Paper improves training of generative models with discrete latent variables using Wasserstein distance.
problem Training subtleties in models with both discrete and continuous latent variables.
method Use of Optimal Transport framework (Wasserstein Autoencoders) to train models with fully leveraged discrete latent variables.
result Discrete latent variable is fully leveraged without modifications to the objective function or fine tuning.
A new method reduces variance in training discrete latent variable models.
problem High variance in stochastic gradient estimators for discrete latent variable models.
method Double control variates for score function estimators using Taylor expansions.
result Our method can have lower variance compared to other estimators.
GFlowNet-EM learns complex latent variable models with discrete structures.
problem Challenges in modeling posteriors over discrete compositional latents with expectation-maximization.
method Uses GFlowNets to learn stochastic policies for sampling from complex posterior distributions.
result GFlowNet-EM enables training expressive LVMs with discrete compositional latents.
Improved hierarchical discrete VAEs for better stability and performance.
problem Training stable and efficient hierarchical discrete VAEs with numerous latent variables.
method Introducing Relaxed-Responsibility Vector-Quantisation to parameterise discrete latent variables in a hierarchical structure.
result Achieved state-of-the-art bits-per-dim results for various standard datasets.
New method for efficient marginalization of discrete latent variables in neural networks.
problem Computational challenges in training models with discrete latent variables.
method Parameterizing discrete distributions using sparse mappings (sparsemax and structured variants) to reduce support and enable efficient marginalization.
result Achieved good performance in various tasks with efficient and practical training.
Discrete-AIR model identifies objects in images with interpretable latent codes.
problem Identifying objects in images without labeled data.
method Recurrent Auto-Encoder with structured latent distributions for discrete, continuous, and spatial attention.
result Discrete-AIR model uses minimal latent variables for efficient inference.
Proposes a non-parametric method for deep discrete latent variable models.
problem Learning sparse discrete latent representations in deep models.
method Iterative algorithm with Beta-Bernoulli process prior and local data scaling.
result Improves sparsity and scalability of deep discrete latent variable models.
Probabilistic models with discrete latent variables naturally capture datasets composed of discrete classes. However, they are difficult to train efficiently, since backpropagation through discrete variables is generally not possible. We present a novel method to train a class of probabilistic models with discrete late…
New estimators reduce variance in training variational autoencoders with discrete latent variables.
problem Training variational autoencoders with discrete latent variables requires efficient gradient estimation.
method Introduce ReinMax-Rao and ReinMax-CV estimators using Rao-Blackwellisation and control variates.
result Demonstrate superior performance on training variational autoencoders with discrete latent spaces.
Direct optimization of discrete variational auto-encoders using arg max.
problem Optimizing discrete latent variables in variational auto-encoders.
method Direct optimization through arg max without softmax relaxations.
result Empirical effectiveness of direct loss minimization in discrete latent variables.
LADD models improve discrete diffusion for faster language generation.
problem Practical discrete diffusion models ignore cross-token dependencies, degrading performance.
method Introduces a learnable auxiliary latent channel, diffusing over the joint (token, latent) space.
result LADD models yield improvements on unconditional generation metrics.
Training of discrete latent variable models remains challenging because passing gradient information through discrete units is difficult. We propose a new class of smoothing transformations based on a mixture of two overlapping distributions, and show that the proposed transformation can be used for training binary lat…
New JSA autoencoders tackle discrete latent variable models for semi-supervised learning.
problem Handling discrete observations and latent codes in deep generative models.
method Joint-stochastic-approximation (JSA) autoencoders that directly maximize data log-likelihood and minimize KL divergence.
result JSA autoencoders achieve comparable performance to continuous latent space models in semi-supervised tasks.
RAD approach models both continuous and discrete data.
problem Flow models struggle with discrete structures in data.
method Domain partitioning with locally invertible functions for real and discrete latent variables.
result RAD approach models both continuous and discrete structures.
SPIGOT bypasses gradients of argmax functions in neural nets with discrete latent variables.
problem Training neural networks with discrete latent variables.
method Structured projected intermediate gradient optimization technique (SPIGOT).
result SPIGOT bypasses gradients of argmax functions effectively.
DCRL learns causal relationships from mixed-type discrete data.
problem Challenges in learning causal relationships from discrete, mixed-type data.
method Generative framework modeling directed acyclic graph and sparse bipartite graph, flexible measurement models for different types of data.
result Consistent recovery of latent causal structure from observed data distribution.
We present a semi-supervised learning algorithm for learning discrete factor analysis models with arbitrary structure on the latent variables. Our algorithm assumes that every latent variable has an "anchor", an observed variable with only that latent variable as its parent. Given such anchors, we show that it is possi…
A new gradient estimator reduces variance near boundaries for binary latent variables.
problem Explosive gradient variance near boundaries in binary latent variable models.
method Introduces a new gradient estimator (bitflip-1) and an aggregated estimator (UGC) that uses either bitflip-1 or DisARM for each coordinate.
result UGC has uniformly lower variance than DisARM and achieves optimal optimization objectives.
New gradient estimators for discrete variables improve model training.
problem Training models with discrete latent variables is challenging due to high gradient variance.
method Introduced novel gradient estimators based on importance sampling and statistical couplings, extending to categorical variables.
result Proposed gradient estimators outperform previous methods in systematic experiments.
Improved VQ-VAE training technique enhances image generation and machine translation.
problem Training discrete latent variable models remains challenging and underperforms continuous counterparts.
method Alternate training technique inspired by Expectation Maximization (EM) algorithm.
result Improved image generation results on CIFAR-10 and non-autoregressive machine translation model.
Unsupervised framework captures acquisition variability in structural connectomes.
problem Acquisition differences across sites, scanners, and protocols complicate structural connectome analysis.
method An unsupervised framework using architectural annealing to balance discrete and continuous latent variables.
result Architectural annealing produces stronger site learning than baseline models.
Proposes a new model for unsupervised clustering with latent variables.
problem The challenge of unsupervised clustering in machine learning.
method Clustered Generator Model with continuous and discrete latent variables.
result Achieves competitive unsupervised clustering accuracy and disentangled latent representations.
A new test for conditional independence in discretized data.
problem Testing conditional independence when only discretized observations are available.
method Proposes a conditional independence test designed for discretized observations, using bridge equations to recover latent variables' information.
result Demonstrates the effectiveness of the proposed test through theoretical and empirical validation.
Improved training for VQ-VAE models with robust codebook learning.
problem Challenges in training discrete latent variable models, especially VQ-VAEs.
method Increased learning rate and periodic re-initialization of codebook for robust training.
result More robust training and increased usage of latent codewords, even for large codebooks.
Bayesian optimization tackles mixed discrete-continuous problems with Gaussian processes.
problem Optimizing problems with both discrete and continuous variables using costly simulations.
method Relaxing discrete variables into continuous latent variables, using Bayesian optimization, and incorporating compatibility constraints with Lagrangians.
result Comparative analysis of different mixed Bayesian optimization approaches.
Improved clustering speed for 20 clusters on CIFAR-100 dataset.
problem Training time complexity for VAEs with discrete latent variables is linear in the number of clusters.
method Applied a continuous relaxation to discrete variables in Gaussian Mixture VAE, reducing training time complexity to constant.
result Reduced training time from 47 hours to 6 hours for 20 clusters on CIFAR-100.
Bayesian active learning reduces data needed for latent variable models.
problem Active learning for latent variable models, especially mixtures of linear regressions and HMMs with GLM observations.
method Maximum-mutual-information input selection for discrete latent variable regression models.
result Active learning can achieve large gains for mixtures of linear-Gaussian models and substantially reduces data needed for GLM-HMM.
Graphical models are commonly used tools for modeling multivariate random variables. While there exist many convenient multivariate distributions such as Gaussian distribution for continuous data, mixed data with the presence of discrete variables or a combination of both continuous and discrete variables poses new cha…
A new method learns latent variable updates directly, not approximating the posterior.
problem Intractable maximum-likelihood learning for complex latent-variable models.
method Amortised learning using wake-sleep Monte-Carlo strategy.
result Demonstrated effectiveness on various complex models.
Proposes a VAE with a discrete bottleneck for better text generation.
problem VAEs struggle with latent variable auto-regressive decoding in text generation.
method Introduces a discretized bottleneck to enforce latent feature matching in a compact space.
result Demonstrates improved text generation capabilities across various tasks.
Bayesian network models with latent variables are widely used in statistics and machine learning. In this paper we provide a complete algebraic characterization of Bayesian network models with latent variables when the observed variables are discrete and no assumption is made about the state-space of the latent variabl…
A new CI test avoids information loss in discretized data.
problem Incorrect CI conclusions from discretized data.
method Proposes a sample-efficient CI test using GMM and nodewise regression.
result Derives an accurate test statistic and establishes its asymptotic distribution.
New method uses joint stochastic approximation to improve learning of discrete latent models.
problem Challenges in learning discrete latent variable models, especially with inference model gradients and log-likelihood optimization.
method Proposes a new method based on stochastic approximation theory that directly maximizes the target log-likelihood and minimizes the posterior-inference model divergence.
result Consistently outperforms recent competitive algorithms in generative modeling and structured prediction tasks.
Framework estimates multiple plausible solutions with uncertainty measures.
problem Machine learning models need to propose multiple plausible solutions with meaningful uncertainty.
method Discrete latent variables model one-to-many mappings, allowing effective conditional probability estimation.
result Framework outperforms state-of-the-art in uncertainty estimation and is practical.
Efficiently models discrete dynamical systems with autoregressive distributions.
problem Inadequate approximate posteriors for discrete latent variables in variational inference.
method Developed a fast, parallel sampling procedure for autoregressive distributions.
result Accurate uncertainty and connectivity estimates in discrete state-space latent variable dynamical systems.
Formalizes concepts as latent variables in hierarchical models for high-dimensional data.
problem Lack of formalization and theoretical insights for learning discrete concepts from high-dimensional data.
method Formalizes concepts as latent causal variables in a hierarchical model, formulates conditions for concept identification.
result Conditions for identifying latent hierarchical models in unsupervised data, handling complex structures and high-dimensional data.
Developing a dialogue agent that is capable of making autonomous decisions and communicating by natural language is one of the long-term goals of machine learning research. Traditional approaches either rely on hand-crafting a small state-action set for applying reinforcement learning that is not scalable or constructi…
Proposes a framework to improve VAE latent codes using mutual information.
problem Lack of explicit measure for VAE latent variable quality.
method Variational Mutual Information Maximization Framework.
result Improves relationships between latent codes and observations.
A new method for generative modeling of discrete data using geometric latent subspaces.
problem Learning generative models for discrete data with statistical dependencies.
method Geometric latent-subspace framework in exponential parameter space of product manifolds of categorical distributions.
result Low-dimensional latent space encodes statistical dependencies and accurately models high-dimensional discrete data.
Neural model extracts tokens as latent variables for text compression.
problem Compressing text using neural models.
method Extracting tokens with highest tf-idf scores or highest loss from a bidirectional language model.
result Extracting tokens as latent variables significantly outperforms state-of-the-art methods.
New particle algorithms optimize latent variable models.
problem Optimizing latent variable models for maximum likelihood estimation.
method Identify gradient flows associated with free energy functional and discretize them to create particle-based algorithms.
result Novel particle algorithms scale to high-dimensional settings and perform well in experiments.
Paper relaxes identifiability conditions for causal models with latent variables.
problem Challenges in identifying causal graphical models with latent variables.
method Proposes a double triangular graphical condition for nonparametric measurement models with binary latent variables.
result Guarantees identifiability of the entire causal graphical model under relaxed conditions.
A new method for training generative models with sparse supervision.
problem Training deep generative models with sparse and varying supervision.
method Caffeinated Wake-Sleep (CWS) method, combining reweighted wake-sleep and teacher-forcing.
result The CWS method is robust to variable length supervision and performs well on various datasets.
A new method for categorical variational inference using discrete normalizing flows.
problem Challenges in optimizing variational approximations for discrete latent variables.
method Differentiable reparameterization using a mixture of discrete normalizing flows.
result Improves optimization of evidence lower bound and reduces sensitivity to hyperparameters.
Adaptive probabilistic PCA adapts complexity with varying subspaces.
problem Adaptive probabilistic PCA models varying complexity in data.
method Relaxed linear Gaussian model with discrete latent variables, Bayesian nonparametric approach.
result Proposes locally adaptive probabilistic PCA (A-PPCA) for varying subspaces.
NegBio-VAE models neural spike counts with negative binomial distribution.
problem Limited biological plausibility of continuous latent variables in VAEs for neural spike modeling.
method Proposes a negative binomial latent-variable model with a dispersion parameter for overdispersed spike count modeling.
result NegBio-VAE outperforms competing models in reconstruction and generation tasks.
Learning in models with discrete latent variables is challenging due to high variance gradient estimators. Generally, approaches have relied on control variates to reduce the variance of the REINFORCE estimator. Recent work (Jang et al. 2016, Maddison et al. 2016) has taken a different approach, introducing a continuou…
Unified framework for disentangling latent variables.
problem Unidentifiability of deep latent-variable models.
method Variational autoencoders and nonlinear ICA, with a factorized prior conditioned on an observed variable.
result Identification of true joint distribution over observed and latent variables is possible up to simple transformations.