2.7k citations · 2.8k across the 6 of their papers we have counts for
6 papers
Llama 2: Open Foundation and Fine-Tuned Chat Models
Hugo Touvron, Louis Martin, Kevin Stone +65
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our f…
A Theory on Adam Instability in Large-Scale Machine Learning
Igor Molybog, Peter Albert, Moya Chen +14
We present a theory for the previously unexplained divergent behavior noticed in the training of large language models. We argue that the phenomenon is an artifact of the dominant…
Developing and Evaluating Deep Neural Network-based Denoising for Nanoparticle TEM Images with Ultra-low Signal-to-Noise
Joshua L. Vincent, Ramon Manzorro, Sreyas Mohan +6
A deep convolutional neural network has been developed to denoise atomic-resolution TEM image datasets of nanoparticles acquired using direct electron counting detectors, for appli…
Deep Denoising For Scientific Discovery: A Case Study In Electron Microscopy
Sreyas Mohan, Ramon Manzorro, Joshua L. Vincent +6
Denoising is a fundamental challenge in scientific imaging. Deep convolutional neural networks (CNNs) provide the current state of the art in denoising natural images, where they p…
PyXtal FF: a Python Library for Automated Force Field Generation
Howard Yanxon, David Zagaceta, Binh Tang +2
We present PyXtal FF, a package based on Python programming language, for developing machine learning potentials (MLPs). The aim of PyXtal FF is to promote the application of atomi…
Graph-Based Continual Learning
Binh Tang, David S. Matteson
Despite significant advances, continual learning models still suffer from catastrophic forgetting when exposed to incrementally available data from non-stationary distributions. Re…