Search Results for author: Divyam Madaan

Found 6 papers, 4 papers with code

Representational Continuity for Unsupervised Continual Learning

1 code implementation 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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