Experiments with Generative Models for Dependency Tree Linearization by Futrell, Richard Landy Jones, Gibson, Edward A.

“…Our linearization models are derived from generative models for dependency structure (Eisner, 1996). …”
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Generative models for graph-based protein design by Ingraham, John, Garg, Vikas Kamur, Barzilay, Regina, Jaakkola, Tommi S

“…In this work, we introduce a conditional generative model for protein sequences given 3D structures based on graph representations. …”
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Generative models for graph-based protein design by Ingraham, John, Garg, Vikas Kamur, Barzilay, Regina, Jaakkola, Tommi S

“…In this work, we introduce a conditional generative model for protein sequences given 3D structures based on graph representations. …”
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Learning generative models across incomparable spaces by Bunne, C, Alvarez-Melis, D, Krause, A, Jegelka, S

“…In this work, we propose an approach to learn generative models across such incomparable spaces, and demonstrate how to steer the learned distribution towards target properties. …”
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Learning generative models across incomparable spaces by Bunne, Charlotte, Alvarez Melis, David, Krause, Andreas, Jegelka, Stefanie Sabrina

“…In this work, we propose an approach to learn generative models across such incomparable spaces, and demonstrate how to steer the learned distribution towards target properties. …”
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Generative Modeling of Audible Shapes for Object Perception by Zhang, Zhoutong, Wu, Jiajun, Li, Qiujia, Huang, Zhengjia, Traer, James, McDermott, Josh H., Tenenbaum, Joshua B., Freeman, William T.

“…Through comparison with audio recordings and human behavioral studies, we validate the accuracy of the sounds it generates. Using this generative model, we are able to construct a synthetic audio-visual dataset, namely Sound-20K, for object perception tasks. …”
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Automatically generated model for light alkene combustion by Pio, Gianmaria, Dong, Xiaorui, Salzano, Ernesto, Green, William H.

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Exploring Generative Models with Middle School Students by Ali, Safinah, DiPaola, Daniella, Lee, Irene, Hong, Jenna, Breazeal, Cynthia

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Structured Diffusion Processes in Deep Generative Models by Jing, Bowen

“…Diffusion generative models have emerged as a powerful, versatile, and elegant generative modeling framework for diverse data modalities. …”
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Graph and Geometry Generative Modeling for Drug Discovery by Xu, Minkai, Liu, Meng, Jin, Wengong, Ji, Shuiwang, Leskovec, Jure, Ermon, Stefano

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Data Attribution: From Classifiers to Generative Models by Georgiev, Kristian

“…Moreover, existing methods are often tailored to the supervised learning setting, and are not well-defined for generative models. In this thesis, we introduce TRAK (Tracing with the Randomly-projected After Kernel), a data attribution method that is both effective and computationally tractable for large-scale, differentiable models. …”
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Generative models for simulation of KamLAND-Zen by Fu, Zhenghao, Grant, Christopher, Krawiec, Dominika M., Li, Aobo, Winslow, Lindley A.

“…Traditional Monte Carlo (MC) simulations can be supplemented by machine-learning-based generative models. This work describes the performance of generative models that we designed for monolithic liquid scintillator detectors like KamLAND to produce accurate simulation data without a predefined physics model. …”
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Indeterminacy of relative prices in overlapping generations models by Kehoe, Timothy Jerome, Levine, David K.

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Generative models of brain connectivity for population studies by Venkataraman, Archana, Ph. D. Massachusetts Institute of Technology

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Differentiable generative models for trajectory data analytics by Li, Xiucheng

“…In this dissertation, we seek to effectively and efficiently distill knowledge from trajectory data with differentiable generative models. We develop three flexible generative models with efficiency in mind. …”
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Image-to-image translation based on generative models by Tang, Mengxiao

“…In this thesis, generative models are used to capture the distribution of images, and the potentials of generative models on the image-to-image translation tasks are explored. …”
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Deep generative modeling for image synthesis and manipulation by Yu, Yingchen

“…With the rise of deep neural networks (DNNs), deep generative models have defined a new state-of-the-art in various image generation and manipulation tasks. …”
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Highly controllable human motion generation model by Huang, Jingfang

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Learning affordances in object-centric generative models by Wu, Y, Kasewa, S, Groth, O, Salter, S, Sun, L, Parker Jones, O, Posner, H

“…The embedding learned by the generative model captures the factors of variation in object geometry, e.g. length, width, and configuration. …”

Generative modelling for supervised, unsupervised and private learning by Jordon, J

“…<p>In this thesis we develop several state-of-the-art generative modelling-based approaches for a variety of supervised, unsupervised and private learning problems. …”