Michael Rabbat

Chuan Guo

2024-03-21

ArXiv (prépublication)

DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning

Jonathan Lebensold

Maziar Sanjabi

Pietro Astolfi

Adriana Romero Soriano

Kamalika Chaudhuri

Chuan Guo

Text-to-image diffusion models have been shown to suffer from sample-level memorization, possibly reproducing near-perfect replica of images… (voir plus) that they are trained on, which may be undesirable. To remedy this issue, we develop the first differentially private (DP) retrieval-augmented generation algorithm that is capable of generating high-quality image samples while providing provable privacy guarantees. Specifically, we assume access to a text-to-image diffusion model trained on a small amount of public data, and design a DP retrieval mechanism to augment the text prompt with samples retrieved from a private retrieval dataset. Our \emph{differentially private retrieval-augmented diffusion model} (DP-RDM) requires no fine-tuning on the retrieval dataset to adapt to another domain, and can use state-of-the-art generative models to generate high-quality image samples while satisfying rigorous DP guarantees. For instance, when evaluated on MS-COCO, our DP-RDM can generate samples with a privacy budget of

2024-03-21

ArXiv (prépublication)

Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping

Lucas Lehnert

Sainbayar Sukhbaatar

Paul McVay

Yuandong Tian

While Transformers have enabled tremendous progress in various application settings, such architectures still lag behind traditional symboli… (voir plus)c planners for solving complex decision making tasks. In this work, we demonstrate how to train Transformers to solve complex planning tasks and present Searchformer, a Transformer model that optimally solves previously unseen Sokoban puzzles 93.7% of the time, while using up to 26.8% fewer search steps than standard

2024-02-21

ArXiv (prépublication)

Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping

Lucas Lehnert

Sainbayar Sukhbaatar

Paul McVay

Yuandong Tian

While Transformers have enabled tremendous progress in various application settings, such architectures still trail behind traditional symbo… (voir plus)lic planners for solving complex decision making tasks. In this work, we demonstrate how to train Transformers to solve complex planning tasks. This is accomplished by training an encoder-decoder Transformer model to predict the search dynamics of the

2024-02-21

ArXiv (prépublication)

Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping

Lucas Lehnert

Sainbayar Sukhbaatar

Paul McVay

Yuandong Tian

While Transformers have enabled tremendous progress in various application settings, such architectures still trail behind traditional symbo… (voir plus)lic planners for solving complex decision making tasks. In this work, we demonstrate how to train Transformers to solve complex planning tasks. This is accomplished by training an encoder-decoder Transformer model to predict the search dynamics of the

2024-02-21

ArXiv (prépublication)

Revisiting Feature Prediction for Learning Visual Representations from Video

Adrien Bardes

Quentin Garrido

Jean Ponce

Xinlei Chen

Yann LeCun

Mahmoud Assran

Nicolas Ballas

This paper explores feature prediction as a stand-alone objective for unsupervised learning from video and introduces V-JEPA, a collection o… (voir plus)f vision models trained solely using a feature prediction objective, without the use of pretrained image encoders, text, negative examples, reconstruction, or other sources of supervision. The models are trained on 2 million videos collected from public datasets and are evaluated on downstream image and video tasks. Our results show that learning by predicting video features leads to versatile visual representations that perform well on both motion and appearance-based tasks, without adaption of the model's parameters; e.g., using a frozen backbone. Our largest model, a ViT-H/16 trained only on videos, obtains 81.9% on Kinetics-400, 72.2% on Something-Something-v2, and 77.9% on ImageNet1K.

2024-02-15

ArXiv (prépublication)

Revisiting Feature Prediction for Learning Visual Representations from Video

Adrien Bardes

Quentin Garrido

Jean Ponce

Xinlei Chen

Yann LeCun

Mahmoud Assran

Nicolas Ballas

This paper explores feature prediction as a stand-alone objective for unsupervised learning from video and introduces V-JEPA, a collection o… (voir plus)f vision models trained solely using a feature prediction objective, without the use of pretrained image encoders, text, negative examples, reconstruction, or other sources of supervision. The models are trained on 2 million videos collected from public datasets and are evaluated on downstream image and video tasks. Our results show that learning by predicting video features leads to versatile visual representations that perform well on both motion and appearance-based tasks, without adaption of the model's parameters; e.g., using a frozen backbone. Our largest model, a ViT-H/16 trained only on videos, obtains 81.9% on Kinetics-400, 72.2% on Something-Something-v2, and 77.9% on ImageNet1K.

2024-02-15

ArXiv (prépublication)

DINOv2: Learning Robust Visual Features without Supervision

Maxime Oquab

Timothée Darcet

Théo Moutakanni

Huy V. Vo

Marc Szafraniec

Vasil Khalidov

Pierre Fernandez

Daniel HAZIZA

Francisco Massa

Alaaeldin El-Nouby

Mahmoud Assran

Nicolas Ballas

Wojciech Galuba

Russell Howes

Po-Yao Huang

Shang-Wen Li

Ishan Misra

Vasu Sharma

Gabriel Synnaeve … (voir 8 de plus)

Hu Xu 0001

Hu Xu

Huijiao Xu

Herve Jegou

Julien Mairal

Patrick Labatut

Armand Joulin

Piotr Bojanowski

The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar fo… (voir plus)undation models in computer vision. These models could greatly simplify the use of images in any system by producing all-purpose visual features, i.e., features that work across image distributions and tasks without finetuning. This work shows that existing pretraining methods, especially self-supervised methods, can produce such features if trained on enough curated data from diverse sources. We revisit existing approaches and combine different techniques to scale our pretraining in terms of data and model size. Most of the technical contributions aim at accelerating and stabilizing the training at scale. In terms of data, we propose an automatic pipeline to build a dedicated, diverse, and curated image dataset instead of uncurated data, as typically done in the self-supervised literature. In terms of models, we train a ViT model with 1B parameters and distill it into a series of smaller models that surpass the best available all-purpose features, OpenCLIP on most of the benchmarks at image and pixel levels.

2024-01-11

TMLR (accepté)

openreview.net

A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale

Hao-Jun Michael Shi

Tsung-Hsien Lee

Shintaro Iwasaki

Jose Gallego-Posada

Zhijing Li

Kaushik Rangadurai

Dheevatsa Mudigere

2023-09-12

ArXiv (prépublication)

Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design

Chuan Guo

Kamalika Chaudhuri

Pierre Stock

2023-07-03

Proceedings of the 40th International Conference on Machine Learning (publié)

proceedings.mlr.press

openreview.net

Green Federated Learning

Ashkan Yousefpour

Shen Guo

Ashish Shenoy

Sayan Ghosh

Pierre Stock

Kiwan Maeng

Schalk-Willem Kruger

Carole-Jean Wu

Ilya Mironov

2023-06-19

ICML.cc/2023/Workshop/FL (publié)

openreview.net

Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture

Mahmoud Assran

Quentin Duval

Ishan Misra

Piotr Bojanowski

Pascal Vincent

Yann LeCun

Nicolas Ballas

This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations. W… (voir plus)e introduce the Image-based Joint-Embedding Predictive Architecture (I-JEPA), a non-generative approach for self-supervised learning from images. The idea behind I-JEPA is simple: from a single context block, predict the representations of various target blocks in the same image. A core design choice to guide I-JEPA towards producing semantic representations is the masking strategy; specifically, it is crucial to (a) sample target blocks with sufficiently large scale (semantic), and to (b) use a sufficiently informative (spatially distributed) context block. Empirically, when combined with Vision Transformers, we find I-JEPA to be highly scalable. For instance, we train a ViT-Huge/14 on ImageNet using 16 A100 GPUs in under 72 hours to achieve strong downstream performance across a wide range of tasks, from linear classification to object counting and depth prediction.

2023-06-17

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (publié)