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Problem-solving approaches are an essential part of learning. Knowing how students approach solving problems can help instructors improve their instructional designs and effectively guide the learning process of students. We propose a natural language ...

Citizens and policy institutions increasingly express their concerns regarding the emerging challenges in the context of Artificial Intelligence (AI) and have concrete demands for the protection of human rights. In parallel, studies in the field of AI ...

As robots become increasingly prevalent in our communities, aligning the values motivating their behavior with human values is critical. However, it is often difficult or impossible for humans, both expert and non-expert, to enumerate values ...

Human-robot interaction is limited in large part by the challenge of writing correct specifications for robots. The research community wants alignment between humans' goals and robot behaviors, but this alignment is very hard to achieve. My research ...

AI curricula are being developed and tested in classrooms, but wider adoption is premised by teacher professional development and buy-in. When engaging in professional development, curricula are treated as set in stone, static and educators are prepared ...

Loop interchange is an important code optimization that improves data locality and extracts parallelism. While previous research in compilers has tried to automate the selection of which loops to interchange, existing methods have an important ...

This paper describes an AI Book Club as an innovative 20-hour professional development (PD) model designed to prepare teachers with AI content knowledge and an understanding of the ethical issues posed by bias in AI that are foundational to developing ...

We present Wav2Lip-Emotion, a video-to-video translation architecture that modifies facial expressions of emotion in videos of speakers. Previous work modifies emotion in images, uses a single image to produce a video with animated emotion, or puppets ...

Constrained by the limitations of learning toolkits engineered for other applications, such as those in image processing, many mesh-based learning algorithms employ data flows that would be atypical from the perspective of conventional geometry ...

Cutting-edge machine learning techniques often require millions of labeled data objects to train a robust model. Because relying on humans to supply such a huge number of labels is rarely practical, automated methods for label generation are needed. ...

To help facilitate play and learning, game-based educational activities often feature a computational agent as a co-player. Personalizing this agent's behavior to the student player is an active area of research, and prior work has demonstrated the ...

Multi-agent reinforcement learning (MARL) requires coordination to efficiently solve certain tasks. Fully centralized control is often infeasible in such domains due to the size of joint action spaces. Coordination graph based formalization allows ...

The impact of machine learning models on healthcare will depend on the degree of trust that healthcare professionals place in the predictions made by these models. In this paper, we present a method to provide individuals with clinical expertise with ...

The recent release of many Chest X-Ray datasets has prompted a lot of interest in radiology report generation. To date, this has been framed as an image captioning task, where the machine takes an RGB image as input and generates a 2-3 sentence summary ...

Differentiable rendering computes derivatives of the light transport equation with respect to arbitrary 3D scene parameters, and enables various applications in inverse rendering and machine learning. We present an unbiased and efficient differentiable ...

We present RoboGrammar, a fully automated approach for generating optimized robot structures to traverse given terrains. In this framework, we represent each robot design as a graph, and use a graph grammar to express possible arrangements of physical ...

With the continuous drive toward integrated circuits scaling, efficient yield analysis is becoming more crucial yet more challenging. In this paper, we propose a novel methodology for wafer map defect pattern classification using deep selective ...

Computer programs are increasingly being deployed in partially-observable environments. A partially observable environment is an environment whose state is not completely visible to the program, but from which the program receives partial observations. ...

Federated learning is a technique that enables distributed clients to collaboratively learn a shared machine learning model without sharing their training data. This reduces data privacy risks, however, privacy concerns still exist since it is possible ...

A comprehensive artificial intelligence system needs to not only perceive the environment with different “senses” (e.g., seeing and hearing) but also infer the world’s conditional (or even causal) relations and corresponding uncertainty. The past decade ...