Browsing Faculty of Arts and Sciences by Keyword "Computer science"
Now showing items 1-20 of 220
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A Calibrated Defocus Simulator for Research in Passive Ranging
(2022-05-23)Depth from defocus is a prominent area of computer vision research, yet unlike many other areas of the field, there is no dedicated dataset or general library for training and testing depth from defocus applications. As a ... -
A Computational Approach for Detailed Quantification of Mouse Parenting Behavior
(2022-05-23)A major aim of neuroscience is understanding the brain circuitry underlying observable behaviors. Recently, an increasing number of tools have allowed researchers to investigate brain circuitry in unprecedented ways. ... -
A Declarative Specification for Machine Learning Architectures
(2023-06-30)Going from the description of a model architecture in a figure to its implementation can be a fraught process. This work presents a markup language for specifying model architectures; an associated Python package used to ... -
A Framework for Rapid Active Learning in Resource-Constrained Environmental Sensing Domains
(2022-05-23)Recent advancements in deep convolutional neural networks have enabled accurate, efficient, and intelligent feature learning for a wide variety of classification tasks. However, there remains a research to practice gap ... -
A General Curriculum for Meta-Learning
(2022-05-23)Meta-learning is an effective method for few-shot learning, building transferable machine learning models that generalize to new contexts with relatively little new data. Given this limited data, a natural path to improve ... -
A Lighting-Invariant Approach to Local Shape from Shading
(2021-05-04)Shape from shading is a classical problem in computer vision, in which the depth field of an object or a scene is reconstructed from a pattern of intensities in an image. This can be thought of in some sense as the inverse ... -
A Market for Impact
(2023-06-30)There are two fundamental problems in philanthropy: 1) information aggregation, to allow for the broad assessment of the wide range of charities, and 2) incentive alignment, to better incentivize philanthropists to donate ... -
A Method for Fast Single-Network Uncertainty Estimation in Deep Learning Interatomic Potentials
(2023-06-30)Deep learning has emerged as a promising paradigm for predicting molecular and materials properties with high accuracy. A common shortcoming shared by deep learning neural networks, however, is that they only produce point ... -
A Multi-resolution Hard Attention Model to Select Regions of Interest on Whole Pathology Slide Images
(2022-05-23)With drastic improvements in the performance of neural networks and computer vision algorithms, deep learning-based imaging analysis models have been applied to a wide variety of fields. Along with this expansion, many ... -
A System for Applying Deep Reinforcement Learning to Soft Robotic Control
(2022-03-07)Soft robots offer a host of benefits over traditional rigid robots, including inherent compliance that lets them passively adapt to variable environments and operate safely around humans and fragile objects. However, ... -
Accelerating Markov chain Monte Carlo via parallel predictive prefetching
(2014-10-21)We present a general framework for accelerating a large class of widely used Markov chain Monte Carlo (MCMC) algorithms. This dissertation demonstrates that MCMC inference can be accelerated in a model of parallel computation ... -
Acoustic Source Separation, Contour Classification, and Trajectory Optimization
(2023-05-12)How can we distinguish individual birds singing in a dawn chorus, and thereby improve measurements of biodiversity to aid conservation efforts? What are the key differences between the walking patterns of an unimpaired ... -
Active Learning for Improved Damage Detection and Disaster Response
(2021-06-23)From the 2020 Western Wildfires to the 2010 Haiti earthquake, each year natural disasters cost the world thousands of lives lost, trillions of dollars in damage, and irreparable long- term harm to the communities they ... -
Activity Allocation in an Under-Resourced World: Toward Improving Engagement with Public Health Programs via Restless Bandits
(2023-08-22)Artificial intelligence (AI) tools are being developed widely within society to improve decision-making, especially in resource-constrained settings like public health. However, developing effective AI tools for public ... -
Actualizing Impact of AI in Public Health: Optimization of Scarce Health Intervention Resources in the Real World
(2023-07-31)While AI is assuming omnipresence today more than ever, its adoption is still limited in solving challenges pertaining to socially-critical problem domains such as in public health, especially among low-resource and ... -
Adapting Fairness-Intervention Algorithms to Missing Data
(2023-06-30)Missing values in real-world data pose a significant and unique challenge to algorithmic fairness. Different demographic groups may be unequally affected by missing data, and standard procedures for handling missing values ... -
Adapting Verified Compilation for Target-Language Errors
(2022-02-24)Verified compilers have the potential to greatly improve users’ trust in their code by providing machine-checked proofs of compiler correctness. In recent years they have become increasingly sophisticated and practical, ... -
Agent-Based Modeling for Optimal Economic Policy with Exogenous Shocks
(2021-06-17)My thesis explores the application of reinforcement learning and agent-based computational economics to the problem of optimal policy in an environment with exogenous shocks. I develop an agent-based general equilibrium ... -
AI Pricing Collusion: Multi-Agent Reinforcement Learning Algorithms in Bertrand Competition
(2021-06-03)As e-commerce and online shopping become more widespread, firms are starting to maximize profit by using artificial intelligence, or more specifically reinforcement learning, to price goods. Calvano et al. showed that in ... -
Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness-intervention Algorithms in Classification
(2023-06-30)Machine learning (ML) models can underperform on certain population groups due to choices made during model development and bias inherent in the data. We categorize sources of discrimination in the ML pipeline into two ...