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Eric Heim
Eric Heim
Chief Scientist (AI Division), Software Engineering Institute, Carnegie Mellon University
Bestätigte E-Mail-Adresse bei sei.cmu.edu
Titel
Zitiert von
Zitiert von
Jahr
Creating xBD: A dataset for assessing building damage from satellite imagery
R Gupta, B Goodman, N Patel, R Hosfelt, S Sajeev, E Heim, J Doshi, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
3732019
Constrained generative adversarial networks for interactive image generation
E Heim
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
252019
Generating Triples with Adversarial Networks for Scene Graph Construction
M Klawonn, E Heim
Thirty-Second AAAI Conference on Artificial Intelligence, 2018
242018
Efficient online relative comparison kernel learning
E Heim, M Berger, LM Seversky, M Hauskrecht
Proceedings of the 2015 SIAM International Conference on Data Mining, 271-279, 2015
172015
Active perceptual similarity modeling with auxiliary information
E Heim, M Berger, L Seversky, M Hauskrecht
arXiv preprint arXiv:1511.02254, 2015
152015
Exploiting Class Learnability in Noisy Data
M Klawonn, E Heim, J Hendler
Thirty-Third AAAI Conference on Artificial Intelligence, 2019
52019
Relative comparison kernel learning with auxiliary kernels
E Heim, H Valizadegan, M Hauskrecht
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2014
52014
Robust and Secure AI
H Barmer, R Dzombak, M Gaston, E Heim, V Palat, F Redner, T Smith, ...
Carnegie Mellon University, 2021
32021
Sparse multidimensional patient modeling using auxiliary confidence labels
E Heim, M Hauskrecht
2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM …, 2015
32015
Harnessing AI and robotics in humanitarian assistance and disaster response
T Manzini, RR Murphy, E Heim, C Robinson, G Zarrella, R Gupta
Science robotics 8 (80), eadj2767, 2023
12023
What is Your Metric Telling You? Evaluating Classifier Calibration under Context-Specific Definitions of Reliability
J Kirchenbauer, J Oaks, E Heim
arXiv preprint arXiv:2205.11454, 2022
12022
Measuring AI Systems Beyond Accuracy
V Turri, R Dzombak, E Heim, N VanHoudnos, J Palat, A Sinha
arXiv preprint arXiv:2204.04211, 2022
12022
Efficiently and effectively learning models of similarity from human feedback
E Heim
University of Pittsburgh, 2015
12015
Towards Better Understanding of Domain Shift on Linear-Probed Visual Foundation Models
E Heim
Neural Information Processing Workshops, I Can't Believe It's Not Better …, 2023
2023
A network model that combines latent factors and sparse graphs
N Suh, X Huo, E Heim, L Seversky
Statistical Analysis and Data Mining: The ASA Data Science Journal 14 (2 …, 2021
2021
xBD: A Dataset for Assessing Building Damage from Satellite Imagery
R Gupta, R Hosfelt, S Sajeev, N Patel, B Goodman, J Doshi, E Heim, ...
arXiv preprint arXiv:1911.09296, 2019
2019
A Practitioner's Guide to Maximum Causal Entropy Inverse Reinforcement Learning, Starting from Markov Decision Processes
E Heim, ...
2019
Measuring Beyond Accuracy
V Turri, R Dzombak, E Heim, N van Houdnos, J Palat, A Sinha
Spatial Active Learning For Cost-Effective Sensing and Feature Extraction
L Magee, H NEU, LM Seversky, E Heim
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