Search Results for « Latent Space Hypothesis »

Artificial intelligence is a powerful tool that could be deployed to accelerate the scientific enterprise. Here we address a major unmet need: use of existing scientific literature to generate novel hypotheses. We use a deep symmetry between the fields of neuroscience and developmental bioelectricity to test a tool, FieldSHIFT, that generates scientific text from existing published studies. We show how this system helps human scientists explore a latent space of papers that could exist, which provides a rich field of suggested future research. We then test a key prediction of this model using bioinformatics, showing a surprising conservation of molecular mechanisms involved in cognitive behavior and developmental morphogenesis. By allowing scientists to rapidly explore symmetries and meta-parameters that exist in a corpus of scientific papers, we show how machine learning can potentiate human creativity and assist with one of the most interesting and crucial aspects of research: identifying insights from data and generating potential candidates for research agendas.

<p>Computational design tools enable designers to construct and manipulate representations of design artifacts to arrive at a solution. However, the constraints of deterministic programming impose a high cost of tedium and inflexibility to exploring design alternatives through these models. They require designers to express high-level design intent through sequences of low-level operations. Generative neural networks are able to construct generalised models of images which capture principles implicit within them. The latent spaces of these models can be sampled to create novel images and to perform semantic operations. This presents the opportunity for more meaningful and efficient design experimentation, where designers are able to express design intent through principles inferred by the model, instead of sequences of low-level operations. A general purpose software prototype has been devised and evaluated to investigate the affordances of such a tool. This software — termed a SpaceSheet — takes the form of a spreadsheet interface and enables users to explore a latent space of fonts. User testing and observation of task-based evaluations revealed that the tool enabled a novel top-down approach to design experimentation. This mode of working required a new set of skills for users to derive meaning and navigate within the model effectively. Despite this, a rudimentary understanding was observed to be sufficient to enable designers and non-designers alike to explore design possibilities more effectively.</p>

This paper presents novel visual diagnostics for understanding and refining iterative optimization procedures in high-dimensional data analysis. Through innovative Latent Space Analysis & Embedding Visualization techniques, the approach provides a unique feedback mechanism, revealing how projections evolve and where optimization paths encounter complexities. This enables practitioners to diagnose and address intricacies within model transformations, ensuring robust and insightful data exploration.

<p><strong>Deep generative models have greatly advanced the field of artificial intelligence by learning the distribution of unlabelled datasets. In this thesis, we aim to develop a novel variational autoencoder (VAE)-based deep generative model that learns meaningful representations for effective unsupervised clustering in the latent space of high-dimensional datasets.</strong></p><p>Existing VAE-based deep clustering algorithms assume a mixture distribution either for the variational posterior over the latent variables or for the prior, to approximate the distribution of unlabelled datasets. The proposed mixtures variational autoencoders (MVAE) model assumes a mixture distribution for both the prior and variational posterior over the latent variables within the VAE framework.</p><p>We apply the variational inference to construct the evidence lower bound (ELBO) of the marginal log-likelihood function for our proposed MVAE model and then derive the analytical form for the clustering assignment estimate, also called the posterior probability estimate. This estimate is associated with each component of the VAE framework and enables the optimization of two distinct ELBO functions based on soft and hard assignment approaches. The training procedure for MVAE involves the joint optimization of both clustering assignment probabilities and model parameters. We evaluate the clustering performance on the latent embeddings of a dataset using both the posterior probability estimate and the Gaussian mixture model (GMM) methods.</p><p>We proposed two variants of the expectation-maximization (EM) algorithm to optimize the parameters for the MVAE model. The MVAE (EM) algorithm provides two distinct, numerically stable solutions for the posterior probability estimate: MVAE(EM-V1), where the estimate is associated with only the prior distribution, and MVAE(EM-V2), where the estimate incorporates both the variational posterior and the prior distribution. Among the proposed models, MVAE(EM-V2) requires less computation time for training. Additionally, this model achieves superior clustering performance on most datasets compared to the baseline models.</p><p>We modified the MVAE(EM-V2) algorithm by including a coefficient (β) with the regularizer term. The trained regularized mixtures VAE model with a small regularization coefficient (β < 1) achieves good unsupervised clustering performance on the test datasets. We further propose a variant of the regularized mixtures VAE model in which the regularization coefficient follows an annealing schedule from β > 1 to β < 1. The scheduled regularized model demonstrates superior clustering accuracy across most benchmark datasets compared to state-of-the-art deep clustering algorithms.</p><p>In this thesis, we point out that assuming a mixture distribution for both the prior and variational posterior components over the latent variables within the VAE framework enhances unsupervised clustering in the latent space. Our proposed models outperform state-of-the-art deep clustering algorithms, including VADE and k-DVAE, as well as the standard VAE model, in cluster analysis of latent representations across most benchmark datasets. Additionally, the proposed models demonstrate reasonable reconstruction performance and generate realistic examples from the latent space.</p>