• Publications
  • Influence
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.
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.
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.
Task Adaptive Parameter Sharing for Multi-Task Learning
TLDR
Task Adaptive Parameter Sharing (TAPS), a general method for tuning a base model to a new task by adaptively modifying a small, task-specific subset of layers, enables multi-task learning while minimizing resources used and competition between tasks.
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.
Matryoshka Representations for Adaptive Deployment
Learned representations are a central component in modern ML systems, serv-ing a multitude of downstream tasks. When training such representations, it is often the case that computational and
FLUID: A Unified Evaluation Framework for Flexible Sequential Data
TLDR
A new unified evaluation framework – FLUID (Flexible Sequential Data), which integrates the objectives of few-shot, continual, transfer, and representation learning while enabling comparison and integration of techniques across these subfields and presents two new baselines which outperform other evaluated methods on FLUID.