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09/15/2023
Scaling Laws for Sparsely-Connected Foundation Models
We explore the impact of parameter sparsity on the scaling behavior of T...
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08/02/2023
From Sparse to Soft Mixtures of Experts
Sparse mixture of expert architectures (MoEs) scale model capacity witho...
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07/12/2023
Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution
The ubiquitous and demonstrably suboptimal choice of resizing images to ...
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06/16/2023
Scaling Open-Vocabulary Object Detection
Open-vocabulary object detection has benefited greatly from pretrained v...
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01/30/2023
Massively Scaling Heteroscedastic Classifiers
Heteroscedastic classifiers, which learn a multivariate Gaussian distrib...
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12/15/2022
Image-and-Language Understanding from Pixels Only
Multimodal models are becoming increasingly effective, in part due to un...
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12/09/2022
Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints
Training large, deep neural networks to convergence can be prohibitively...
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12/05/2022
Location-Aware Self-Supervised Transformers
Pixel-level labels are particularly expensive to acquire. Hence, pretrai...
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10/20/2022
Transcending Scaling Laws with 0.1
Scaling language models improves performance but comes with significant ...
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09/14/2022
PaLI: A Jointly-Scaled Multilingual Language-Image Model
Effective scaling and a flexible task interface enable large language mo...
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06/06/2022
Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts
Large sparsely-activated models have obtained excellent performance in m...
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05/20/2022
UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes
We introduce UViM, a unified approach capable of modeling a wide range o...
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05/19/2022
Robust and Efficient Medical Imaging with Self-Supervision
Recent progress in Medical Artificial Intelligence (AI) has delivered sy...
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02/24/2022
Learning to Merge Tokens in Vision Transformers
Transformers are widely applied to solve natural language understanding ...
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10/07/2021
Sparse MoEs meet Efficient Ensembles
Machine learning models based on the aggregated outputs of submodels, ei...
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07/14/2021
The Benchmark Lottery
The world of empirical machine learning (ML) strongly relies on benchmar...
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06/15/2021
Revisiting the Calibration of Modern Neural Networks
Accurate estimation of predictive uncertainty (model calibration) is ess...
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06/10/2021
Scaling Vision with Sparse Mixture of Experts
Sparsely-gated Mixture of Experts networks (MoEs) have demonstrated exce...
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06/08/2021
Scaling Vision Transformers
Attention-based neural networks such as the Vision Transformer (ViT) hav...
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05/04/2021
MLP-Mixer: An all-MLP Architecture for Vision
Convolutional Neural Networks (CNNs) are the go-to model for computer vi...
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04/09/2021
SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size
Before deploying machine learning models it is critical to assess their ...
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04/06/2021
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark
Meta and transfer learning are two successful families of approaches to ...
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01/14/2021
Supervised Transfer Learning at Scale for Medical Imaging
Transfer learning is a standard technique to improve performance on task...
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11/06/2020
Underspecification Presents Challenges for Credibility in Modern Machine Learning
ML models often exhibit unexpectedly poor behavior when they are deploye...
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10/22/2020
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
While the Transformer architecture has become the de-facto standard for ...
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10/14/2020
Deep Ensembles for Low-Data Transfer Learning
In the low-data regime, it is difficult to train good supervised models ...
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10/06/2020
Representation learning from videos in-the-wild: An object-centric approach
We propose a method to learn image representations from uncurated videos...
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09/30/2020
Training general representations for remote sensing using in-domain knowledge
Automatically finding good and general remote sensing representations al...
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09/28/2020
Scalable Transfer Learning with Expert Models
Transfer of pre-trained representations can improve sample efficiency an...
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07/16/2020
On Robustness and Transferability of Convolutional Neural Networks
Modern deep convolutional networks (CNNs) are often criticized for not g...
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02/20/2020
Automatic Shortcut Removal for Self-Supervised Representation Learning
In self-supervised visual representation learning, a feature extractor i...
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12/24/2019
Large Scale Learning of General Visual Representations for Transfer
Transfer of pre-trained representations improves sample efficiency and s...
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12/05/2019
Self-Supervised Learning of Video-Induced Visual Invariances
We propose a general framework for self-supervised learning of transfera...
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11/15/2019
In-domain representation learning for remote sensing
Given the importance of remote sensing, surprisingly little attention ha...
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02/02/2019
Parameter-Efficient Transfer Learning for NLP
Fine-tuning large pre-trained models is an effective transfer mechanism ...
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12/27/2018
Neural Architecture Search Over a Graph Search Space
Neural architecture search (NAS) enabled the discovery of state-of-the-a...
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11/27/2018
Self-Supervised Generative Adversarial Networks
Conditional GANs are at the forefront of natural image synthesis. The ma...
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10/27/2018
Self-Supervised GAN to Counter Forgetting
GANs involve training two networks in an adversarial game, where each ne...
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10/02/2018
On Self Modulation for Generative Adversarial Networks
Training Generative Adversarial Networks (GANs) is notoriously challengi...
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03/07/2018
Transfer Automatic Machine Learning
Building effective neural networks requires many design choices. These i...
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01/23/2018
Analyzing Language Learned by an Active Question Answering Agent
We analyze the language learned by an agent trained with reinforcement l...
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05/22/2017
Ask the Right Questions: Active Question Reformulation with Reinforcement Learning
We frame Question Answering as a Reinforcement Learning task, an approac...
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09/02/2013
Scalable Probabilistic Entity-Topic Modeling
We present an LDA approach to entity disambiguation. Each topic is assoc...
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12/24/2011
