1 code implementation • 19 Feb 2024 • Baojie Li, Xin Chen, Anubhav Jain
This dynamic model, periodically-updated (as short as daily), can closely capture the actual health status, enabling precise power estimation.
no code implementations • 20 Nov 2023 • Sudipta Banerjee, Anubhav Jain, Zehua Jiang, Nasir Memon, Julian Togelius, Arun Ross
A dictionary attack in a biometric system entails the use of a small number of strategically generated images or templates to successfully match with a large number of identities, thereby compromising security.
2 code implementations • 28 Aug 2023 • Janosh Riebesell, Rhys E. A. Goodall, Philipp Benner, Yuan Chiang, Bowen Deng, Alpha A. Lee, Anubhav Jain, Kristin A. Persson
The top 3 models are UIPs, the winning methodology for ML-guided materials discovery, achieving F1 scores of ~0. 6 for crystal stability classification and discovery acceleration factors (DAF) of up to 5x on the first 10k most stable predictions compared to dummy selection from our test set.
no code implementations • 16 Aug 2023 • Anubhav Jain, Nasir Memon, Julian Togelius
We do so by generating balanced data from an existing imbalanced deep generative model using an evolutionary algorithm and then using this data to train a balanced generative model.
1 code implementation • 12 May 2023 • Anubhav Jain, Nasir Memon, Julian Togelius
Facial recognition systems have made significant strides thanks to data-heavy deep learning models, but these models rely on large privacy-sensitive datasets.
no code implementations • 26 Apr 2023 • Nicholas Walker, John Dagdelen, Kevin Cruse, SangHoon Lee, Samuel Gleason, Alexander Dunn, Gerbrand Ceder, A. Paul Alivisatos, Kristin A. Persson, Anubhav Jain
To that end, we present an approach using the powerful GPT-3 language model to extract structured multi-step seed-mediated growth procedures and outcomes for gold nanorods from unstructured scientific text.
no code implementations • 10 Dec 2022 • Alexander Dunn, John Dagdelen, Nicholas Walker, SangHoon Lee, Andrew S. Rosen, Gerbrand Ceder, Kristin Persson, Anubhav Jain
Here, we present a simple sequence-to-sequence approach to joint named entity recognition and relation extraction for complex hierarchical information in scientific text.
1 code implementation • 5 Dec 2022 • Anubhav Jain, Nasir Memon, Julian Togelius
Face swapping technology used to create "Deepfakes" has advanced significantly over the past few years and now enables us to create realistic facial manipulations.
no code implementations • 24 Apr 2022 • Mirko Marras, Pawel Korus, Anubhav Jain, Nasir Memon
In this paper, we propose dictionary attacks against speaker verification - a novel attack vector that aims to match a large fraction of speaker population by chance.
no code implementations • 2 May 2020 • Alexander Dunn, Qi. Wang, Alex Ganose, Daniel Dopp, Anubhav Jain
The reference algorithm, Automatminer, is a highly-extensible, fully-automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning.
Materials Science Computational Physics
2 code implementations • 28 Jan 2020 • Christopher J. Bartel, Amalie Trewartha, Qi. Wang, Alex Dunn, Anubhav Jain, Gerbrand Ceder
By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85, 014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on predicting the stability of compounds, making them considerably less useful than DFT for the discovery and design of new solids.
Materials Science Computational Physics
no code implementations • 20 Oct 2019 • Anubhav Jain, Avdesh Kumar, Saumya Balodi, Pravesh Biyani
This makes it vital to ensure that public transport is efficient.
no code implementations • 7 Apr 2019 • Qi. Wang, Anubhav Jain
When metallic glasses are subjected to mechanical loads, the plastic response of atoms is heterogeneous.
Materials Science Computational Physics
no code implementations • 26 Jan 2019 • Anubhav Jain, Richa Singh, Mayank Vatsa
For distinguishing between real images and images generated using GANs, the proposed algorithm yields an accuracy of 99. 83%.