Deep Discrete Encoders: Identifiable Deep Generative Models for Rich Data with Discrete Latent Layers

• URL: http://arxiv.org/abs/2501.01414v1
• Date: Thu, 02 Jan 2025 18:56:23 GMT
• Title: Deep Discrete Encoders: Identifiable Deep Generative Models for Rich Data with Discrete Latent Layers
• Authors: Seunghyun Lee, Yuqi Gu,
• Abstract summary: We propose an interpretable deep generative modeling framework for rich data types with discrete latent layers. We apply DDEs to three diverse real datasets for hierarchical topic modeling, image representation learning, response time modeling in educational testing, and obtain interpretable findings.
• Score: 13.545948734057268
• License:
• Abstract: In the era of generative AI, deep generative models (DGMs) with latent representations have gained tremendous popularity. Despite their impressive empirical performance, the statistical properties of these models remain underexplored. DGMs are often overparametrized, non-identifiable, and uninterpretable black boxes, raising serious concerns when deploying them in high-stakes applications. Motivated by this, we propose an interpretable deep generative modeling framework for rich data types with discrete latent layers, called Deep Discrete Encoders (DDEs). A DDE is a directed graphical model with multiple binary latent layers. Theoretically, we propose transparent identifiability conditions for DDEs, which imply progressively smaller sizes of the latent layers as they go deeper. Identifiability ensures consistent parameter estimation and inspires an interpretable design of the deep architecture. Computationally, we propose a scalable estimation pipeline of a layerwise nonlinear spectral initialization followed by a penalized stochastic approximation EM algorithm. This procedure can efficiently estimate models with exponentially many latent components. Extensive simulation studies validate our theoretical results and demonstrate the proposed algorithms' excellent performance. We apply DDEs to three diverse real datasets for hierarchical topic modeling, image representation learning, response time modeling in educational testing, and obtain interpretable findings.

Related papers

• OOD Detection with immature Models [8.477943884416023]
  Likelihood-based deep generative models (DGMs) have gained significant attention for their ability to approximate the distributions of high-dimensional data. These models lack a performance guarantee in assigning higher likelihood values to in-distribution (ID) inputs, data the models are trained on, compared to out-of-distribution (OOD) inputs. In this work, we demonstrate that using immature models,stopped at early stages of training, can mostly achieve equivalent or even superior results on this downstream task.
  arXiv  Detail & Related papers  (2025-02-02T15:14:17Z)
• Exploring Representation-Aligned Latent Space for Better Generation [86.45670422239317]
  We introduce ReaLS, which integrates semantic priors to improve generation performance. We show that fundamental DiT and SiT trained on ReaLS can achieve a 15% improvement in FID metric. The enhanced semantic latent space enables more perceptual downstream tasks, such as segmentation and depth estimation.
  arXiv  Detail & Related papers  (2025-02-01T07:42:12Z)
• A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional Data [0.41942958779358674]
  latent discriminative generative decoder (LDGD) employs both the data and associated labels in the manifold discovery process. We show that LDGD can robustly infer manifold and precisely predict labels for scenarios in that data size is limited.
  arXiv  Detail & Related papers  (2024-01-29T19:11:03Z)
• Learning Hierarchical Features with Joint Latent Space Energy-Based Prior [44.4434704520236]
  We study the fundamental problem of multi-layer generator models in learning hierarchical representations. We propose a joint latent space EBM prior model with multi-layer latent variables for effective hierarchical representation learning.
  arXiv  Detail & Related papers  (2023-10-14T15:44:14Z)
• SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion Models [0.0]
  Denoising Diffusion Probabilistic Models (DDPMs) have demonstrated significant promise in synthesizing realistic images from semantic layouts. In this paper, a conditional DDPM model capable of taking a semantic map and generating high-quality, diverse, and correspondingly accurate satellite images is implemented. The effectiveness of our proposed model is validated using a meticulously labeled dataset introduced within the context of this study.
  arXiv  Detail & Related papers  (2023-09-28T19:39:13Z)
• StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data [129.92449761766025]
  We propose a novel data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning. This approach harnesses the power of generative models, marrying the abilities of ChatGPT and text-to-image generative models. Our research includes comprehensive experiments conducted on various datasets.
  arXiv  Detail & Related papers  (2023-08-20T12:43:52Z)
• Learning in latent spaces improves the predictive accuracy of deep neural operators [0.0]
  L-DeepONet is an extension of standard DeepONet, which leverages latent representations of high-dimensional PDE input and output functions identified with suitable autoencoders. We show that L-DeepONet outperforms the standard approach in terms of both accuracy and computational efficiency across diverse time-dependent PDEs.
  arXiv  Detail & Related papers  (2023-04-15T17:13:09Z)
• Deep networks for system identification: a Survey [56.34005280792013]
  System identification learns mathematical descriptions of dynamic systems from input-output data. Main aim of the identified model is to predict new data from previous observations. We discuss architectures commonly adopted in the literature, like feedforward, convolutional, and recurrent networks.
  arXiv  Detail & Related papers  (2023-01-30T12:38:31Z)
• Solving High-Dimensional PDEs with Latent Spectral Models [74.1011309005488]
  We present Latent Spectral Models (LSM) toward an efficient and precise solver for high-dimensional PDEs. Inspired by classical spectral methods in numerical analysis, we design a neural spectral block to solve PDEs in the latent space. LSM achieves consistent state-of-the-art and yields a relative gain of 11.5% averaged on seven benchmarks.
  arXiv  Detail & Related papers  (2023-01-30T04:58:40Z)
• Deep Variational Models for Collaborative Filtering-based Recommender Systems [63.995130144110156]
  Deep learning provides accurate collaborative filtering models to improve recommender system results. Our proposed models apply the variational concept to injectity in the latent space of the deep architecture. Results show the superiority of the proposed approach in scenarios where the variational enrichment exceeds the injected noise effect.
  arXiv  Detail & Related papers  (2021-07-27T08:59:39Z)
• Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference [55.35176938713946]
  We develop deep autoencoding topic model (DATM) that uses a hierarchy of gamma distributions to construct its multi-stochastic-layer generative network. We propose a Weibull upward-downward variational encoder that deterministically propagates information upward via a deep neural network, followed by a downward generative model. The efficacy and scalability of our models are demonstrated on both unsupervised and supervised learning tasks on big corpora.
  arXiv  Detail & Related papers  (2020-06-15T22:22:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.