Michael Noseworthy

Orcid: 0009-0004-6565-2883

Affiliations:
  • Massachusetts Institute of Technology, Cambridge, MA, USA


According to our database1, Michael Noseworthy authored at least 16 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2024
FORGE: Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertainty.
CoRR, 2024

Amortized Inference for Efficient Grasp Model Adaptation.
Proceedings of the IEEE International Conference on Robotics and Automation, 2024

2023
Bayes3D: fast learning and inference in structured generative models of 3D objects and scenes.
CoRR, 2023

Structured Latent Variable Models for Articulated Object Interaction.
CoRR, 2023

Queer In AI: A Case Study in Community-Led Participatory AI.
CoRR, 2023


2021
Active Learning of Abstract Plan Feasibility.
Proceedings of the Robotics: Science and Systems XVII, Virtual Event, July 12-16, 2021., 2021

2020
Visual Prediction of Priors for Articulated Object Interaction.
Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

2019
Inferring Task Goals and Constraints using Bayesian Nonparametric Inverse Reinforcement Learning.
Proceedings of the 3rd Annual Conference on Robot Learning, 2019

Task-Conditioned Variational Autoencoders for Learning Movement Primitives.
Proceedings of the 3rd Annual Conference on Robot Learning, 2019

Leveraging Past References for Robust Language Grounding.
Proceedings of the 23rd Conference on Computational Natural Language Learning, 2019

2018
The RLLChatbot: a solution to the ConvAI challenge.
CoRR, 2018

2017
Predicting Success in Goal-Driven Human-Human Dialogues.
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, 2017

Towards an automatic Turing test: Learning to evaluate dialogue responses.
Proceedings of the 5th International Conference on Learning Representations, 2017

2016
On the Evaluation of Dialogue Systems with Next Utterance Classification.
Proceedings of the SIGDIAL 2016 Conference, 2016

How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation.
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016


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