113 citations · 208 across the 7 of their papers we have counts for
8 papers
Neural Variational Learning for Grounded Language Acquisition
Nisha Pillai, Cynthia Matuszek, Francis Ferraro
We propose a learning system in which language is grounded in visual percepts without specific pre-defined categories of terms. We present a unified generative method to acquire a…
Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery
John Boutsikas, Maksim E. Eren, Charles Varga +3
The use of Machine Learning has become a significant part of malware detection efforts due to the influx of new malware, an ever changing threat landscape, and the ability of Machi…
Sampling Approach Matters: Active Learning for Robotic Language Acquisition
Nisha Pillai, Edward Raff, Francis Ferraro +1
Ordering the selection of training data using active learning can lead to improvements in learning efficiently from smaller corpora. We present an exploration of active learning ap…
Spoken Language Interaction with Robots: Research Issues and Recommendations, Report from the NSF Future Directions Workshop
Matthew Marge, Carol Espy-Wilson, Nigel Ward
With robotics rapidly advancing, more effective human-robot interaction is increasingly needed to realize the full potential of robots for society. While spoken language must be pa…
Practical Cross-modal Manifold Alignment for Grounded Language
Andre T. Nguyen, Luke E. Richards, Gaoussou Youssouf Kebe +4
We propose a cross-modality manifold alignment procedure that leverages triplet loss to jointly learn consistent, multi-modal embeddings of language-based concepts of real-world it…
Presentation and Analysis of a Multimodal Dataset for Grounded Language Learning
Patrick Jenkins, Rishabh Sachdeva, Gaoussou Youssouf Kebe +7
Grounded language acquisition -- learning how language-based interactions refer to the world around them -- is amajor area of research in robotics, NLP, and HCI. In practice the da…
Planning with Abstract Learned Models While Learning Transferable Subtasks
John Winder, Stephanie Milani, Matthew Landen +5
We introduce an algorithm for model-based hierarchical reinforcement learning to acquire self-contained transition and reward models suitable for probabilistic planning at multiple…
A Joint Model of Language and Perception for Grounded Attribute Learning
Cynthia Matuszek, Nicholas FitzGerald, Luke Zettlemoyer +2
As robots become more ubiquitous and capable, it becomes ever more important to enable untrained users to easily interact with them. Recently, this has led to study of the language…