The Gaussian process state-space model (GPSSM) has attracted much attention over the past decade. However, the model representation power of the GPSSM is far from satisfactory. Most GPSSM studies rely on the standard Gaussian process (GP) with a preliminary kernel, such as the squared exponential (SE) kernel or Mat\'{e}rn kernel, which limits the model representation power and its application in complex scenarios. To address this issue, this paper proposes a novel class of probabilistic state-space models, called TGPSSMs. By leveraging a parametric normalizing flow, the TGPSSMs enrich the GP priors in the standard GPSSM, rendering the state-space model more flexible and expressive. Additionally, we present a scalable variational inference algorithm for learning and inference in TGPSSMs, which provides a flexible and optimal structure for the variational distribution of latent states. The algorithm is interpretable and computationally efficient owing to the sparse representation of GP and the bijective nature of normalizing flow. To further improve the learning and inference performance of the proposed algorithm, we integrate a constrained optimization framework to enhance the state-space representation capabilities and optimize the hyperparameters. The experimental results based on various synthetic and real datasets corroborate that the proposed TGPSSM yields superior learning and inference performance compared to several state-of-the-art methods. The accompanying source code is available at \url{//github.com/zhidilin/TGPSSM}.
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Creating Computer Vision (CV) models remains a complex and taxing practice for end-users to build, inspect, and improve these models. Interactive ML perspectives have helped address some of these issues by considering a teacher-in-the-loop where planning, teaching, and evaluating tasks take place. To improve the experience of end-users with various levels of ML expertise, we designed and evaluated two interactive visualizations in the context of Sprite, a system for creating CV classification and detection models for images originating from videos. We study how these visualizations, as part of the machine teaching loop, help users identify (evaluate) and select (plan) images where a model is struggling and improve the model being trained. We found that users who had used the visualizations found more images across a wider set of potential types of model errors, as well as in assessing and contrasting the prediction behavior of one or more models, thus reducing the potential effort required to improve a model.
Temporal data such as time series can be viewed as discretized measurements of the underlying function. To build a generative model for such data we have to model the stochastic process that governs it. We propose a solution by defining the denoising diffusion model in the function space which also allows us to naturally handle irregularly-sampled observations. The forward process gradually adds noise to functions, preserving their continuity, while the learned reverse process removes the noise and returns functions as new samples. To this end, we define suitable noise sources and introduce novel denoising and score-matching models. We show how our method can be used for multivariate probabilistic forecasting and imputation, and how our model can be interpreted as a neural process.
Temporally indexed data are essential in a wide range of fields and of interest to machine learning researchers. Time series data, however, are often scarce or highly sensitive, which precludes the sharing of data between researchers and industrial organizations and the application of existing and new data-intensive ML methods. A possible solution to this bottleneck is to generate synthetic data. In this work, we introduce Time Series Generative Modeling (TSGM), an open-source framework for the generative modeling of synthetic time series. TSGM includes a broad repertoire of machine learning methods: generative models, probabilistic, and simulator-based approaches. The framework enables users to evaluate the quality of the produced data from different angles: similarity, downstream effectiveness, predictive consistency, diversity, and privacy. The framework is extensible, which allows researchers to rapidly implement their own methods and compare them in a shareable environment. TSGM was tested on open datasets and in production and proved to be beneficial in both cases. Additionally to the library, the project allows users to employ command line interfaces for synthetic data generation which lowers the entry threshold for those without a programming background.
Despite the many use cases for large language models (LLMs) in the design of chatbots in various industries and the research showing the importance of personalizing chatbots to cater to different personality traits, little work has been done to evaluate whether the behaviors of personalized LLMs can reflect certain personality traits accurately and consistently. We consider studying the behavior of LLM-based simulated agents which refer to as LLM personas and present a case study with GPT-3.5 (text-davinci-003) to investigate whether LLMs can generate content with consistent, personalized traits when assigned Big Five personality types and gender roles. We created 320 LLM personas (5 females and 5 males for each of the 32 Big Five personality types) and prompted them to complete the classic 44-item Big Five Inventory (BFI) and then write an 800-word story about their childhood. Results showed that LLM personas' self-reported BFI scores are consistent with their assigned personality types, with large effect sizes found on all five traits. Moreover, significant correlations were found between assigned personality types and some Linguistic Inquiry and Word Count (LIWC) psycholinguistic features of their writings. For instance, extroversion is associated with pro-social and active words, and neuroticism is associated with words related to negative emotions and mental health. Besides, we only found significant differences in using technological and cultural words in writing between LLM-generated female and male personas. This work provides a first step for further research on personalized LLMs and their applications in Human-AI conversation.
Shortcomings of current models of moderation have driven policy makers, scholars, and technologists to speculate about alternative models of content moderation. While alternative models provide hope for the future of online spaces, they can fail without proper scaffolding. Community moderators are routinely confronted with similar issues and have therefore found creative ways to navigate these challenges. Learning more about the decisions these moderators make, the challenges they face, and where they are successful can provide valuable insight into how to ensure alternative moderation models are successful. In this study, I perform a collaborative ethnography with moderators of r/AskHistorians, a community that uses an alternative moderation model, highlighting the importance of accounting for power in moderation. Drawing from Black feminist theory, I call this "intersectional moderation." I focus on three controversies emblematic of r/AskHistorians' alternative model of moderation: a disagreement over a moderation decision; a collaboration to fight racism on Reddit; and a period of intense turmoil and its impact on policy. Through this evidence I show how volunteer moderators navigated multiple layers of power through care work. To ensure the successful implementation of intersectional moderation, I argue that designers should support decision-making processes and policy makers should account for the impact of the sociotechnical systems in which moderators work.
Hierarchical Bayesian models of perception and learning feature prominently in contemporary cognitive neuroscience where, for example, they inform computational concepts of mental disorders. This includes predictive coding and hierarchical Gaussian filtering (HGF), which differ in the nature of hierarchical representations. Predictive coding assumes that higher levels in a given hierarchy influence the state (value) of lower levels. In HGF, however, higher levels determine the rate of change at lower levels. Here, we extend the space of generative models underlying HGF to include a form of nonlinear hierarchical coupling between state values akin to predictive coding and artificial neural networks in general. We derive the update equations corresponding to this generalization of HGF and conceptualize them as connecting a network of (belief) nodes where parent nodes either predict the state of child nodes or their rate of change. This enables us to (1) create modular architectures with generic computational steps in each node of the network, and (2) disclose the hierarchical message passing implied by generalized HGF models and to compare this to comparable schemes under predictive coding. We find that the algorithmic architecture instantiated by the generalized HGF is largely compatible with that of predictive coding but extends it with some unique predictions which arise from precision and volatility related computations. Our developments enable highly flexible implementations of hierarchical Bayesian models for empirical data analysis and are available as open source software.
Prior beliefs about the latent function to shape inductive biases can be incorporated into a Gaussian Process (GP) via the kernel. However, beyond kernel choices, the decision-making process of GP models remains poorly understood. In this work, we contribute an analysis of the loss landscape for GP models using methods from physics. We demonstrate $\nu$-continuity for Matern kernels and outline aspects of catastrophe theory at critical points in the loss landscape. By directly including $\nu$ in the hyperparameter optimisation for Matern kernels, we find that typical values of $\nu$ are far from optimal in terms of performance, yet prevail in the literature due to the increased computational speed. We also provide an a priori method for evaluating the effect of GP ensembles and discuss various voting approaches based on physical properties of the loss landscape. The utility of these approaches is demonstrated for various synthetic and real datasets. Our findings provide an enhanced understanding of the decision-making process behind GPs and offer practical guidance for improving their performance and interpretability in a range of applications.
Along with the massive growth of the Internet from the 1990s until now, various innovative technologies have been created to bring users breathtaking experiences with more virtual interactions in cyberspace. Many virtual environments with thousands of services and applications, from social networks to virtual gaming worlds, have been developed with immersive experience and digital transformation, but most are incoherent instead of being integrated into a platform. In this context, metaverse, a term formed by combining meta and universe, has been introduced as a shared virtual world that is fueled by many emerging technologies, such as fifth-generation networks and beyond, virtual reality, and artificial intelligence (AI). Among such technologies, AI has shown the great importance of processing big data to enhance immersive experience and enable human-like intelligence of virtual agents. In this survey, we make a beneficial effort to explore the role of AI in the foundation and development of the metaverse. We first deliver a preliminary of AI, including machine learning algorithms and deep learning architectures, and its role in the metaverse. We then convey a comprehensive investigation of AI-based methods concerning six technical aspects that have potentials for the metaverse: natural language processing, machine vision, blockchain, networking, digital twin, and neural interface, and being potential for the metaverse. Subsequently, several AI-aided applications, such as healthcare, manufacturing, smart cities, and gaming, are studied to be deployed in the virtual worlds. Finally, we conclude the key contribution of this survey and open some future research directions in AI for the metaverse.
Graph Convolution Networks (GCNs) manifest great potential in recommendation. This is attributed to their capability on learning good user and item embeddings by exploiting the collaborative signals from the high-order neighbors. Like other GCN models, the GCN based recommendation models also suffer from the notorious over-smoothing problem - when stacking more layers, node embeddings become more similar and eventually indistinguishable, resulted in performance degradation. The recently proposed LightGCN and LR-GCN alleviate this problem to some extent, however, we argue that they overlook an important factor for the over-smoothing problem in recommendation, that is, high-order neighboring users with no common interests of a user can be also involved in the user's embedding learning in the graph convolution operation. As a result, the multi-layer graph convolution will make users with dissimilar interests have similar embeddings. In this paper, we propose a novel Interest-aware Message-Passing GCN (IMP-GCN) recommendation model, which performs high-order graph convolution inside subgraphs. The subgraph consists of users with similar interests and their interacted items. To form the subgraphs, we design an unsupervised subgraph generation module, which can effectively identify users with common interests by exploiting both user feature and graph structure. To this end, our model can avoid propagating negative information from high-order neighbors into embedding learning. Experimental results on three large-scale benchmark datasets show that our model can gain performance improvement by stacking more layers and outperform the state-of-the-art GCN-based recommendation models significantly.
High spectral dimensionality and the shortage of annotations make hyperspectral image (HSI) classification a challenging problem. Recent studies suggest that convolutional neural networks can learn discriminative spatial features, which play a paramount role in HSI interpretation. However, most of these methods ignore the distinctive spectral-spatial characteristic of hyperspectral data. In addition, a large amount of unlabeled data remains an unexploited gold mine for efficient data use. Therefore, we proposed an integration of generative adversarial networks (GANs) and probabilistic graphical models for HSI classification. Specifically, we used a spectral-spatial generator and a discriminator to identify land cover categories of hyperspectral cubes. Moreover, to take advantage of a large amount of unlabeled data, we adopted a conditional random field to refine the preliminary classification results generated by GANs. Experimental results obtained using two commonly studied datasets demonstrate that the proposed framework achieved encouraging classification accuracy using a small number of data for training.
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