22 citations · 32 across the 13 of their papers we have counts for
7 papers · 1 filter
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.…
An Adaptive Tangent Feature Perspective of Neural Networks
Daniel LeJeune, Sina Alemohammad
In order to better understand feature learning in neural networks, we propose a framework for understanding linear models in tangent feature space where the features are allowed to…
Self-Consuming Generative Models Go MAD
Sina Alemohammad, Josue Casco-Rodriguez, Lorenzo Luzi +5
Seismic advances in generative AI algorithms for imagery, text, and other data types has led to the temptation to use synthetic data to train next-generation models. Repeating this…
NeuroView-RNN: It's About Time
CJ Barberan, Sina Alemohammad, Naiming Liu +2
Recurrent Neural Networks (RNNs) are important tools for processing sequential data such as time-series or video. Interpretability is defined as the ability to be understood by a p…
NFT-K: Non-Fungible Tangent Kernels
Sina Alemohammad, Hossein Babaei, CJ Barberan +4
Deep neural networks have become essential for numerous applications due to their strong empirical performance such as vision, RL, and classification. Unfortunately, these networks…
Enhanced Recurrent Neural Tangent Kernels for Non-Time-Series Data
Sina Alemohammad, Randall Balestriero, Zichao Wang +1
Kernels derived from deep neural networks (DNNs) in the infinite-width regime provide not only high performance in a range of machine learning tasks but also new theoretical insigh…
The Recurrent Neural Tangent Kernel
Sina Alemohammad, Zichao Wang, Randall Balestriero +1
The study of deep neural networks (DNNs) in the infinite-width limit, via the so-called neural tangent kernel (NTK) approach, has provided new insights into the dynamics of learnin…