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We present WineSensed, a large multimodal wine dataset for studying the relations between visual perception, language, and flavor.
Liii. on lines and planes of closest fit to systems of points in space
Karl Pearson · 1901
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Relations between two sets of variates
Hotelling Harold · 1936
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Generalized procrustes analysis
John C Gower · 1975
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A combined corner and edge detector
Chris Harris, Mike Stephens, et al · 1988
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Object modelling by registration of multiple range images
Yang Chen and Gérard Medioni · 1992
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Learning classification with unlabeled data
Virginia R de Sa · 1994
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Flavornet: A database of aroma compounds based on odor potency in natural products
H Arn and TE Acree · 1998
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Collection and analysis of perceived product inter-distances using multiple factor analysis: Application to the study of 10 white wines from the loire valley
Jérôme Pagès · 2005
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Generalized non-metric multidimensional scaling
Sameer Agarwal, Josh Wills, Lawrence Cayton, Gert Lanckriet, David Kriegman, and Serge Belongie · 2007
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Multidimensional scaling using majorization: Smacof in r
Jan de Leeuw and Patrick Mair · 2009
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Perceptual mapping of apples and cheeses using projective mapping and sorting
Michael A Nestrud and Harry T Lawless · 2010
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The sorted napping: A new holistic approach in sensory evaluation
Jérome Pagès, Marine Cadoret, and Sébastien Lê · 2010
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Multimodal deep learning
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and Andrew Y Ng · 2011
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Technology: The taste of things to come
Neil Savage · 2012
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Stochastic triplet embedding
Laurens van der Maaten and Kilian Weinberger · 2012
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Stochastic triplet embedding
Laurens Van Der Maaten and Kilian Weinberger · 2012
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Sensory profiling, the blurred line between sensory and consumer science. a review of novel methods for product characterization
Paula Varela and Gastón Ares · 2012
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Consumer-based product profiling: Application of partial napping® for sensory characterization of specialty beers by novices and experts
Davide Giacalone, Leticia Machado Ribeiro, and Michael Bom Frøst · 2013
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A cross-cultural study using napping®: Do korean and french consumers perceive various green tea products differently?
Young-Kyung Kim, Laureen Jombart, Dominique Valentin, and Kwang-Ok Kim · 2013
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Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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Learning concept embeddings with combined human-machine expertise
Michael Wilber, Iljung S Kwak, David Kriegman, and Serge Belongie · 2015
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Learning visual features from large weakly supervised data
Armand Joulin, Laurens Van Der Maaten, Allan Jabri, and Nicolas Vasilache · 2016
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Napping-ultra flash profile as a tool for category identification and subsequent model system formulation of caramel corn products
Emily Mayhew, Shelly Schmidt, and Soo-Yeun Lee · 2016
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Visually indicated sounds
Andrew Owens, Phillip Isola, Josh McDermott, Antonio Torralba, Edward H Adelson, and William T Freeman · 2016
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Look, listen and learn
Relja Arandjelovic and Andrew Zisserman · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Learning cross-modal embeddings for cooking recipes and food images
Amaia Salvador, Nicholas Hynes, Yusuf Aytar, Javier Marin, Ferda Ofli, Ingmar Weber, and Antonio Torralba · 2017
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Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and machine learning algorithms
Claudia Gonzalez Viejo, Sigfredo Fuentes, Damir Torrico, Kate Howell, and Frank R Dunshea · 2018
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Selection of important features and predicting wine quality using machine learning techniques
Mix and localize: Localizing sound sources in mixtures
Xixi Hu, Ziyang Chen, and Andrew Owens · 2022
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Mealrec: A meal recommendation dataset
Ming Li, Lin Li, Qing Xie, Jingling Yuan, and Xiaohui Tao · 2022
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Strumming to the beat: Audio-conditioned contrastive video textures
Medhini Narasimhan, Shiry Ginosar, Andrew Owens, Alexei A Efros, and Trevor Darrell · 2022
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Comparison of rata, cata, sorting and napping® as rapid alternatives to sensory profiling in a food industry environment
Nicolas Pineau, Alicia Girardi, Céline Lacoste Gregorutti, Laurence Fillion, and David Labbe · 2022
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Touch and go: Learning from human-collected vision and touch
Fengyu Yang, Chenyang Ma, Jiacheng Zhang, Jing Zhu, Wenzhen Yuan, and Andrew Owens · 2022
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Yogesh Gupta · 2018
Cited alongside, same era.
Exploring the limits of weakly supervised pretraining
Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens Van Der Maaten · 2018
Cited alongside, same era.
Umap: Uniform manifold approximation and projection for dimension reduction
Leland McInnes, John Healy, and James Melville · 2018
Cited alongside, same era.
Modeling pinot noir aroma profiles based on weather and water management information using machine learning algorithms: A vertical vintage analysis using artificial intelligence
Sigfredo Fuentes, Eden Tongson, Damir D Torrico, and Claudia Gonzalez Viejo · 2019
Cited alongside, same era.
Chocolate quality assessment based on chemical fingerprinting using near infra-red and machine learning modeling
Thejani M Gunaratne, Claudia Gonzalez Viejo, Nadeesha M Gunaratne, Damir D Torrico, Frank R Dunshea, and Sigfredo Fuentes · 2019
Cited alongside, same era.
Nutrient composition databases in the age of big data: fooddb, a comprehensive, real-time database infrastructure
Richard Andrew Harrington, Vyas Adhikari, Mike Rayner, and Peter Scarborough · 2019
Cited alongside, same era.
Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut · 2019
Cited alongside, same era.
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