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Journal Articles
- 2023
[j6]Yizheng Chen
, Lequan Yu, Jen-Yeu Wang, Neil Panjwani, Jean-Pierre Obeid, Wu Liu, Lianli Liu, Nataliya Kovalchuk, Michael Francis Gensheimer, Lucas Kas Vitzthum, Beth M. Beadle, Daniel T. Chang, Quynh-Thu Le, Bin Han, Lei Xing:
Adaptive Region-Specific Loss for Improved Medical Image Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 45(11): 13408-13421 (2023)- 2021
- [j5]Michael Francis Gensheimer, Sonya Aggarwal, Kathryn R. K. Benson, Justin N. Carter, Solomon Henry, Douglas J. Wood, Scott G. Soltys, Steven Hancock, Erqi Pollom, Nigam H. Shah, Daniel T. Chang:
Automated model versus treating physician for predicting survival time of patients with metastatic cancer. J. Am. Medical Informatics Assoc. 28(6): 1108-1116 (2021) - 2020
- [j4]Bulat Ibragimov, Diego A. S. Toesca, Daniel T. Chang, Yixuan Yuan, Albert C. Koong, Lei Xing:
Automated hepatobiliary toxicity prediction after liver stereotactic body radiation therapy with deep learning-based portal vein segmentation. Neurocomputing 392: 181-188 (2020) - 2019
- [j3]Bulat Ibragimov, Diego A. S. Toesca, Yixuan Yuan, Albert C. Koong, Daniel T. Chang, Lei Xing:
Neural Networks for Deep Radiotherapy Dose Analysis and Prediction of Liver SBRT Outcomes. IEEE J. Biomed. Health Informatics 23(5): 1821-1833 (2019) - 2016
- [j2]Jingtao Lu, Michael-Rock Goldsmith, Christopher M. Grulke, Daniel T. Chang, Raina D. Brooks, Jeremy A. Leonard, Martin B. Phillips, Ethan D. Hypes, Matthew J. Fair, Rogelio Tornero-Velez, Jeffre Johnson, Curtis C. Dary, Yu-Mei Tan:
Developing a Physiologically-Based Pharmacokinetic Model Knowledgebase in Support of Provisional Model Construction. PLoS Comput. Biol. 12(2) (2016) - 1997
- [j1]V. Srinivasan, Daniel T. Chang:
Object Persistence in Object-Oriented Applications. IBM Syst. J. 36(1): 66-87 (1997)
Conference and Workshop Papers
- 2018
- [c9]Bulat Ibragimov, Diego A. S. Toesca, Yixuan Yuan, Albert C. Koong, Daniel T. Chang, Lei Xing:
Deep 3D Dose Analysis for Prediction of Outcomes After Liver Stereotactic Body Radiation Therapy. MICCAI (2) 2018: 684-692 - 1999
- [c8]Jyh-Herng Chow, Josephine M. Cheng, Daniel T. Chang, Jane Xu:
Index Design for Structured Documents Based on Abstraction. DASFAA 1999: 89-96 - 1997
- [c7]Daniel T. Chang, Stefan Covaci:
The OMG Mobile Agent Facility: A Submission. Mobile Agents 1997: 98-110 - [c6]Daniel T. Chang, Neelakantan Sundaresan:
A Communication Infrastrutrue for Java Agent Systems. PDPTA 1997: 245-255 - 1995
- [c5]Barrett R. Bryant, Daniel T. Chang, Prakash K. Muthukrishnan, Viswanathan Vaidyanathan:
Automatic Parallelization of Object-Oriented Programming Languages Using Tuple Space. ACM Conference on Computer Science 1995: 89-96 - [c4]Jen-Yao Chung, Yi-Jing Lin, Daniel T. Chang:
Object and relational databases. OOPSLA Addendum 1995: 164-169 - 1993
- [c3]Barrett R. Bryant, Chandrasekaran Venkatapathy, Daniel T. Chang:
Concurrency Control of Persistent Object Transactions in CORAL. CISMOD 1993 - [c2]Daniel T. Chang:
HieNet: A User-Centered Approach for Automatic Link Generation. Hypertext 1993: 145-158 - 1990
- [c1]Daniel T. Chang:
CORAL: a concurrent object-oriented system for constructing and executing sequential, parallel and distributed applications. OOPSLA/ECOOP Workshop on Object-based Concurrent Programming 1990: 26-30
Informal and Other Publications
- 2024
- [i24]Daniel T. Chang:
Hypergraph: A Unified and Uniform Definition with Application to Chemical Hypergraph. CoRR abs/2405.12235 (2024) - 2023
- [i23]Daniel T. Chang:
Variational Quantum Classifiers for Natural-Language Text. CoRR abs/2303.02469 (2023) - [i22]Daniel T. Chang:
Concept-Oriented Deep Learning with Large Language Models. CoRR abs/2306.17089 (2023) - 2022
- [i21]Daniel T. Chang:
Exemplar-Based Contrastive Self-Supervised Learning with Few-Shot Class Incremental Learning. CoRR abs/2202.02601 (2022) - [i20]Daniel T. Chang:
Dual Embodied-Symbolic Concept Representations for Deep Learning. CoRR abs/2203.00600 (2022) - [i19]Daniel T. Chang:
Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs. CoRR abs/2205.06783 (2022) - [i18]Daniel T. Chang:
Distance-Geometric Graph Attention Network (DG-GAT) for 3D Molecular Geometry. CoRR abs/2207.08023 (2022) - [i17]Daniel T. Chang:
Parameterized Quantum Circuits with Quantum Kernels for Machine Learning: A Hybrid Quantum-Classical Approach. CoRR abs/2209.14449 (2022) - [i16]Daniel T. Chang:
Variational Quantum Kernels with Task-Specific Quantum Metric Learning. CoRR abs/2211.05225 (2022) - 2021
- [i15]Daniel T. Chang:
Probabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabilistic Models. CoRR abs/2106.00120 (2021) - [i14]Daniel T. Chang:
Bayesian Neural Networks: Essentials. CoRR abs/2106.13594 (2021) - [i13]Daniel T. Chang:
Hybrid Bayesian Neural Networks with Functional Probabilistic Layers. CoRR abs/2107.07014 (2021) - [i12]Daniel T. Chang:
Concept Representation Learning with Contrastive Self-Supervised Learning. CoRR abs/2112.05677 (2021) - 2020
- [i11]Daniel T. Chang:
Geometric Graph Representations and Geometric Graph Convolutions for Deep Learning on Three-Dimensional (3D) Graphs. CoRR abs/2006.01785 (2020) - [i10]Daniel T. Chang:
Distance-Geometric Graph Convolutional Network (DG-GCN). CoRR abs/2007.03513 (2020) - 2019
- [i9]Daniel T. Chang:
Probabilistic Generative Deep Learning for Molecular Design. CoRR abs/1902.05148 (2019) - [i8]Daniel T. Chang:
Tiered Latent Representations and Latent Spaces for Molecular Graphs. CoRR abs/1904.02653 (2019) - [i7]Daniel T. Chang:
Tiered Graph Autoencoders with PyTorch Geometric for Molecular Graphs. CoRR abs/1908.08612 (2019) - [i6]Daniel T. Chang:
Deep Learning for Molecular Graphs with Tiered Graph Autoencoders and Graph Classification. CoRR abs/1910.11390 (2019) - [i5]Daniel T. Chang:
Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax. CoRR abs/1912.05686 (2019) - 2018
- [i4]Imon Banerjee, Michael Francis Gensheimer, Douglas J. Wood, Solomon Henry, Daniel T. Chang, Daniel L. Rubin:
Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) Utilizing Free-Text Clinical Narratives. CoRR abs/1801.03058 (2018) - [i3]Daniel T. Chang:
Concept-Oriented Deep Learning. CoRR abs/1806.01756 (2018) - [i2]Daniel T. Chang:
Concept-Oriented Deep Learning: Generative Concept Representations. CoRR abs/1811.06622 (2018) - [i1]Daniel T. Chang:
Latent Variable Modeling for Generative Concept Representations and Deep Generative Models. CoRR abs/1812.11856 (2018)
Coauthor Index
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