3 research outputs found
Learning Bijective Feature Maps for Linear ICA
Separating high-dimensional data like images into independent latent factors,
i.e independent component analysis (ICA), remains an open research problem. As
we show, existing probabilistic deep generative models (DGMs), which are
tailor-made for image data, underperform on non-linear ICA tasks. To address
this, we propose a DGM which combines bijective feature maps with a linear ICA
model to learn interpretable latent structures for high-dimensional data. Given
the complexities of jointly training such a hybrid model, we introduce novel
theory that constrains linear ICA to lie close to the manifold of orthogonal
rectangular matrices, the Stiefel manifold. By doing so we create models that
converge quickly, are easy to train, and achieve better unsupervised latent
factor discovery than flow-based models, linear ICA, and Variational
Autoencoders on images.Comment: 8 page
Generative AI in the Construction Industry: A State-of-the-art Analysis
The construction industry is a vital sector of the global economy, but it
faces many productivity challenges in various processes, such as design,
planning, procurement, inspection, and maintenance. Generative artificial
intelligence (AI), which can create novel and realistic data or content, such
as text, image, video, or code, based on some input or prior knowledge, offers
innovative and disruptive solutions to address these challenges. However, there
is a gap in the literature on the current state, opportunities, and challenges
of generative AI in the construction industry. This study aims to fill this gap
by providing a state-of-the-art analysis of generative AI in construction, with
three objectives: (1) to review and categorize the existing and emerging
generative AI opportunities and challenges in the construction industry; (2) to
propose a framework for construction firms to build customized generative AI
solutions using their own data, comprising steps such as data collection,
dataset curation, training custom large language model (LLM), model evaluation,
and deployment; and (3) to demonstrate the framework via a case study of
developing a generative model for querying contract documents. The results show
that retrieval augmented generation (RAG) improves the baseline LLM by 5.2,
9.4, and 4.8% in terms of quality, relevance, and reproducibility. This study
provides academics and construction professionals with a comprehensive analysis
and practical framework to guide the adoption of generative AI techniques to
enhance productivity, quality, safety, and sustainability across the
construction industry.Comment: 74 pages, 11 figures, 20 table