Soham De | DeepAI

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∙ 08/21/2023

Unlocking Accuracy and Fairness in Differentially Private Image Classification

Privacy-preserving machine learning aims to train models on private data...

0 Leonard Berrada, et al. ∙

∙ 07/21/2023

On the Universality of Linear Recurrences Followed by Nonlinear Projections

In this note (work in progress towards a full-length paper) we show that...

0 Antonio Orvieto, et al. ∙

∙ 03/11/2023

Resurrecting Recurrent Neural Networks for Long Sequences

Recurrent Neural Networks (RNNs) offer fast inference on long sequences ...

10 Antonio Orvieto, et al. ∙

∙ 02/27/2023

Differentially Private Diffusion Models Generate Useful Synthetic Images

The ability to generate privacy-preserving synthetic versions of sensiti...

3 Sahra Ghalebikesabi, et al. ∙

∙ 04/28/2022

Unlocking High-Accuracy Differentially Private Image Classification through Scale

Differential Privacy (DP) provides a formal privacy guarantee preventing...

0 Soham De, et al. ∙

∙ 03/07/2022

Regularising for invariance to data augmentation improves supervised learning

Data augmentation is used in machine learning to make the classifier inv...

0 Aleksander Botev, et al. ∙

∙ 05/31/2021

A study on the plasticity of neural networks

One aim shared by multiple settings, such as continual learning or trans...

0 Tudor Berariu, et al. ∙

∙ 05/27/2021

Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error

In computer vision, it is standard practice to draw a single sample from...

0 Stanislav Fort, et al. ∙

∙ 02/11/2021

High-Performance Large-Scale Image Recognition Without Normalization

Batch normalization is a key component of most image classification mode...

0 Andrew Brock, et al. ∙

∙ 01/28/2021

On the Origin of Implicit Regularization in Stochastic Gradient Descent

For infinitesimal learning rates, stochastic gradient descent (SGD) foll...

0 Samuel L. Smith, et al. ∙

∙ 01/21/2021

Characterizing signal propagation to close the performance gap in unnormalized ResNets

Batch Normalization is a key component in almost all state-of-the-art im...

0 Andrew Brock, et al. ∙

∙ 10/20/2020

BYOL works even without batch statistics

Bootstrap Your Own Latent (BYOL) is a self-supervised learning approach ...

0 Pierre H. Richemond, et al. ∙

∙ 06/26/2020

On the Generalization Benefit of Noise in Stochastic Gradient Descent

It has long been argued that minibatch stochastic gradient descent can g...

12 Samuel L. Smith, et al. ∙

∙ 02/24/2020

Batch Normalization Biases Deep Residual Networks Towards Shallow Paths

Batch normalization has multiple benefits. It improves the conditioning ...

0 Soham De, et al. ∙

∙ 07/04/2019

Adversarial Robustness through Local Linearization

Adversarial training is an effective methodology for training deep neura...

3 Chongli Qin, et al. ∙

∙ 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)...

0 Karthik A Sankararaman, et al. ∙

∙ 07/18/2018

Convergence guarantees for RMSProp and ADAM in non-convex optimization and their comparison to Nesterov acceleration on autoencoders

RMSProp and ADAM continue to be extremely popular algorithms for trainin...

0 Amitabh Basu, et al. ∙

∙ 04/19/2018

Tipping Points for Norm Change in Human Cultures

Humans interact with each other on a daily basis by developing and maint...

0 Soham De, et al. ∙

∙ 06/07/2017

Training Quantized Nets: A Deeper Understanding

Currently, deep neural networks are deployed on low-power portable devic...

0 Hao Li, et al. ∙

∙ 04/16/2017

Understanding Norm Change: An Evolutionary Game-Theoretic Approach (Extended Version)

Human societies around the world interact with each other by developing ...

0 Soham De, et al. ∙

∙ 01/09/2017

Son of Zorn's Lemma: Targeted Style Transfer Using Instance-aware Semantic Segmentation

Style transfer is an important task in which the style of a source image...

0 Carlos Castillo, et al. ∙

∙ 10/18/2016

Big Batch SGD: Automated Inference using Adaptive Batch Sizes

Classical stochastic gradient methods for optimization rely on noisy gra...

0 Soham De, et al. ∙

∙ 12/09/2015

Efficient Distributed SGD with Variance Reduction

Stochastic Gradient Descent (SGD) has become one of the most popular opt...

0 Soham De, et al. ∙

∙ 12/05/2015

Variance Reduction for Distributed Stochastic Gradient Descent

Variance reduction (VR) methods boost the performance of stochastic grad...

0 Soham De, et al. ∙

∙ 10/15/2015

Layer-Specific Adaptive Learning Rates for Deep Networks

The increasing complexity of deep learning architectures is resulting in...

0 Bharat Singh, et al. ∙

∙ 02/27/2015

Plagiarism Detection in Polyphonic Music using Monaural Signal Separation

Given the large number of new musical tracks released each year, automat...

0 Soham De, et al. ∙

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