Zobrazeno 1 - 10
of 108
pro vyhledávání: '"Hiszpanski, Anna"'
Autor:
Yi, Gyeong Hoon, Choi, Jiwoo, Song, Hyeongyun, Miano, Olivia, Choi, Jaewoong, Bang, Kihoon, Lee, Byungju, Sohn, Seok Su, Buttler, David, Hiszpanski, Anna, Han, Sang Soo, Kim, Donghun
Efficiently extracting data from tables in the scientific literature is pivotal for building large-scale databases. However, the tables reported in materials science papers exist in highly diverse forms; thus, rule-based extractions are an ineffectiv
Externí odkaz:
http://arxiv.org/abs/2406.05431
Autor:
Tsaknakis, Ioannis, Kailkhura, Bhavya, Liu, Sijia, Loveland, Donald, Diffenderfer, James, Hiszpanski, Anna Maria, Hong, Mingyi
Using deep learning (DL) to accelerate and/or improve scientific workflows can yield discoveries that are otherwise impossible. Unfortunately, DL models have yielded limited success in complex scientific domains due to large data requirements. In thi
Externí odkaz:
http://arxiv.org/abs/2206.02785
One of the grand challenges of utilizing machine learning for the discovery of innovative new polymers lies in the difficulty of accurately representing the complex structures of polymeric materials. Although a wide array of hand-designed polymer rep
Externí odkaz:
http://arxiv.org/abs/2205.13757
Graph neural network (GNN) explanations have largely been facilitated through post-hoc introspection. While this has been deemed successful, many post-hoc explanation methods have been shown to fail in capturing a model's learned representation. Due
Externí odkaz:
http://arxiv.org/abs/2106.13427
Autor:
Liu, Shusen, Kailkhura, Bhavya, Zhang, Jize, Hiszpanski, Anna M., Robertson, Emily, Loveland, Donald, Han, T. Yong-Jin
The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges. However, despite the effectiveness in building predictive models, fundamental challenges exist in extracting action
Externí odkaz:
http://arxiv.org/abs/2007.08631
Autor:
Liu, Shusen, Kailkhura, Bhavya, Zhang, Jize, Hiszpanski, Anna M., Robertson, Emily, Loveland, Donald, Han, T. Yong-Jin
The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges. However, despite the effectiveness in building predictive models, fundamental challenges exist in extracting action
Externí odkaz:
http://arxiv.org/abs/2006.16533
Autor:
Gallagher, Brian, Rever, Matthew, Loveland, Donald, Mundhenk, T. Nathan, Beauchamp, Brock, Robertson, Emily, Jaman, Golam G., Hiszpanski, Anna M., Han, T. Yong-Jin
We explore the application of computer vision and machine learning (ML) techniques to predict material properties (e.g. compressive strength) based on SEM images. We show that it's possible to train ML models to predict materials performance based on
Externí odkaz:
http://arxiv.org/abs/1906.02130
Material scientists are increasingly adopting the use of machine learning (ML) for making potentially important decisions, such as, discovery, development, optimization, synthesis and characterization of materials. However, despite ML's impressive pe
Externí odkaz:
http://arxiv.org/abs/1901.02717
Autor:
Gallagher, Brian, Rever, Matthew, Loveland, Donald, Mundhenk, T. Nathan, Beauchamp, Brock, Robertson, Emily, Jaman, Golam G., Hiszpanski, Anna M., Han, T. Yong-Jin
Publikováno v:
In Materials & Design May 2020 190
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