Search Results for author: Divyam Madaan

Found 10 papers, 6 papers with code

On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis

1 code implementation23 Jun 2023 Divyam Madaan, Daniel Sodickson, Kyunghyun Cho, Sumit Chopra

However, the image reconstruction process within the MRI pipeline, which requires the use of complex hardware and adjustment of a large number of scanner parameters, is highly susceptible to noise of various forms, resulting in arbitrary artifacts within the images.

Image Reconstruction

Heterogeneous Continual Learning

no code implementations CVPR 2023 Divyam Madaan, Hongxu Yin, Wonmin Byeon, Jan Kautz, Pavlo Molchanov

We propose a novel framework and a solution to tackle the continual learning (CL) problem with changing network architectures.

Continual Learning Knowledge Distillation +1

Improving Representational Continuity via Continued Pretraining

1 code implementation26 Feb 2023 Michael Sun, Ananya Kumar, Divyam Madaan, Percy Liang

We consider the continual representation learning setting: sequentially pretrain a model $M'$ on tasks $T_1, \ldots, T_T$, and then adapt $M'$ on a small amount of data from each task $T_i$ to check if it has forgotten information from old tasks.

Continual Learning Representation Learning +1

Representational Continuity for Unsupervised Continual Learning

2 code implementations ICLR 2022 Divyam Madaan, Jaehong Yoon, Yuanchun Li, Yunxin Liu, Sung Ju Hwang

Continual learning (CL) aims to learn a sequence of tasks without forgetting the previously acquired knowledge.

Continual Learning

Online Coreset Selection for Rehearsal-based Continual Learning

no code implementations ICLR 2022 Jaehong Yoon, Divyam Madaan, Eunho Yang, Sung Ju Hwang

We validate the effectiveness of our coreset selection mechanism over various standard, imbalanced, and noisy datasets against strong continual learning baselines, demonstrating that it improves task adaptation and prevents catastrophic forgetting in a sample-efficient manner.

Continual Learning

Learning to Generate Noise for Multi-Attack Robustness

1 code implementation22 Jun 2020 Divyam Madaan, Jinwoo Shin, Sung Ju Hwang

Adversarial learning has emerged as one of the successful techniques to circumvent the susceptibility of existing methods against adversarial perturbations.


Adversarial Neural Pruning with Latent Vulnerability Suppression

1 code implementation ICML 2020 Divyam Madaan, Jinwoo Shin, Sung Ju Hwang

Despite the remarkable performance of deep neural networks on various computer vision tasks, they are known to be susceptible to adversarial perturbations, which makes it challenging to deploy them in real-world safety-critical applications.

Adversarial Robustness

Learning Sparse Networks Using Targeted Dropout

2 code implementations31 May 2019 Aidan N. Gomez, Ivan Zhang, Siddhartha Rao Kamalakara, Divyam Madaan, Kevin Swersky, Yarin Gal, Geoffrey E. Hinton

Before computing the gradients for each weight update, targeted dropout stochastically selects a set of units or weights to be dropped using a simple self-reinforcing sparsity criterion and then computes the gradients for the remaining weights.

Network Pruning Neural Network Compression

VayuAnukulani: Adaptive Memory Networks for Air Pollution Forecasting

no code implementations8 Apr 2019 Divyam Madaan, Radhika Dua, Prerana Mukherjee, Brejesh lall

Extensive experiments on data sources obtained in Delhi demonstrate that the proposed adaptive attention based Bidirectional LSTM Network outperforms several baselines for classification and regression models.

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