Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Markov Decision Process (2)
- Natural language processing (2)
- Self-supervised learning (2)
- Semantic Parsing (2)
- Anytime algorithms (1)
-
- Artificial Intelligence (1)
- Automated Decision-Making (1)
- Automated Planning (1)
- Bayesian neural networks (1)
- Bipartite Independent Set query (1)
- COVID-19 (1)
- Case-based Reasoning (1)
- Causal Inference (1)
- Causal discovery (1)
- Clustering (1)
- Code use metrics (1)
- Commonsense reasoning (1)
- Cross-Lingual Information Retrieval (1)
- Cross-Market Information Retrieval (1)
- Domain Adaptation (1)
- Entity resolution (1)
- Evaluation (1)
- Exception recovery (1)
- Experimental Design (1)
- Few-shot learning (1)
- Genetic programming (1)
- Gleam (1)
- HIV (1)
- Hierarchical clustering (1)
- Intervention (1)
Articles 1 - 15 of 15
Full-Text Articles in Physical Sciences and Mathematics
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 …