101 citations · 383 across the 24 of their papers we have counts for
8 papers · 1 filter
Mixture of Nested Experts: Adaptive Processing of Visual Tokens
Gagan Jain, Nidhi Hegde, Aditya Kusupati +5
The visual medium (images and videos) naturally contains a large amount of information redundancy, thereby providing a great opportunity for leveraging efficiency in processing. Wh…
Are "Hierarchical" Visual Representations Hierarchical?
Ethan Shen, Ali Farhadi, Aditya Kusupati
Learned visual representations often capture large amounts of semantic information for accurate downstream applications. Human understanding of the world is fundamentally grounded…
Objaverse-XL: A Universe of 10M+ 3D Objects
Matt Deitke, Ruoshi Liu, Matthew Wallingford +14
Natural language processing and 2D vision models have attained remarkable proficiency on many tasks primarily by escalating the scale of training data. However, 3D vision tasks hav…
Neural Radiance Field Codebooks
Matthew Wallingford, Aditya Kusupati, Alex Fang +4
Compositional representations of the world are a promising step towards enabling high-level scene understanding and efficient transfer to downstream tasks. Learning such representa…
MERLOT Reserve: Neural Script Knowledge through Vision and Language and Sound
Rowan Zellers, Jiasen Lu, Ximing Lu +7
As humans, we navigate a multimodal world, building a holistic understanding from all our senses. We introduce MERLOT Reserve, a model that represents videos jointly over time -- t…
Disrupting Model Training with Adversarial Shortcuts
Ivan Evtimov, Ian Covert, Aditya Kusupati +1
When data is publicly released for human consumption, it is unclear how to prevent its unauthorized usage for machine learning purposes. Successful model training may be preventabl…
FLUID: A Unified Evaluation Framework for Flexible Sequential Data
Matthew Wallingford, Aditya Kusupati, Keivan Alizadeh-Vahid +3
Modern ML methods excel when training data is IID, large-scale, and well labeled. Learning in less ideal conditions remains an open challenge. The sub-fields of few-shot, continual…
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
Oindrila Saha, Aditya Kusupati, Harsha Vardhan Simhadri +2
Standard Convolutional Neural Networks (CNNs) designed for computer vision tasks tend to have large intermediate activation maps. These require large working memory and are thus un…