37 citations · 121 across the 28 of their papers we have counts for
6 papers · 1 filter
Learning Transferable Features for Implicit Neural Representations
Kushal Vyas, Ahmed Imtiaz Humayun, Aniket Dashpute +3
Implicit neural representations (INRs) have demonstrated success in a variety of applications, including inverse problems and neural rendering. An INR is typically trained to captu…
Self-Improving Diffusion Models with Synthetic Data
Sina Alemohammad, Ahmed Imtiaz Humayun, Shruti Agarwal +2
The artificial intelligence (AI) world is running out of real data for training increasingly large generative models, resulting in accelerating pressure to train on synthetic data.…
What Secrets Do Your Manifolds Hold? Understanding the Local Geometry of Generative Models
Ahmed Imtiaz Humayun, Ibtihel Amara, Cristina Vasconcelos +7
Deep Generative Models are frequently used to learn continuous representations of complex data distributions using a finite number of samples. For any generative model, including p…
On the Geometry of Deep Learning
Randall Balestriero, Ahmed Imtiaz Humayun, Richard Baraniuk
In this paper, we overview one promising avenue of progress at the mathematical foundation of deep learning: the connection between deep networks and function approximation by affi…
Mitigating over-exploration in latent space optimization using LES
Omer Ronen, Ahmed Imtiaz Humayun, Richard Baraniuk +2
We develop Latent Exploration Score (LES) to mitigate over-exploration in Latent Space Optimization (LSO), a popular method for solving black-box discrete optimization problems. LS…
Deep Networks Always Grok and Here is Why
Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk
Grokking, or delayed generalization, is a phenomenon where generalization in a deep neural network (DNN) occurs long after achieving near zero training error. Previous studies have…