Stephen Chou Abstract:Multidimensional arrays (tensors) are commonly used to represent data in many domains, including data analytics, machine learning, engineering, and the physical sciences. Many highly-optimized libraries and…
Tarfah Alrashed Abstract: Today, many websites offer third-party access to their data through web APIs. However, manually encoding URLs with arbitrary endpoints, parameters, authentication handshakes, and pagination, among other…
Yen-Ling Kuo Abstract: To understand environments effectively and to interact safely with humans, robots must generalize their learned models to scenarios they have never been trained on before,…
Tianxiao Shen Abstract: Language models have achieved success in a wide range of applications such as machine translation, dialogue generation, and writing autocompletion. However, typical models operate in…
Shaoxiong (Shawn) Wang Abstract:Towards helping people in daily life, robots need to better interact with our physical world and inevitably make contact with various objects. Touch provides contact…
Songtao He Abstract: Digital street maps with rich features are the foundation of many applications. However, creating and maintaining up-to-date digital maps often involve many labor-intensive tasks, making…
Vaikkunth Mugunthan Abstract:Machine learning models benefit from large and diverse training datasets. However, the sensitivity of the data and government regulations such as GDPR, HIPPA, and CCPA restrict…
Caris Moses Abstract: Manipulation tasks such as construction and assembly require reasoning over complex object interactions. In order for a robot to successfully plan for, execute, and achieve…
Alexander Amini Abstract:Because the physical world is complex, ambiguous, and unpredictable, autonomous agents must be engineered to exhibit a human-level degree of flexibility and generality — far beyond…
Rohan Chitnis Abstract: An autonomous agent should make good decisions quickly. These two considerations — effectiveness and efficiency — are especially important, and often competing, when an agent…