Search Results for author: Felipe Oviedo

Found 9 papers, 8 papers with code

BankNote-Net: Open dataset for assistive universal currency recognition

1 code implementation7 Apr 2022 Felipe Oviedo, Srinivas Vinnakota, Eugene Seleznev, Hemant Malhotra, Saqib Shaikh, Juan Lavista Ferres

This last task, the recognition of banknotes from different denominations, has been addressed by the use of computer vision models for image recognition.

Contrastive Learning Few-Shot Learning +1

An Artificial Intelligence Dataset for Solar Energy Locations in India

1 code implementation31 Jan 2022 Anthony Ortiz, Dhaval Negandhi, Sagar R Mysorekar, Joseph Kiesecker, Shivaprakash K Nagaraju, Caleb Robinson, Priyal Bhatia, Aditi Khurana, Jane Wang, Felipe Oviedo, Juan Lavista Ferres

Using this dataset, we measure the solar footprint across India and quantified the degree of landcover modification associated with the development of PV infrastructure.

Interpretable and Explainable Machine Learning for Materials Science and Chemistry

no code implementations1 Nov 2021 Felipe Oviedo, Juan Lavista Ferres, Tonio Buonassisi, Keith Butler

While the uptake of data-driven approaches for materials science and chemistry is at an exciting, early stage, to realise the true potential of machine learning models for successful scientific discovery, they must have qualities beyond purely predictive power.

BIG-bench Machine Learning Interpretable Machine Learning

Bridging the gap between photovoltaics R&D and manufacturing with data-driven optimization

1 code implementation28 Apr 2020 Felipe Oviedo, Zekun Ren, Xue Hansong, Siyu Isaac Parker Tian, Kaicheng Zhang, Mariya Layurova, Thomas Heumueller, Ning li, Erik Birgersson, Shijing Sun, Benji Mayurama, Ian Marius Peters, Christoph J. Brabec, John Fisher III, Tonio Buonassisi

Novel photovoltaics, such as perovskites and perovskite-inspired materials, have shown great promise due to high efficiency and potentially low manufacturing cost.

Applied Physics

Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks

2 code implementations npj Computational Materials 2019 Felipe Oviedo, Zekun Ren, Shijing Sun, Charles Settens, Zhe Liu, Noor Titan Putri Hartono, Savitha Ramasamy, Brian L. DeCost, Siyu I. P. Tian, Giuseppe Romano, Aaron Gilad Kusne, Tonio Buonassisi

We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data.

BIG-bench Machine Learning Data Augmentation +6

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