3 citations · 6 across the 4 of their papers we have counts for
6 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…
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…