Search Results - "Mooney, Raymond J."

Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog by Thomason, Jesse, Padmakumar, Aishwarya, Sinapov, Jivko, Walker, Nick, Jiang, Yuqian, Yedidsion, Harel, Hart, Justin, Stone, Peter, Mooney, Raymond

“…In this work, we present methods for using human-robot dialog to improve language understanding for a mobile robot agent. The agent parses natural language to…”
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Learning Deep Semantics for Test Completion by Nie, Pengyu, Banerjee, Rahul, Li, Junyi Jessy, Mooney, Raymond J., Gligoric, Milos

“…Writing tests is a time-consuming yet essential task during software development. We propose to leverage recent advances in deep learning for text and code…”
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Comparative experiments on learning information extractors for proteins and their interactions by Bunescu, Razvan, Ge, Ruifang, Kate, Rohit J., Marcotte, Edward M., Mooney, Raymond J., Ramani, Arun K., Wong, Yuk Wah

“…Automatically extracting information from biomedical text holds the promise of easily consolidating large amounts of biological knowledge in…”
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Consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome by Ramani, Arun K, Bunescu, Razvan C, Mooney, Raymond J, Marcotte, Edward M

“…Extensive protein interaction maps are being constructed for yeast, worm, and fly to ask how the proteins organize into pathways and systems, but no such…”
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Creating diversity in ensembles using artificial data by Melville, Prem, Mooney, Raymond J.

“…The diversity of an ensemble of classifiers is known to be an important factor in determining its generalization error. We present a new method for generating…”
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Guest Editors' Introduction: Machine Learning and Natural Language by Cardie, Claire, Mooney, Raymond J

“…In recent years, the application of machine learning techniques to natural language processing (NLP) has dramatically increased. However, most of this research…”
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Schema Acquisition From a Single Example by Ahn, Woo-kyoung, Brewer, William F, Mooney, Raymond J

“…This study compares Similarity-Based Learning (SBL) and Explanation-Based Learning (EBL) approaches to schema acquisition. In SBL approaches, concept formation…”
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Training a Multilingual Sportscaster: Using Perceptual Context to Learn Language by Chen, D. L., Kim, J., Mooney, R. J.

“…We present a novel framework for learning to interpret and generate language using only perceptual context as supervision. We demonstrate its capabilities by…”
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Entity-Focused Dense Passage Retrieval for Outside-Knowledge Visual Question Answering by Wu, Jialin, Mooney, Raymond J

“…Most Outside-Knowledge Visual Question Answering (OK-VQA) systems employ a two-stage framework that first retrieves external knowledge given the visual…”
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Using Both Demonstrations and Language Instructions to Efficiently Learn Robotic Tasks by Yu, Albert, Mooney, Raymond J

“…Demonstrations and natural language instructions are two common ways to specify and teach robots novel tasks. However, for many complex tasks, a demonstration…”
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Advantages of decision lists and implicit negatives in Inductive Logic Programming by Califf, Mary Elaine, Mooney, Raymond J.

“…This paper demonstrates the capabilities of FOIDL, an inductive logic programming (ILP) system whose distinguishing characteristics are the ability to produce…”
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Language-guided Task Adaptation for Imitation Learning by Goyal, Prasoon, Mooney, Raymond J, Niekum, Scott

“…We introduce a novel setting, wherein an agent needs to learn a task from a demonstration of a related task with the difference between the tasks communicated…”
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Improving Grounded Natural Language Understanding through Human-Robot Dialog by Thomason, Jesse, Padmakumar, Aishwarya, Sinapov, Jivko, Walker, Nick, Jiang, Yuqian, Yedidsion, Harel, Hart, Justin, Stone, Peter, Mooney, Raymond J.

“…Natural language understanding for robotics can require substantial domain- and platform-specific engineering. For example, for mobile robots to pick-and-place…”
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Zero-shot Video Moment Retrieval With Off-the-Shelf Models by Diwan, Anuj, Peng, Puyuan, Mooney, Raymond J

“…For the majority of the machine learning community, the expensive nature of collecting high-quality human-annotated data and the inability to efficiently…”
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Dialog as a Vehicle for Lifelong Learning by Padmakumar, Aishwarya, Mooney, Raymond J

“…Dialog systems research has primarily been focused around two main types of applications - task-oriented dialog systems that learn to use clarification to aid…”
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Towards Automated Error Analysis: Learning to Characterize Errors by Gao, Tong, Singh, Shivang, Mooney, Raymond J

“…Characterizing the patterns of errors that a system makes helps researchers focus future development on increasing its accuracy and robustness. We propose a…”
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Dialog Policy Learning for Joint Clarification and Active Learning Queries by Padmakumar, Aishwarya, Mooney, Raymond J

“…Proceedings of 2021 AAAI Conference on Artificial Intelligence (AAAI-2021) Intelligent systems need to be able to recover from mistakes, resolve uncertainty,…”
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Zero-shot Task Adaptation using Natural Language by Goyal, Prasoon, Mooney, Raymond J, Niekum, Scott

“…Imitation learning and instruction-following are two common approaches to communicate a user's intent to a learning agent. However, as the complexity of tasks…”
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Hidden State Guidance: Improving Image Captioning using An Image Conditioned Autoencoder by Wu, Jialin, Mooney, Raymond J

“…Most RNN-based image captioning models receive supervision on the output words to mimic human captions. Therefore, the hidden states can only receive noisy…”
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Using Developer Discussions to Guide Fixing Bugs in Software by Panthaplackel, Sheena, Gligoric, Milos, Li, Junyi Jessy, Mooney, Raymond J

“…Automatically fixing software bugs is a challenging task. While recent work showed that natural language context is useful in guiding bug-fixing models, the…”
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