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05/09/2023
ImageBind: One Embedding Space To Bind Them All
We present ImageBind, an approach to learn a joint embedding across six ...
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02/27/2023
LLaMA: Open and Efficient Foundation Language Models
We introduce LLaMA, a collection of foundation language models ranging f...
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07/08/2022
Improving Wikipedia Verifiability with AI
Verifiability is a core content policy of Wikipedia: claims that are lik...
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06/16/2022
OmniMAE: Single Model Masked Pretraining on Images and Videos
Transformer-based architectures have become competitive across a variety...
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04/14/2022
Masked Siamese Networks for Label-Efficient Learning
We propose Masked Siamese Networks (MSN), a self-supervised learning fra...
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09/16/2021
An End-to-End Transformer Model for 3D Object Detection
We propose 3DETR, an end-to-end Transformer based object detection model...
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05/07/2021
ResMLP: Feedforward networks for image classification with data-efficient training
We present ResMLP, an architecture built entirely upon multi-layer perce...
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02/21/2020
Accessing Higher-level Representations in Sequential Transformers with Feedback Memory
Transformers are feedforward networks that can process input tokens in p...
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12/17/2019
Libri-Light: A Benchmark for ASR with Limited or No Supervision
We introduce a new collection of spoken English audio suitable for train...
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11/01/2019
CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data
Pre-training text representations have led to significant improvements i...
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07/12/2019
And the Bit Goes Down: Revisiting the Quantization of Neural Networks
In this paper, we address the problem of reducing the memory footprint o...
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07/02/2019
Augmenting Self-attention with Persistent Memory
Transformer networks have lead to important progress in language modelin...
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05/19/2019
Adaptive Attention Span in Transformers
We propose a novel self-attention mechanism that can learn its optimal a...
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05/03/2019
Leveraging Large-Scale Uncurated Data for Unsupervised Pre-training of Visual Features
Pre-training general-purpose visual features with convolutional neural n...
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07/15/2018
Deep Clustering for Unsupervised Learning of Visual Features
Clustering is a class of unsupervised learning methods that has been ext...
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07/18/2017
Optimizing the Latent Space of Generative Networks
Generative Adversarial Networks (GANs) have been shown to be able to sam...
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06/27/2016
