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Presenters often collect audience feedback through practice talks to refine their presentations. In formative interviews, we find that although text feedback and verbal discussions allow presenters to receive feedback, organizing that feedback into ...

Extreme Classification (XC) seeks to tag data points with the most relevant subset of labels from an extremely large label set. Performing deep XC with dense, learnt representations for data points and labels has attracted much attention due to its ...

Algorithms for mobile networking are increasingly being moved from centralized servers towards the edge in order to decrease latency and improve the user experience. While much of this work is traditionally done using ASICs, 6G emphasizes the ...

Weightless neural networks (WNNs) are a class of machine learning model which use table lookups to perform inference, rather than the multiply-accumulate operations typical of deep neural networks (DNNs). Individual weightless neurons are capable of ...

Object counting in images has been studied extensively, in particular using deep network models recently. The existing counting models typically output the point estimates of the object counts in given images. However, none of these can provide reliable ...

Pretrained language models have been shown to be effective in many software-related generation tasks; however, they are not well-suited for editing tasks as they are not designed to reason about edits. To address this, we propose a novel pretraining ...

Effectively balancing traffic in datacenter networks is a crucial operational goal. Most existing load balancing approaches are handcrafted to the structure of the network and/or network workloads. Thus, new load balancing strategies are required if ...

Knowledge-Based Visual Question Answering (KBVQA) is a bi-modal task requiring external world knowledge in order to correctly answer a text question and associated image. Recent single modality text work has shown knowledge injection into pre-trained ...

Self-supervision is recently surging at its new frontier of graph learning. It facilitates graph representations beneficial to downstream tasks; but its success could hinge on domain knowledge for handcraft or the often expensive trials and errors. Even ...

With the onset of the COVID-19 pandemic in the United States, "essential work" became a calling card for the labor that kept the country running. But the activity of essential workers often occurs out of sight. For example, the products of waste workers ...

Automatic scoring engines have been used for scoring approximately fifteen million test-takers in just the last three years. This number is increasing further due to COVID-19 and the associated automation of education and testing. Yet, the AI-based ...

This paper proposes a novel scalable image-based rendering (IBR) pipeline for indoor scenes with reflections. We make substantial progress towards three sub-problems in IBR, namely, depth and reflection reconstruction, view selection for temporally ...

Human gaze is known to be an intention-revealing signal in human demonstrations of tasks. In this work, we use gaze cues from human demonstrators to enhance the performance of agents trained via three popular imitation learning methods --- behavioral ...

In cooperative multi-agent reinforcement learning, a collection of agents learns to interact in a shared environment to achieve a common goal. We propose the use of reward machines (RM) -- Mealy machines used as structured representations of reward ...

Multi-agent reinforcement learning (MARL) has been increasingly used in a wide range of safety-critical applications, which require guaranteed safety (e.g., no unsafe states are ever visited) during the learning process.Unfortunately, current MARL ...

Traffic congestion is a major challenge in modern urban settings. The industry-wide development of autonomous and automated vehicles (AVs) motivates the question of how can AVs contribute to congestion reduction. Past research has shown that in small ...

Crowdsourcing is a valuable approach for tracking objects in videos in a more scalable manner than possible with domain experts. However, existing frameworks do not produce high quality results with non-expert crowdworkers, especially for scenarios ...

Acoustic ranging is a technique for estimating the distance between two objects using acoustic signals, which plays a critical role in many applications, such as motion tracking, gesture/activity recognition, and indoor localization. Although many ...

Shape deformation is an important component in any geometry processing toolbox. The goal is to enable intuitive deformations of single or multiple shapes or to transfer example deformations to new shapes while preserving the plausibility of the deformed ...

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 ...