Why People Contribute Software Documentation
Deeksha M. Arya
Jin Guo
Martin P. Robillard
Software technologies are used by a large population of programmers with diverse backgrounds. To fulfill their need for information, enthusi… (see more)asts contribute numerous learning resources that vary in style and content, and act as documentation for the corresponding technology. We interviewed 26 volunteer contributors to understand why they create such documentation. We surface five motivations our informants had for contributing documentation, including to overcome issues they had faced with documentation and to capture their own learning. Among other findings, our observations suggest that the unique experience and background of documentation contributors provides the opportunity to create documentation that caters to users who have information needs and preferences similar to that of the contributor. CCS CONCEPTS • Software and its engineering
2024-04-14
IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (published)
doi.org
Why People Contribute Software Documentation
Deeksha M. Arya
Jin Guo
Martin P. Robillard
2024-04-14
IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (published)
doi.org
Assessing Numerical Analysis Performance with the Practi Mobile App
Maria Cutumisu
Kristin Garn
Raymond J. Spiteri
2024-04-12
Education Sciences (published)
doi.org
Model-independent Approach of the JUNO 8B Solar Neutrino Program
Jun-Zhang Zhao
B. Yue
Haoqi Lu
Yufeng Li
J. Ling
Zeyuan Yu
Angel Abusleme
Thomas Adam
Shakeel Ahmad
Rizwan Ahmed
Sebastiano Aiello
Muhammad Akram
Abid Aleem
Tsagkarakis Alexandros
Fengpeng An
Q. An
Giuseppe Andronico
Nikolay Anfimov
Vito Antonelli
Tatiana Antoshkina … (see 480 more)
Burin Asavapibhop
J. Andr'e
Didier Auguste
Weidong Bai
Nikita Balashov
Wander Baldini
Andrea Barresi
Davide Basilico
Eric Baussan
Marco Bellato
Antonio Bergnoli
Thilo Birkenfeld
Sylvie Blin
D. Blum
Simon Blyth
Anastasia Bolshakova
Mathieu Bongrand
Clément Bordereau
Dominique Breton
Augusto Brigatti
Riccardo Brugnera
Riccardo Bruno
Antonio Budano
Jose Busto
I. Butorov
Anatael Cabrera
Barbara Caccianiga
Hao Cai
Xiao Cai
Yanke Cai
Zucong Cai
Riccardo Callegari
Antonio Cammi
Agustin Campeny
C. Cao
Guofu Cao
Jun Cao
Rossella Caruso
C. Cerna
Chi Chan
Jinfan Chang
Yun Chang
Guoming Chen
Pingping Chen
Po-An Chen
Shaomin Chen
Xurong Chen
Yixue Chen
Yu Chen
Zhiyuan Chen
Zikang Chen
Jie Cheng
Yaping Cheng
Alexander Chepurnov
Alexey Chetverikov
Davide Chiesa
Pietro Chimenti
Artem Chukanov
Gérard Claverie
Catia Clementi
Barbara Clerbaux
Marta Colomer Molla
Selma Conforti Di Lorenzo
Daniele Corti
Flavio Dal Corso
Olivia Dalager
C. Taille
Z. Y. Deng
Ziyan Deng
Wilfried Depnering
Marco Diaz
Xuefeng Ding
Yayun Ding
Bayu Dirgantara
Sergey Dmitrievsky
Tadeas Dohnal
Dmitry Dolzhikov
Georgy Donchenko
Jianmeng Dong
Evgeny Doroshkevich
Marcos Dracos
Frédéric Druillole
Ran Du
S. X. Du
Stefano Dusini
Martin Dvorak
Timo Enqvist
H. Enzmann
Andrea Fabbri
D. Fan
Lei Fan
Jian Fang
Wen Fang
Marco Fargetta
Dmitry Fedoseev
Zheng-hao Fei
Li-Cheng Feng
Qichun Feng
R. Ford
Amélie Fournier
H. Gan
Feng Gao
Alberto Garfagnini
Arsenii Gavrikov
Marco Giammarchi
Nunzio Giudice
Maxim Gonchar
G. Gong
Hui Gong
Yuri Gornushkin
A. Gottel
Marco Grassi
Maxim Gromov
Vasily Gromov
M. H. Gu
Xiang Gu
Yunting Gu
M. Guan
Yuduo Guan
Nunzio Guardone
Cong Guo
Jingyuan Guo
Wanlei Guo
Xinheng Guo
Yuhang Guo
Paul Hackspacher
Caren Hagner
Ran Han
Yang Han
Miao He
W. He
Tobias Heinz
Patrick Hellmuth
Yue-kun Heng
Rafael Herrera
Yuenkeung Hor
Shaojing Hou
Yee Hsiung
Bei-Zhen Hu
Hang Hu
Jianrun Hu
Jun Hu
Shouyang Hu
Tao Hu
Yuxiang Hu
Zhuojun Hu
Guihong Huang
Hanxiong Huang
Kaixuan Huang
Wenhao Huang
Xinglong Huang
X. T. Huang
Yongbo Huang
Jiaqi Hui
L. Huo
Wenju Huo
Cédric Huss
Safeer Hussain
Ara Ioannisian
Roberto Isocrate
Beatrice Jelmini
Ignacio Jeria
Xiaolu Ji
Huihui Jia
Junji Jia
Siyu Jian
Di Jiang
Wei Jiang
Xiaoshan Jiang
X. Jing
Cécile Jollet
L. Kalousis
Philipp Kampmann
Li Kang
Rebin Karaparambil
Narine Kazarian
Amina Khatun
Khanchai Khosonthongkee
Denis Korablev
K. Kouzakov
Alexey Krasnoperov
Nikolay Kutovskiy
Pasi Kuusiniemi
Tobias Lachenmaier
Cecilia Landini
Sébastien Leblanc
Victor Lebrin
F. Lefèvre
R. Lei
Rupert Leitner
Jason Leung
Daozheng Li
Demin Li
Fei Li
Fule Li
Gaosong Li
Huiling Li
Mengzhao Li
Min Li
Nan Li
Qingjiang Li
Ruhui Li
Ruiting Lei
Shanfeng Li
Tao Li
Teng Li
Weidong Li
Wei-guo Li
Xiaomei Li
Xiaonan Li
Xinglong Li
Yi Li
Yichen Li
Zepeng Li
Zhaohan Li
Zhibing Li
Ziyuan Li
Zonghui Li
Hao Liang
Jiaming Yan
Ayut Limphirat
Gen Lin
Shengxin Lin
Tao Lin
Ivano Lippi
Yang Liu
Haidong Liu
H. Liu
Hongbang Liu
Hongjuan Liu
Hongtao Liu
Hui Liu
Jianglai Liu
Jinchang Liu
Min Liu
Qian Liu
Q. Liu
Runxuan Liu
Shubin Liu
Shulin Liu
Xiaowei Liu
Xiwen Liu
Yong Liu
Yunzhe Liu
Alexey Lokhov
Paolo Lombardi
Claudio Lombardo
K. Loo
Chuan Lu
Jingbin Lu
Junguang Lu
Shuxiang Lu
Bayarto Lubsandorzhiev
Sultim Lubsandorzhiev
Livia Ludhova
Arslan Lukanov
Daibin Luo
F. Luo
Guang Luo
Shu Luo
Wu Luo
Xiaojie Luo
Vladimir Lyashuk
B. Ma
Bing Ma
R. Q. Ma
Si Ma
Xiaoyan Ma
Xubo Ma
Jihane Maalmi
Jingyu Mai
Yury Malyshkin
Roberto Carlos Mandujano
Fabio Mantovani
Francesco Manzali
Xin Mao
Yajun Mao
S. Mari
F. Marini
Cristina Martellini
Gisèle Martin-chassard
Agnese Martini
Matthias Mayer
Davit Mayilyan
Ints Mednieks
Yu Meng
Anselmo Meregaglia
Emanuela Meroni
David J. Meyhofer
Mauro Mezzetto
Jonathan Andrew Miller
Lino Miramonti
Paolo Montini
Michele Montuschi
Axel Muller
M. Nastasi
D. Naumov
Elena Naumova
Diana Navas-Nicolas
Igor Nemchenok
Minh Thuan Nguyen Thi
Alexey Nikolaev
F. Ning
Zhe Ning
Hiroshi Nunokawa
Lothar Oberauer
Juan Pedro Ochoa-Ricoux
Alexander Olshevskiy
Domizia Orestano
Fausto Ortica
Rainer Othegraven
A. Paoloni
Sergio Parmeggiano
Y. P. Pei
Nicomede Pelliccia
Anguo Peng
Yu Peng
Yuefeng Peng
Z-R Peng
Frédéric Perrot
P. Petitjean
Fabrizio Petrucci
Oliver Pilarczyk
Luis Felipe Piñeres Rico
Artyom Popov
Pascal Poussot
Ezio Previtali
Fazhi Qi
M. Qi
Sen Qian
X. Qian
Zhen Qian
Hao-xue Qiao
Zhonghua Qin
S. Qiu
Gioacchino Ranucci
Neill Raper
A. Re
Henning Rebber
Abdel Rebii
Mariia Redchuk
Bin Ren
Jie Ren
Barbara Ricci
Mariam Rifai
Mathieu Roche
Narongkiat Rodphai
Aldo M. Romani
Bedřich Roskovec
X. Ruan
Arseniy Rybnikov
Andrey Sadovsky
Paolo Saggese
Simone Sanfilippo
Anut Sangka
Utane Sawangwit
Julia Sawatzki
Michaela Schever
Cédric Schwab
Konstantin Schweizer
Alexandr Selyunin
Andrea Serafini
Giulio Settanta
M. Settimo
Zhuang Shao
V. Sharov
Arina Shaydurova
Jingyan Shi
Yanan Shi
Vitaly Shutov
Andrey Sidorenkov
Fedor Šimkovic
Chiara Sirignano
Jaruchit Siripak
Monica Sisti
Maciej Slupecki
Mikhail Smirnov
Oleg Smirnov
Thiago Sogo-Bezerra
Sergey Sokolov
Julanan Songwadhana
Boonrucksar Soonthornthum
Albert Sotnikov
Ondvrej vSr'amek
Warintorn Sreethawong
A. Stahl
Luca Stanco
Konstantin Stankevich
Duvsan Vstef'anik
Hans Steiger
Jochen Steinmann
Tobias Sterr
M. Stock
Virginia Strati
Alexander Studenikin
Jun Su
Shifeng Sun
Xilei Sun
Yongjie Sun Sun
Yongzhao Sun
Zhengyang Sun
Narumon Suwonjandee
Michal Szelezniak
Jian Tang
Qiang Tang
Quan Tang
Xiao Tang
Alexander Tietzsch
Igor Tkachev
Tomas Tmej
M. Torri
K. Treskov
Andrea Triossi
Giancarlo Troni
Wladyslaw Trzaska
Cristina Tuve
Nikita Ushakov
Vadim Vedin
Giuseppe Verde
Maxim Vialkov
Benoit Viaud
Cornelius Moritz Vollbrecht
C. Volpe
Katharina von Sturm
Vit Vorobel
Dmitriy Voronin
Lucia Votano
Pablo Walker
Caishen Wang
Chung-Hsiang Wang
En Wang
Guoli Wang
Jian Wang
Jun Wang
Lucinda W. Wang
Meifen Wang
Meng Wang
Ruiguang Wang
Siguang Wang
Wei Wang
Wenshuai Wang
Xi Wang
Xiangyue Wang
Yangfu Wang
Yaoguang Wang
Yi Xing Wang
Yifang Wang
Yuanqing Wang
Yuman Wang
Zhe Wang
Zheng Wang
Zhimin Wang
Zongyi Wang
Apimook Watcharangkool
Wei Wei
Wenlu Wei
Yadong Wei
K. Wen
Kaile Wen
Christopher Wiebusch
S. Wong
Bjoern Wonsak
Diru Wu
Qun Wu
Zhi Wu
Michael Wurm
Jacques Wurtz
Christian Wysotzki
Yufei Xi
D. Xia
Xiang Xiao
Xiaochuan Xie
Yu-guang Xie
Z. P. Xie
Zhao-Liang Xin
Z. Xing
Benda D. Xu
Chengze Xu
Donglian Xu
Fanrong Xu
The physics potential of detecting 8B solar neutrinos will be exploited at the Jiangmen Underground Neutrino Observatory (JUNO), in a model-… (see more)independent manner by using three distinct channels of the charged current (CC), neutral current (NC), and elastic scattering (ES) interactions. Due to the largest-ever mass of 13C nuclei in the liquid scintillator detectors and the expected low background level, 8B solar neutrinos are observable in the CC and NC interactions on 13C for the first time. By virtue of optimized event selections and muon veto strategies, backgrounds from the accidental coincidence, muon-induced isotopes, and external backgrounds can be greatly suppressed. Excellent signal-to-background ratios can be achieved in the CC, NC, and ES channels to guarantee the observation of the 8B solar neutrinos. From the sensitivity studies performed in this work, we show that JUNO, with 10 yr of data, can reach the 1σ precision levels of 5%, 8%, and 20% for the 8B neutrino flux, sin 2 θ 12 , and Δ m 21 2 , respectively. Probing the details of both solar physics and neutrino physics would be unique and helpful. In addition, when combined with the Sudbury Neutrino Observatory measurement, the world's best precision of 3% is expected for the measurement of the 8B neutrino flux.
2024-04-12
The Astrophysical Journal (published)
doi.org
arxiv.org
Towards Causal Deep Learning for Vulnerability Detection
Md Mahbubur Rahman
Ira Ceka
Chengzhi Mao
Saikat Chakraborty
Baishakhi Ray
Wei Le
Deep learning vulnerability detection has shown promising results in recent years. However, an important challenge that still blocks it from… (see more) being very useful in practice is that the model is not robust under perturbation and it cannot generalize well over the out-of-distribution (OOD) data, e.g., applying a trained model to unseen projects in real world. We hypothesize that this is because the model learned non-robust features, e.g., variable names, that have spurious correlations with labels. When the perturbed and OOD datasets no longer have the same spurious features, the model prediction fails. To address the challenge, in this paper, we introduced causality into deep learning vulnerability detection. Our approach CausalVul consists of two phases. First, we designed novel perturbations to discover spurious features that the model may use to make predictions. Second, we applied the causal learning algorithms, specifically, do-calculus, on top of existing deep learning models to systematically remove the use of spurious features and thus promote causal based prediction. Our results show that CausalVul consistently improved the model accuracy, robustness and OOD performance for all the state-of-the-art models and datasets we experimented. To the best of our knowledge, this is the first work that introduces do calculus based causal learning to software engineering models and shows it's indeed useful for improving the model accuracy, robustness and generalization. Our replication package is located at https://figshare.com/s/0ffda320dcb96c249ef2.
2024-04-12
Proceedings of the IEEE/ACM 46th International Conference on Software Engineering (published)
doi.org
arxiv.org
Deep learning for high-resolution dose prediction in high dose rate brachytherapy for breast cancer treatment.
Sébastien Quetin
Boris Bahoric
Farhad Maleki
Shirin A. Enger
OBJECTIVE Monte Carlo (MC) simulations are the benchmark for accurate radiotherapy dose calculations, notably in patient-specific high dose … (see more)rate brachytherapy (HDR BT), in cases where considering tissue heterogeneities is critical. However, the lengthy computational time limits the practical application of MC simulations. Prior research used Deep Learning (DL) for dose prediction as an alternative to MC simulations. While accurate dose predictions akin to MC were attained, GPU limitations constrained these predictions to large voxels of 3mm × 3mm × 3mm. This study aimed to enable dose predictions as accurate as MC simulations in 1mm × 1mm × 1mm voxels within a clinically acceptable timeframe. Approach: Computed tomography scans of 98 breast cancer patients treated with Iridium-192-based HDR BT were used: 70 for training, 14 for validation, and 14 for testing. A new cropping strategy based on the distance to the seed was devised to reduce the volume size, enabling efficient training of 3D DL models using 1 mm × 1 mm × 1 mm dose grids. Additionally, novel DL architecture with layer-level fusion were proposed to predict MC simulated dose to medium-in-medium (Dm,m). These architectures fuse information from TG-43 dose to water-in-water (Dw,w) with patient tissue composition at the layer-level. Different inputs describing patient body composition were investigated. Main results: The proposed approach demonstrated state-of-the-art performance, on par with the MC Dm,m maps, but 300 times faster. The mean absolute percent error for dosimetric indices between the MC and DL-predicted complete treatment plans was 0.17%±0.15% for the planning target volume V100, 0.30%±0.32% for the skin D2cc, 0.82%±0.79% for the lung D2cc, 0.34%±0.29% for the chest wall D2cc and 1.08%±0.98% for the heart D2cc. Significance: Unlike the time-consuming MC simulations, the proposed novel strategy efficiently converts TG-43 Dw,w maps into precise Dm,m maps at high resolution, enabling clinical integration.
2024-04-11
Physics in Medicine and Biology (published)
doi.org
Deep learning for high-resolution dose prediction in high dose rate brachytherapy for breast cancer treatment
Sébastien Quetin
Boris Bahoric
Farhad Maleki
Shirin A. Enger
Objective. Monte Carlo (MC) simulations are the benchmark for accurate radiotherapy dose calculations, notably in patient-specific high dose… (see more) rate brachytherapy (HDR BT), in cases where considering tissue heterogeneities is critical. However, the lengthy computational time limits the practical application of MC simulations. Prior research used deep learning (DL) for dose prediction as an alternative to MC simulations. While accurate dose predictions akin to MC were attained, graphics processing unit limitations constrained these predictions to large voxels of 3 mm × 3 mm × 3 mm. This study aimed to enable dose predictions as accurate as MC simulations in 1 mm × 1 mm × 1 mm voxels within a clinically acceptable timeframe. Approach. Computed tomography scans of 98 breast cancer patients treated with Iridium-192-based HDR BT were used: 70 for training, 14 for validation, and 14 for testing. A new cropping strategy based on the distance to the seed was devised to reduce the volume size, enabling efficient training of 3D DL models using 1 mm × 1 mm × 1 mm dose grids. Additionally, novel DL architecture with layer-level fusion were proposed to predict MC simulated dose to medium-in-medium (D m,m ). These architectures fuse information from TG-43 dose to water-in-water (D w,w ) with patient tissue composition at the layer-level. Different inputs describing patient body composition were investigated. Main results. The proposed approach demonstrated state-of-the-art performance, on par with the MC D m,m maps, but 300 times faster. The mean absolute percent error for dosimetric indices between the MC and DL-predicted complete treatment plans was 0.17% ± 0.15% for the planning target volume V 100, 0.30% ± 0.32% for the skin D 2cc , 0.82% ± 0.79% for the lung D 2cc , 0.34% ± 0.29% for the chest wall D 2cc and 1.08% ± 0.98% for the heart D 2cc . Significance. Unlike the time-consuming MC simulations, the proposed novel strategy efficiently converts TG-43 D w,w maps into precise D m,m maps at high resolution, enabling clinical integration.
2024-04-11
Physics in Medicine and Biology (published)
doi.org
From the Lab to the Theater: An Unconventional Field Robotics Journey
Ali Imran
Vivek Shankar Vardharajan
Rafael Gomes Braga
Yann Bouteiller
Abdalwhab Abdalwhab
Matthis Di-Giacomo
Alexandra Mercader
Giovanni Beltrame
David St-Onge
2024-04-11
ArXiv (preprint)
doi.org
arxiv.org
Scalable Hierarchical Self-Attention with Learnable Hierarchy for Long-Range Interactions
Thuan Nguyen Anh Trang
Khang Nhat Ngo
Hugo Sonnery
Thieu Vo
Siamak Ravanbakhsh
Truong Son Hy
Self-attention models have made great strides toward accurately modeling a wide array of data modalities, including, more recently, graph-st… (see more)ructured data. This paper demonstrates that adaptive hierarchical attention can go a long way toward successfully applying transformers to graphs. Our proposed model Sequoia provides a powerful inductive bias towards long-range interaction modeling, leading to better generalization. We propose an end-to-end mechanism for a data-dependent construction of a hierarchy which in turn guides the self-attention mechanism. Using adaptive hierarchy provides a natural pathway toward sparse attention by constraining node-to-node interactions with the immediate family of each node in the hierarchy (e.g., parent, children, and siblings). This in turn dramatically reduces the computational complexity of a self-attention layer from quadratic to log-linear in terms of the input size while maintaining or sometimes even surpassing the standard transformer's ability to model long-range dependencies across the entire input. Experimentally, we report state-of-the-art performance on long-range graph benchmarks while remaining computationally efficient. Moving beyond graphs, we also display competitive performance on long-range sequence modeling, point-clouds classification, and segmentation when using a fixed hierarchy. Our source code is publicly available at https://github.com/HySonLab/HierAttention
2024-04-11
TMLR (accepted)
openreview.net
openreview.net
Temporal trends in disparities in COVID-19 seropositivity among Canadian blood donors
Yuan Yu
Matthew J Knight
Diana Gibson
Sheila F O’Brien
David Buckeridge
W Alton Russell
Abstract Background In Canada’s largest COVID-19 serological study, SARS-CoV-2 antibodies in blood donors have been monitored since 2020. … (see more)No study has analysed changes in the association between anti-N seropositivity (a marker of recent infection) and geographic and sociodemographic characteristics over the pandemic. Methods Using Bayesian multi-level models with spatial effects at the census division level, we analysed changes in correlates of SARS-CoV-2 anti-N seropositivity across three periods in which different variants predominated (pre-Delta, Delta and Omicron). We analysed disparities by geographic area, individual traits (age, sex, race) and neighbourhood factors (urbanicity, material deprivation and social deprivation). Data were from 420 319 blood donations across four regions (Ontario, British Columbia [BC], the Prairies and the Atlantic region) from December 2020 to November 2022. Results Seropositivity was higher for racialized minorities, males and individuals in more materially deprived neighbourhoods in the pre-Delta and Delta waves. These subgroup differences dissipated in the Omicron wave as large swaths of the population became infected. Across all waves, seropositivity was higher in younger individuals and those with lower neighbourhood social deprivation. Rural residents had high seropositivity in the Prairies, but not other regions. Compared to generalized linear models, multi-level models with spatial effects had better fit and lower error when predicting SARS-CoV-2 anti-N seropositivity by geographic region. Conclusions Correlates of recent COVID-19 infection have evolved over the pandemic. Many disparities lessened during the Omicron wave, but public health intervention may be warranted to address persistently higher burden among young people and those with less social deprivation.
2024-04-11
International Journal of Epidemiology (published)
doi.org
Association between arterial oxygen and mortality across critically ill patients with hematologic malignancies: results from an international collaborative network
Guillaume Dumas
Idunn S. Morris
Tamishta Hensman
Sean M. Bagshaw
Alexandre Demoule
Bruno Ferreyro
Achille Kouatchet
Virginie Lemiale
Djamel Mokart
Frédéric Pène
Sangeeta Mehta
Elie Azoulay
Laveena Munshi
Laurent Argaud
François Barbier
Dominique Benoit
Naike Bigé
Fabrice Bruneel
Emmanuel Canet
Yves Cohen … (see 30 more)
Michaël Darmon
Didier Gruson
Kada Klouche
Loay Kontar
Alexandre Lautrette
Christine Lebert
Guillaume Louis
Julien Mayaux
Anne-Pascale Meert
Anne-Sophie Moreau
Martine Nyunga
Vincent Peigne
Pierre Perez
Jean Herlé Raphalen
Carole Schwebel
Jean-Marie Tonnelier
Florent Wallet
Lara Zafrani
Bram Rochwerg
Farah Shoukat
Dean Fergusson
Paul Heffernan
Margaret Herridge
Sheldon Magder
Mark Minden
Rakesh Patel
Salman Qureshi
Aaron Schimmer
Santhosh Thyagu
Han Ting Wang
2024-04-10
Intensive Care Medicine (published)
doi.org
Deep Generative Sampling in the Dual Divergence Space: A Data-efficient&Interpretative Approach for Generative AI
Sahil Garg
Anderson Schneider
Anant Raj
Kashif Rasul
Yuriy Nevmyvaka
S. Gopal
Amit Dhurandhar
Guillermo A. Cecchi
Irina Rish
Building on the remarkable achievements in generative sampling of natural images, we propose an innovative challenge, potentially overly amb… (see more)itious, which involves generating samples of entire multivariate time series that resemble images. However, the statistical challenge lies in the small sample size, sometimes consisting of a few hundred subjects. This issue is especially problematic for deep generative models that follow the conventional approach of generating samples from a canonical distribution and then decoding or denoising them to match the true data distribution. In contrast, our method is grounded in information theory and aims to implicitly characterize the distribution of images, particularly the (global and local) dependency structure between pixels. We achieve this by empirically estimating its KL-divergence in the dual form with respect to the respective marginal distribution. This enables us to perform generative sampling directly in the optimized 1-D dual divergence space. Specifically, in the dual space, training samples representing the data distribution are embedded in the form of various clusters between two end points. In theory, any sample embedded between those two end points is in-distribution w.r.t. the data distribution. Our key idea for generating novel samples of images is to interpolate between the clusters via a walk as per gradients of the dual function w.r.t. the data dimensions. In addition to the data efficiency gained from direct sampling, we propose an algorithm that offers a significant reduction in sample complexity for estimating the divergence of the data distribution with respect to the marginal distribution. We provide strong theoretical guarantees along with an extensive empirical evaluation using many real-world datasets from diverse domains, establishing the superiority of our approach w.r.t. state-of-the-art deep learning methods.
2024-04-10
ArXiv (preprint)
doi.org
arxiv.org