research
∙
06/26/2023
Private Federated Learning in Gboard
This white paper describes recent advances in Gboard(Google Keyboard)'s ...
share
research
∙
06/13/2023
(Amplified) Banded Matrix Factorization: A unified approach to private training
Matrix factorization (MF) mechanisms for differential privacy (DP) have ...
research
∙
05/29/2023
Federated Learning of Gboard Language Models with Differential Privacy
We train language models (LMs) with federated learning (FL) and differen...
research
∙
05/20/2023
Can Public Large Language Models Help Private Cross-device Federated Learning?
We study (differentially) private federated learning (FL) of language mo...
research
∙
03/17/2023
An Empirical Evaluation of Federated Contextual Bandit Algorithms
As the adoption of federated learning increases for learning from sensit...
research
∙
03/01/2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
ML models are ubiquitous in real world applications and are a constant f...
research
∙
11/20/2022
Learning to Generate Image Embeddings with User-level Differential Privacy
Small on-device models have been successfully trained with user-level di...
research
∙
06/18/2022
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning
Personalized federated learning considers learning models unique to each...
research
∙
10/06/2021
Efficient and Private Federated Learning with Partially Trainable Networks
Federated learning is used for decentralized training of machine learnin...
research
∙
06/04/2021
Local Adaptivity in Federated Learning: Convergence and Consistency
The federated learning (FL) framework trains a machine learning model us...
research
∙
02/16/2021
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training
Changes in neural architectures have fostered significant breakthroughs ...
research
∙
10/14/2020
Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer
When large scale training data is available, one can obtain compact and ...
research
∙
05/30/2020
Exploring Model Robustness with Adaptive Networks and Improved Adversarial Training
Adversarial training has proven to be effective in hardening networks ag...
research
∙
12/10/2019
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
research
∙
04/29/2019
Adversarial Training for Free!
Adversarial training, in which a network is trained on adversarial examp...
research
∙
04/15/2019
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent
The goal of this paper is to study why stochastic gradient descent (SGD)...
research
∙
11/27/2018
Universal Adversarial Training
Standard adversarial attacks change the predicted class label of an imag...
research
∙
07/27/2018
Ensemble-based Multi-Filter Feature Selection Method for DDoS Detection in Cloud Computing
Increasing interest in the adoption of cloud computing has exposed it to...
research
∙
07/16/2018
Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays
Chest X-rays is one of the most commonly available and affordable radiol...
research
∙
06/28/2018
Accurate and efficient video de-fencing using convolutional neural networks and temporal information
De-fencing is to eliminate the captured fence on an image or a video, pr...
research
∙
05/25/2018
Beyond Textures: Learning from Multi-domain Artistic Images for Arbitrary Style Transfer
We propose a fast feed-forward network for arbitrary style transfer, whi...
research
∙
05/08/2018
The Effectiveness of Instance Normalization: a Strong Baseline for Single Image Dehazing
We propose a novel deep neural network architecture for the challenging ...
research
∙
03/17/2018
Learning to Cluster for Proposal-Free Instance Segmentation
This work proposed a novel learning objective to train a deep neural net...
research
∙
12/28/2017
Visualizing the Loss Landscape of Neural Nets
Neural network training relies on our ability to find "good" minimizers ...
research
∙
09/02/2017
Learning Loss for Knowledge Distillation with Conditional Adversarial Networks
There is an increasing interest on accelerating neural networks for real...
research
∙
06/09/2017
Adaptive Consensus ADMM for Distributed Optimization
The alternating direction method of multipliers (ADMM) is commonly used ...
research
∙
06/07/2017
Training Quantized Nets: A Deeper Understanding
Currently, deep neural networks are deployed on low-power portable devic...
research
∙
05/20/2017
Stabilizing Adversarial Nets With Prediction Methods
Adversarial neural networks solve many important problems in data scienc...
research
∙
04/10/2017
Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation
Many modern computer vision and machine learning applications rely on so...
research
∙
02/14/2016
