive uncertainties that are not well calibrated, including the occurrence of confident errors. Approximate Bayesian inference methods can help address these issues in a principled way by accounting for uncertainty in model parameters. However, these methods are computationally expensive both when computing approximations to the parameter posterior and when using an approximate parameter posterior to make predictions. They can also require significantly more storage than point-estimated models. In this …
Controllable Neural Synthesis For Natural Images And Vector Art, Difan Liu
Controllable Neural Synthesis For Natural Images And Vector Art, Difan Liu
Doctoral Dissertations
Neural image synthesis approaches have become increasingly popular over the last years due to their ability to generate photorealistic images useful for several applications, such as digital entertainment, mixed reality, synthetic dataset creation, computer art, to name a few. Despite the progress over the last years, current approaches lack two important aspects: (a) they often fail to capture long-range interactions in the image, and as a result, they fail to generate scenes with complex dependencies between their different objects or parts. (b) they often ignore the underlying 3D geometry of the shape/scene in the image, and as a result, they …
Probabilistic Commonsense Knowledge, Xiang Li
Probabilistic Commonsense Knowledge, Xiang Li
Doctoral Dissertations
Commonsense knowledge is critical to achieving artificial general intelligence. This shared common background knowledge is implicit in all human communication, facilitating efficient information exchange and understanding. But commonsense research is hampered by its immense quantity of knowledge because an explicit categorization is impossible. Furthermore, a plumber could repair a sink in a kitchen or a bathroom, indicating that common sense reveals a probable assumption rather than a definitive answer. To align with these properties of commonsense fundamentally, we want to not only model but also evaluate such knowledge human-like using abstractions and probabilistic principles. Traditional combinatorial probabilistic models, e.g., probabilistic …
Modeling The Multi-Mode Distribution In Self-Supervised Language Models, Haw-Shiuan Chang
Modeling The Multi-Mode Distribution In Self-Supervised Language Models, Haw-Shiuan Chang
Doctoral Dissertations
Self-supervised large language models (LMs) have become a highly-influential and foundational tool for many NLP models. For this reason, their expressivity is an important topic of study. In near-universal practice, given the language context, the model predicts a word from the vocabulary using a single embedded vector representation of both context and dictionary entries. Note that the context sometimes implies that the distribution over predicted words should be multi-modal in embedded space. However, the context’s single-vector representation provably fails to capture such a distribution. To address this limitation, we propose to represent context with multiple vector embeddings, which we term …
Combinatorial Algorithms For Graph Discovery And Experimental Design, Raghavendra K. Addanki
Combinatorial Algorithms For Graph Discovery And Experimental Design, Raghavendra K. Addanki
Doctoral Dissertations
In this thesis, we study the design and analysis of algorithms for discovering the structure and properties of an unknown graph, with applications in two different domains: causal inference and sublinear graph algorithms. In both these domains, graph discovery is possible using restricted forms of experiments, and our objective is to design low-cost experiments. First, we describe efficient experimental approaches to the causal discovery problem, which in its simplest form, asks us to identify the causal relations (edges of the unknown graph) between variables (vertices of the unknown graph) of a given system. For causal discovery, we study algorithms …
Nonparametric Contextual Reasoning For Question Answering Over Large Knowledge Bases, Rajarshi Das
Nonparametric Contextual Reasoning For Question Answering Over Large Knowledge Bases, Rajarshi Das
Doctoral Dissertations
Question answering (QA) over knowledge bases provides a user-friendly way of accessing the massive amount of information stored in them. We have experienced tremendous progress in the performance of QA systems, thanks to the recent advancements in representation learning by deep neural models. However, such deep models function as black boxes with an opaque reasoning process, are brittle, and offer very limited control (e.g. for debugging an erroneous model prediction). It is also unclear how to reliably add or update knowledge stored in their model parameters. This thesis proposes nonparametric models for question answering that disentangle logic from knowledge. For …
Metareasoning For Planning And Execution In Autonomous Systems, Justin Svegliato
Metareasoning For Planning And Execution In Autonomous Systems, Justin Svegliato
Doctoral Dissertations
Metareasoning is the process by which an autonomous system optimizes, specifically monitors and controls, its own planning and execution processes in order to operate more effectively in its environment. As autonomous systems rapidly grow in sophistication and autonomy, the need for metareasoning has become critical for efficient and reliable operation in noisy, stochastic, unstructured domains for long periods of time. This is due to the uncertainty over the limitations of their reasoning capabilities and the range of their potential circumstances. However, despite considerable progress in metareasoning as a whole over the last thirty years, work on metareasoning for planning relies …
Reliable Decision-Making With Imprecise Models, Sandhya Saisubramanian
Reliable Decision-Making With Imprecise Models, Sandhya Saisubramanian
Doctoral Dissertations
The rapid growth in the deployment of autonomous systems across various sectors has generated considerable interest in how these systems can operate reliably in large, stochastic, and unstructured environments. Despite recent advances in artificial intelligence and machine learning, it is challenging to assure that autonomous systems will operate reliably in the open world. One of the causes of unreliable behavior is the impreciseness of the model used for decision-making. Due to the practical challenges in data collection and precise model specification, autonomous systems often operate based on models that do not represent all the details in the environment. Even if …
Decision-Analytic Models Using Reinforcement Learning To Inform Dynamic Sequential Decisions In Public Policy, Seyedeh Nazanin Khatami
Decision-Analytic Models Using Reinforcement Learning To Inform Dynamic Sequential Decisions In Public Policy, Seyedeh Nazanin Khatami
Doctoral Dissertations
We developed decision-analytic models specifically suited for long-term sequential decision-making in the context of large-scale dynamic stochastic systems, focusing on public policy investment decisions. We found that while machine learning and artificial intelligence algorithms provide the most suitable frameworks for such analyses, multiple challenges arise in its successful adaptation. We address three specific challenges in two public sectors, public health and climate policy, through the following three essays. In Essay I, we developed a reinforcement learning (RL) model to identify optimal sequence of testing and retention-in-care interventions to inform the national strategic plan “Ending the HIV Epidemic in the US”. …
Incremental Non-Greedy Clustering At Scale, Nicholas Monath
Incremental Non-Greedy Clustering At Scale, Nicholas Monath
Doctoral Dissertations
Clustering is the task of organizing data into meaningful groups. Modern clustering applications such as entity resolution put several demands on clustering algorithms: (1) scalability to massive numbers of points as well as clusters, (2) incremental additions of data, (3) support for any user-specified similarity functions. Hierarchical clusterings are often desired as they represent multiple alternative flat clusterings (e.g., at different granularity levels). These tree-structured clusterings provide for both fine-grained clusters as well as uncertainty in the presence of newly arriving data. Previous work on hierarchical clustering does not fully address all three of the aforementioned desiderata. Work on incremental …
Few-Shot Natural Language Processing By Meta-Learning Without Labeled Data, Trapit Bansal
Few-Shot Natural Language Processing By Meta-Learning Without Labeled Data, Trapit Bansal
Doctoral Dissertations
Humans show a remarkable capability to accurately solve a wide range of problems efficiently -- utilizing a limited amount of computation and experience. Deep learning models, by stark contrast, can be trained to be highly accurate on a narrow task while being highly inefficient in terms of the amount of compute and data required to reach that accuracy. Within natural language processing (NLP), recent breakthroughs in unsupervised pretraining have enabled reusable models that can be applied to many NLP tasks, however, learning of new tasks is still inefficient. This has led to research on few-shot learning, where the goal is …
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 …