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RoboTHOR: An Open Simulation-to-Real Embodied AI Platform
TLDR
RoboTHOR offers a framework of simulated environments paired with physical counterparts to systematically explore and overcome the challenges of simulation-to-real transfer, and a platform where researchers across the globe can remotely test their embodied models in the physical world. Expand
In the Wild: From ML Models to Pragmatic ML Systems
TLDR
A unified learning & evaluation framework - iN thE wilD (NED) is introduced, designed to be a more general paradigm by loosening the restrictive design decisions of past settings & imposing fewer restrictions on learning algorithms. Expand
Are We Overfitting to Experimental Setups in Recognition
TLDR
A new framework is constructed, FLUID, which removes certain assumptions made by current experimental setups while integrating these sub-tasks via the following design choices -- consuming sequential data, allowing for flexible training phases, being compute aware, and working in an open-world setting. Expand
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes
TLDR
This work proposes a novel method for Learning Low-dimensional binary Codes (LLC) for instances as well as classes that is super-efficient while still ensuring nearly optimal classification accuracy for ResNet50 on ImageNet-1K and captures intrinsically important features in the data by discovering an intuitive taxonomy over classes. Expand