Search Results for author: Kunal Pratap Singh

Found 8 papers, 4 papers with code

Scene Graph Contrastive Learning for Embodied Navigation

no code implementations ICCV 2023 Kunal Pratap Singh, Jordi Salvador, Luca Weihs, Aniruddha Kembhavi

Training effective embodied AI agents often involves expert imitation, specialized components such as maps, or leveraging additional sensors for depth and localization.

Contrastive Learning Representation Learning

A General Purpose Supervisory Signal for Embodied Agents

no code implementations1 Dec 2022 Kunal Pratap Singh, Jordi Salvador, Luca Weihs, Aniruddha Kembhavi

Training effective embodied AI agents often involves manual reward engineering, expert imitation, specialized components such as maps, or leveraging additional sensors for depth and localization.

Contrastive Learning Representation Learning

Ask4Help: Learning to Leverage an Expert for Embodied Tasks

1 code implementation18 Nov 2022 Kunal Pratap Singh, Luca Weihs, Alvaro Herrasti, Jonghyun Choi, Aniruddha Kemhavi, Roozbeh Mottaghi

Embodied AI agents continue to become more capable every year with the advent of new models, environments, and benchmarks, but are still far away from being performant and reliable enough to be deployed in real, user-facing, applications.

BNAS v2: Learning Architectures for Binary Networks with Empirical Improvements

1 code implementation16 Oct 2021 Dahyun Kim, Kunal Pratap Singh, Jonghyun Choi

Questioning that the architectures designed for FP networks might not be the best for binary networks, we propose to search architectures for binary networks (BNAS) by defining a new search space for binary architectures and a novel search objective.

Quantization

Factorizing Perception and Policy for Interactive Instruction Following

1 code implementation ICCV 2021 Kunal Pratap Singh, Suvaansh Bhambri, Byeonghwi Kim, Roozbeh Mottaghi, Jonghyun Choi

Performing simple household tasks based on language directives is very natural to humans, yet it remains an open challenge for AI agents.

Instruction Following Navigate

Learning Architectures for Binary Networks

1 code implementation ECCV 2020 Dahyun Kim, Kunal Pratap Singh, Jonghyun Choi

Specifically, based on the cell based search method, we define the new search space of binary layer types, design a new cell template, and rediscover the utility of and propose to use the Zeroise layer instead of using it as a placeholder.

Quantization

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