32 citations · 161 across the 38 of their papers we have counts for
7 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…
Learning to Solve Voxel Building Embodied Tasks from Pixels and Natural Language Instructions
Alexey Skrynnik, Zoya Volovikova, Marc-Alexandre Côté +9
The adoption of pre-trained language models to generate action plans for embodied agents is a promising research strategy. However, execution of instructions in real or simulated e…
Explain My Surprise: Learning Efficient Long-Term Memory by Predicting Uncertain Outcomes
Artyom Sorokin, Nazar Buzun, Leonid Pugachev +1
In many sequential tasks, a model needs to remember relevant events from the distant past to make correct predictions. Unfortunately, a straightforward application of gradient base…
Recurrent Memory Transformer
Aydar Bulatov, Yuri Kuratov, Mikhail S. Burtsev
Transformer-based models show their effectiveness across multiple domains and tasks. The self-attention allows to combine information from all sequence elements into context-aware…
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…