Search Results for author: Matthew Wallingford

Found 7 papers, 7 papers with code

Neural Priming for Sample-Efficient Adaptation

1 code implementation NeurIPS 2023 Matthew Wallingford, Vivek Ramanujan, Alex Fang, Aditya Kusupati, Roozbeh Mottaghi, Aniruddha Kembhavi, Ludwig Schmidt, Ali Farhadi

Performing lightweight updates on the recalled data significantly improves accuracy across a variety of distribution shift and transfer learning benchmarks.

Transfer Learning

Neural Radiance Field Codebooks

1 code implementation10 Jan 2023 Matthew Wallingford, Aditya Kusupati, Alex Fang, Vivek Ramanujan, Aniruddha Kembhavi, Roozbeh Mottaghi, Ali Farhadi

Compositional representations of the world are a promising step towards enabling high-level scene understanding and efficient transfer to downstream tasks.

Object Representation Learning +1

Matryoshka Representation Learning

4 code implementations26 May 2022 Aditya Kusupati, Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, KaiFeng Chen, Sham Kakade, Prateek Jain, Ali Farhadi

The flexibility within the learned Matryoshka Representations offer: (a) up to 14x smaller embedding size for ImageNet-1K classification at the same level of accuracy; (b) up to 14x real-world speed-ups for large-scale retrieval on ImageNet-1K and 4K; and (c) up to 2% accuracy improvements for long-tail few-shot classification, all while being as robust as the original representations.

Ranked #25 on Image Classification on ObjectNet (using extra training data)

4k Image Classification +2

Task Adaptive Parameter Sharing for Multi-Task Learning

1 code implementation CVPR 2022 Matthew Wallingford, Hao Li, Alessandro Achille, Avinash Ravichandran, Charless Fowlkes, Rahul Bhotika, Stefano Soatto

TAPS solves a joint optimization problem which determines which layers to share with the base model and the value of the task-specific weights.

Multi-Task Learning

FLUID: A Unified Evaluation Framework for Flexible Sequential Data

2 code implementations6 Jul 2020 Matthew Wallingford, Aditya Kusupati, Keivan Alizadeh-Vahid, Aaron Walsman, Aniruddha Kembhavi, Ali Farhadi

To foster research towards the goal of general ML methods, we introduce a new unified evaluation framework - FLUID (Flexible Sequential Data).

Continual Learning Representation Learning +1

RoboTHOR: An Open Simulation-to-Real Embodied AI Platform

1 code implementation CVPR 2020 Matt Deitke, Winson Han, Alvaro Herrasti, Aniruddha Kembhavi, Eric Kolve, Roozbeh Mottaghi, Jordi Salvador, Dustin Schwenk, Eli VanderBilt, Matthew Wallingford, Luca Weihs, Mark Yatskar, Ali Farhadi

We argue that interactive and embodied visual AI has reached a stage of development similar to visual recognition prior to the advent of these ecosystems.

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