activity
20202022
most citedPresentation and Analysis of a Multimodal Dataset for Grounded Language Learning

1 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2022

Continuously Generalized Ordinal Regression for Linear and Deep Models

Fred Lu, Francis Ferraro, Edward Raff

Ordinal regression is a classification task where classes have an order and prediction error increases the further the predicted class is from the true class. The standard approach…

cs.CL20211 cited

Discriminative and Generative Transformer-based Models For Situation Entity Classification

Mehdi Rezaee, Kasra Darvish, Gaoussou Youssouf Kebe +1

We re-examine the situation entity (SE) classification task with varying amounts of available training data. We exploit a Transformer-based variational autoencoder to encode senten…

cs.CL2021

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…

cs.RO2020

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…

cs.RO20201 cited

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…