Reinforcement learning defines the problem facing agents that learn to make good decisions through action and observation alone. To be effective problem solvers, such agents …
Humans are able to solve complex problems by distilling their knowledge of the world into simplified task-relevant representations and creating plans to achieve their goals. …
The efficacy of clinical trials hinges significantly on the process of patient selection, often marred by resource-intensive procedures and inherent inefficiencies. While randomized controlled trials …
Just as an interconnected-computerized world has produced large amounts of data resulting in exciting challenges for machine learning, connected households with robots and smart devices …
Anterior cruciate ligament (ACL) disruption is a common injury, particularly in the young and active patient. ACL reconstruction surgery is the current standard of care, …
Sleep apnea is a common, yet underdiagnosed, sleep-related breathing disorder associated with increased risk of cardiovascular issues. This work uses a multifaceted approach integrating artificial …
Designing Artificial Intelligence (AI) is still reserved for experts, and the existing design paradigm follows a data-driven approach: domain experts start with a hypothetical model, …
Coreference Resolution is a fundamental natural language processing (NLP) problem, as it attempts to resolve which underlying discourse objects refer to one another. Further, it …
Recent natural disasters, such as Hurricane Katrina in 2005 and the Japan earthquake in 2011, have demonstrated that situational awareness, the focus of much research …
Abstract of DISSOCIATED RESPONSES TO AI: LEGAL AI ADVISORS ARE PERSUASIVE BUT NOT TRUSTWORTHY?, by ZEYNEP AYDIN, Degree ScM., Brown University, MAY 2024. The rapid …
Solving combinatorial problems is an interplay between search and inference. In this thesis, we focus on search and investigate its important aspects. We start with …
Reinforcement learning (RL) techniques have led to remarkable results in challenging domains such as Atari games, Go, and Starcraft, suggesting that practical applications lie just …
Future collaborative robots must be capable of finding objects. As such a fundamental skill, we expect object search to eventually become an off-the-shelf capability for …
In this work, I present an implemented model that can learn interactively from natural language, enabling non-expert human trainers to convey complex tasks to machines …
The field of computer assisted radiology has recently seen an explosion of novel techniques that have been made possible due to advancements in computer algorithms. …
Indoor scenes and the shapes that comprise them play a central role in our daily experiences. As virtual 3D representations of these environments become increasingly …
In order to intuitively and efficiently collaborate with humans, robots must learn to complete tasks specified using natural language. Natural language instructions can have many …
Financial insecurity characterizes modern life. It reflects ongoing instability in employment and earnings which are notably pronounced in the gig economy. This instability is compounded …
