7 citations · 8 across the 6 of their papers we have counts for
5 papers · 1 filter
Fine-Tuning Diffusion Models via Intermediate Distribution Shaping
Gautham Govind Anil, Shaan Ul Haque, Nithish Kannen +3
Diffusion models are widely used for generative tasks across domains. Given a pre-trained diffusion model, it is often desirable to fine-tune it further either to correct for error…
Interleaved Gibbs Diffusion: Generating Discrete-Continuous Data with Implicit Constraints
Gautham Govind Anil, Sachin Yadav, Dheeraj Nagaraj +2
We introduce Interleaved Gibbs Diffusion (IGD), a novel generative modeling framework for discrete-continuous data, focusing on problems with important, implicit and unspecified co…
Infinite Width Limits of Self Supervised Neural Networks
Maximilian Fleissner, Gautham Govind Anil, Debarghya Ghoshdastidar
The NTK is a widely used tool in the theoretical analysis of deep learning, allowing us to look at supervised deep neural networks through the lenses of kernel regression. Recently…
When can we Approximate Wide Contrastive Models with Neural Tangent Kernels and Principal Component Analysis?
Gautham Govind Anil, Pascal Esser, Debarghya Ghoshdastidar
Contrastive learning is a paradigm for learning representations from unlabelled data that has been highly successful for image and text data. Several recent works have examined con…
Generating Universal Adversarial Perturbations for Quantum Classifiers
Gautham Anil, Vishnu Vinod, Apurva Narayan
Quantum Machine Learning (QML) has emerged as a promising field of research, aiming to leverage the capabilities of quantum computing to enhance existing machine learning methodolo…