3.7k citations · 11.8k across the 85 of their papers we have counts for
8 papers · 1 filter
Collecting Interactive Multi-modal Datasets for Grounded Language Understanding
Shrestha Mohanty, Negar Arabzadeh, Milagro Teruel +9
Human intelligence can remarkably adapt quickly to new tasks and environments. Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by…
CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory
Nur Muhammad Mahi Shafiullah, Chris Paxton, Lerrel Pinto +2
We propose CLIP-Fields, an implicit scene model that can be used for a variety of tasks, such as segmentation, instance identification, semantic search over space, and view localiz…
BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage
Kurt Shuster, Jing Xu, Mojtaba Komeili +15
We present BlenderBot 3, a 175B parameter dialogue model capable of open-domain conversation with access to the internet and a long-term memory, and having been trained on a large…
IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022
Julia Kiseleva, Alexey Skrynnik, Artem Zholus +14
Human intelligence has the remarkable ability to adapt to new tasks and environments quickly. Starting from a very young age, humans acquire new skills and learn how to solve new t…
Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021
Julia Kiseleva, Ziming Li, Mohammad Aliannejadi +18
Human intelligence has the remarkable ability to quickly adapt to new tasks and environments. Starting from a very young age, humans acquire new skills and learn how to solve new t…
Many Episode Learning in a Modular Embodied Agent via End-to-End Interaction
Yuxuan Sun, Ethan Carlson, Rebecca Qian +2
In this work we give a case study of an embodied machine-learning (ML) powered agent that improves itself via interactions with crowd-workers. The agent consists of a set of module…
Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion
Kurt Shuster, Mojtaba Komeili, Leonard Adolphs +3
Language models (LMs) have recently been shown to generate more factual responses by employing modularity (Zhou et al., 2021) in combination with retrieval (Adolphs et al., 2021).…
Can I see an Example? Active Learning the Long Tail of Attributes and Relations
Tyler L. Hayes, Maximilian Nickel, Christopher Kanan +2
There has been significant progress in creating machine learning models that identify objects in scenes along with their associated attributes and relationships; however, there is…