438 citations · 1.3k across the 45 of their papers we have counts for
17 papers · 1 filter
RangeAugment: Efficient Online Augmentation with Range Learning
Sachin Mehta, Saeid Naderiparizi, Fartash Faghri +5
State-of-the-art automatic augmentation methods (e.g., AutoAugment and RandAugment) for visual recognition tasks diversify training data using a large set of augmentation operation…
Objaverse: A Universe of Annotated 3D Objects
Matt Deitke, Dustin Schwenk, Jordi Salvador +7
Massive data corpora like WebText, Wikipedia, Conceptual Captions, WebImageText, and LAION have propelled recent dramatic progress in AI. Large neural models trained on such datase…
Phone2Proc: Bringing Robust Robots Into Our Chaotic World
Matt Deitke, Rose Hendrix, Luca Weihs +3
Training embodied agents in simulation has become mainstream for the embodied AI community. However, these agents often struggle when deployed in the physical world due to their in…
Self-Supervised Object Goal Navigation with In-Situ Finetuning
So Yeon Min, Yao-Hung Hubert Tsai, Wei Ding +4
A household robot should be able to navigate to target objects without requiring users to first annotate everything in their home. Most current approaches to object navigation do n…
Editing Models with Task Arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman +4
Changing how pre-trained models behave -- e.g., improving their performance on a downstream task or mitigating biases learned during pre-training -- is a common practice when devel…
lo-fi: distributed fine-tuning without communication
Mitchell Wortsman, Suchin Gururangan, Shen Li +4
When fine-tuning large neural networks, it is common to use multiple nodes and to communicate gradients at each optimization step. By contrast, we investigate completely local fine…