Publications

@inproceedings{chen_dont_nodate,

title = {Don't Look into the Dark: Latent Codes for Pluralistic Image Inpainting},

author = {Haiwei Chen and Yajie Zhao},

url = {https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Dont_Look_into_the_Dark_Latent_Codes_for_Pluralistic_Image_CVPR_2024_paper.html},

pages = {7591–7600},

abstract = {We present a method for large-mask pluralistic image inpainting based on the generative framework of discrete latent codes. Our method learns latent priors discretized as tokens by only performing computations at the visible locations of the image. This is realized by a restrictive partial encoder that predicts the token label for each visible block a bidirectional transformer that infers the missing labels by only looking at these tokens and a dedicated synthesis network that couples the tokens with the partial image priors to generate coherent and pluralistic complete image even under extreme mask settings. Experiments on public benchmarks validate our design choices as the proposed method outperforms strong baselines in both visual quality and diversity metrics.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{artstein_augmenting_nodate,

title = {Augmenting Training Data for a Virtual Character Using GPT-3.5},

author = {Ron Artstein and Elizabeth Chen},

url = {https://journals.flvc.org/FLAIRS/article/view/135552},

volume = {37},

publisher = {Tyhe Florida Artificial Intelligence Research Society},

abstract = {This paper compares different methods of using a large lan-guage model (GPT-3.5) for creating synthetic training datafor a retrieval-based conversational character. The trainingdata are in the form of linked questions and answers, whichallow a classifier to retrieve a pre-recorded answer to an un-seen question; the intuition is that a large language modelcould predict what human users might ask, thus saving theeffort of collecting real user questions as training data. Re-sults show small improvements in test performance for allsynthetic datasets. However, a classifier trained on only smallamounts of collected user data resulted in a higher F-scorethan the classifiers trained on much larger amounts of syn-thetic data generated using GPT-3.5. Based on these results,we see a potential in using large language models for gener-ating training data, but at this point it is not as valuable ascollecting actual user data for training.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{mozgai_persuasive_nodate,

title = {Persuasive Technology for Suicide Prevention: A Virtual Human mHealth Application},

author = {Sharon Mozgai and Albert A Rizzo and Arno Hartholt},

abstract = {We	are	demoing	Battle	Buddy,	an	mHealth	application	designed	to	support	access	to	physical	and	mental	wellness	content	as	well	as	safety	planning	for	U.S.	military	veterans.	This	virtual	human	interface	will	collect	multimodal	data	through	passive	sensors	native	to	popular	wearables	(e.g.,	Apple	Watch)	and	deliver	adaptive	multimedia	content	specifically	tailored	to	the	user	in	the	interdependent	domains	of	physical,	cognitive,	and	emotional	health.	Battle	Buddy	can	deliver	health	interventions	matched	to	the	individual	user	via	novel	adaptive	logic-based	algorithms	while	employing	various	behavior	change	techniques	(e.g.,	goal-setting,	barrier	identification,	rewards,	modeling,	etc.).	All	interactions	were	specifically	designed	to	engage	and	motivate	by	employing	the	persuasive	strategies	of	(1)	personalization,	(2)	self-monitoring,	(3)	tunneling,	(4)	suggestion,	and	(5)	expertise.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}