573 research outputs found
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Evaluation Framework for Understanding Sensitive Attribute Association Bias in Latent Factor Recommendation Algorithms
We present a novel evaluation framework for representation bias in latent
factor recommendation (LFR) algorithms. Our framework introduces the concept of
attribute association bias in recommendations allowing practitioners to explore
how recommendation systems can introduce or amplify stakeholder representation
harm. Attribute association bias (AAB) occurs when sensitive attributes become
semantically captured or entangled in the trained recommendation latent space.
This bias can result in the recommender reinforcing harmful stereotypes, which
may result in downstream representation harms to system consumer and provider
stakeholders. LFR models are at risk of experiencing AAB due to their ability
to entangle explicit and implicit attributes into the trained latent space.
Understanding this phenomenon is essential due to the increasingly common use
of entity vectors as attributes in downstream components in hybrid industry
recommendation systems. We provide practitioners with a framework for executing
disaggregated evaluations of AAB within broader algorithmic auditing
frameworks. Inspired by research in natural language processing (NLP) observing
gender bias in word embeddings, our framework introduces AAB evaluation methods
specifically for recommendation entity vectors. We present four evaluation
strategies for sensitive AAB in LFR models: attribute bias directions,
attribute association bias metrics, classification for explaining bias, and
latent space visualization. We demonstrate the utility of our framework by
evaluating user gender AAB regarding podcast genres with an industry case study
of a production-level DNN recommendation model. We uncover significant levels
of user gender AAB when user gender is used and removed as a model feature
during training, pointing to the potential for systematic bias in LFR model
outputs
08421 Abstracts Collection -- Uncertainty Management in Information Systems
From October 12 to 17, 2008 the Dagstuhl Seminar 08421 \u27`Uncertainty Management in Information Systems \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. The abstracts of the plenary and session talks given during the seminar as well as those of the shown demos are put together in this paper
Sensitive Attribute Association Bias in Latent Factor Recommendation Algorithms: Theory and In Practice
This dissertation presents methods for evaluating and mitigating a relatively unexplored bias topic in recommendation systems, which we refer to as attribute association bias. Attribute association bias (AAB) can be introduced when leveraging latent factor recommendation models due to their ability to entangle model and implicit attributes into the trained latent space. This type of bias occurs when entity embeddings showcase significant levels of association with specific types of explicit or implicit entity attributes, thus having the potential to introduce representative harms for both consumer and provider stakeholders. We present a novel analysis method framework to help practitioners evaluate their latent factor recommendation models for AAB. This framework consists of three main techniques for gaining insight into sensitive AAB in the recommendation latent space: bias direction creation, bias evaluation metrics, and multi-group evaluation. Methods within our evaluation framework were inspired by techniques presented by the natural language processing research community for measuring gender bias in learned language representations. Additionally, we explore how this bias can be reinforced and produce feedback loops via retraining. Finally, we explore possible mitigation techniques for addressing said bias. Primarily, we demonstrate our methodology with two case studies that evaluate user gender association bias in latent factor recommendation. With our methods, we uncover the existence of user gender association bias and compare the various methods we propose to help guide practitioners in how best to use our techniques for their systems. In addition to exploring user gender, we experiment with measuring user age association bias as a means for evaluating non-binary AAB
Browse-to-search
This demonstration presents a novel interactive online shopping application based on visual search technologies. When users want to buy something on a shopping site, they usually have the requirement of looking for related information from other web sites. Therefore users need to switch between the web page being browsed and other websites that provide search results. The proposed application enables users to naturally search products of interest when they browse a web page, and make their even causal purchase intent easily satisfied. The interactive shopping experience is characterized by: 1) in session - it allows users to specify the purchase intent in the browsing session, instead of leaving the current page and navigating to other websites; 2) in context - -the browsed web page provides implicit context information which helps infer user purchase preferences; 3) in focus - users easily specify their search interest using gesture on touch devices and do not need to formulate queries in search box; 4) natural-gesture inputs and visual-based search provides users a natural shopping experience. The system is evaluated against a data set consisting of several millions commercial product images. © 2012 Authors
Crowdsourcing for Engineering Design: Objective Evaluations and Subjective Preferences
Crowdsourcing enables designers to reach out to large numbers of people who may not have been previously considered when designing a new product, listen to their input by aggregating their preferences and evaluations over potential designs, aiming to improve ``good'' and catch ``bad'' design decisions during the early-stage design process. This approach puts human designers--be they industrial designers, engineers, marketers, or executives--at the forefront, with computational crowdsourcing systems on the backend to aggregate subjective preferences (e.g., which next-generation Brand A design best competes stylistically with next-generation Brand B designs?) or objective evaluations (e.g., which military vehicle design has the best situational awareness?). These crowdsourcing aggregation systems are built using probabilistic approaches that account for the irrationality of human behavior (i.e., violations of reflexivity, symmetry, and transitivity), approximated by modern machine learning algorithms and optimization techniques as necessitated by the scale of data (millions of data points, hundreds of thousands of dimensions).
This dissertation presents research findings suggesting the unsuitability of current off-the-shelf crowdsourcing aggregation algorithms for real engineering design tasks due to the sparsity of expertise in the crowd, and methods that mitigate this limitation by incorporating appropriate information for expertise prediction. Next, we introduce and interpret a number of new probabilistic models for crowdsourced design to provide large-scale preference prediction and full design space generation, building on statistical and machine learning techniques such as sampling methods, variational inference, and deep representation learning. Finally, we show how these models and algorithms can advance crowdsourcing systems by abstracting away the underlying appropriate yet unwieldy mathematics, to easier-to-use visual interfaces practical for engineering design companies and governmental agencies engaged in complex engineering systems design.PhDDesign ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133438/1/aburnap_1.pd
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