23 citations · 48 across the 10 of their papers we have counts for
13 papers
Status-quo policy gradient in Multi-Agent Reinforcement Learning
Pinkesh Badjatiya, Mausoom Sarkar, Nikaash Puri +4
Individual rationality, which involves maximizing expected individual returns, does not always lead to high-utility individual or group outcomes in multi-agent problems. For instan…
D2C: Diffusion-Denoising Models for Few-shot Conditional Generation
Abhishek Sinha, Jiaming Song, Chenlin Meng +1
Conditional generative models of high-dimensional images have many applications, but supervision signals from conditions to images can be expensive to acquire. This paper describes…
Negative Data Augmentation
Abhishek Sinha, Kumar Ayush, Jiaming Song +3
Data augmentation is often used to enlarge datasets with synthetic samples generated in accordance with the underlying data distribution. To enable a wider range of augmentations,…
On the Benefits of Models with Perceptually-Aligned Gradients
Gunjan Aggarwal, Abhishek Sinha, Nupur Kumari +1
Adversarial robust models have been shown to learn more robust and interpretable features than standard trained models. As shown in [\cite{tsipras2018robustness}], such robust mode…
Inducing Cooperative behaviour in Sequential-Social dilemmas through Multi-Agent Reinforcement Learning using Status-Quo Loss
Pinkesh Badjatiya, Mausoom Sarkar, Abhishek Sinha +4
In social dilemma situations, individual rationality leads to sub-optimal group outcomes. Several human engagements can be modeled as a sequential (multi-step) social dilemmas. How…
cFineGAN: Unsupervised multi-conditional fine-grained image generation
Gunjan Aggarwal, Abhishek Sinha
We propose an unsupervised multi-conditional image generation pipeline: cFineGAN, that can generate an image conditioned on two input images such that the generated image preserves…
A Method for Computing Class-wise Universal Adversarial Perturbations
Tejus Gupta, Abhishek Sinha, Nupur Kumari +2
We present an algorithm for computing class-specific universal adversarial perturbations for deep neural networks. Such perturbations can induce misclassification in a large fracti…
Attributional Robustness Training using Input-Gradient Spatial Alignment
Mayank Singh, Nupur Kumari, Puneet Mangla +3
Interpretability is an emerging area of research in trustworthy machine learning. Safe deployment of machine learning system mandates that the prediction and its explanation be rel…
Charting the Right Manifold: Manifold Mixup for Few-shot Learning
Puneet Mangla, Mayank Singh, Abhishek Sinha +3
Few-shot learning algorithms aim to learn model parameters capable of adapting to unseen classes with the help of only a few labeled examples. A recent regularization technique - M…
Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models
Mayank Singh, Abhishek Sinha, Nupur Kumari +3
Neural networks are vulnerable to adversarial attacks -- small visually imperceptible crafted noise which when added to the input drastically changes the output. The most effective…
Attention Based Natural Language Grounding by Navigating Virtual Environment
Akilesh B, Abhishek Sinha, Mausoom Sarkar +1
In this work, we focus on the problem of grounding language by training an agent to follow a set of natural language instructions and navigate to a target object in an environment.…
Neural Networks in Adversarial Setting and Ill-Conditioned Weight Space
Mayank Singh, Abhishek Sinha, Balaji Krishnamurthy
Recently, Neural networks have seen a huge surge in its adoption due to their ability to provide high accuracy on various tasks. On the other hand, the existence of adversarial exa…
Introspection: Accelerating Neural Network Training By Learning Weight Evolution
Abhishek Sinha, Mausoom Sarkar, Aahitagni Mukherjee +1
Neural Networks are function approximators that have achieved state-of-the-art accuracy in numerous machine learning tasks. In spite of their great success in terms of accuracy, th…