Paper shows how SFA fits into FBM framework for time series separation.
problem Identifying time series decomposition in flow-based models.
method Combining SFA and FBM to make time series decomposition identifiable.
result Time series decomposition becomes identifiable using SFA and FBM.
New method separates latent variables from noise in generative processes.
problem Reconstructing true latent variables from complex generative processes.
method General Incompressible-flow Network (GIN) for nonlinear ICA.
result Informative latent variables are automatically separated from noise.
Improves latent variable learning for complex data.
problem Expressive latent variables for model prediction on multi-component data.
method Dynamic Latent Separation method that distances data samples in the latent space.
result Enhances output diversity and provides interpretable representations.
The paper uses deep neural networks to estimate economic models without separability restrictions.
problem Estimating economic models with complex interaction effects and non-separable restrictions.
method Uses deep neural networks as a nonparametric sieve to approximate regression functions from nonlinear latent variable models.
result Economic shape, sparsity, or separability restrictions are imposed more straightforwardly when a flexible latent variable model is used.
Develops a Bayesian non-parametric approach for signal separation with varying components.
problem Signal separation with varying components across different input locations.
method Augments Gaussian Process Latent Variable Models with weighted sums of pure component signals and incorporates priors for linear weights.
result Framework allows for non-linear variations in signals and incorporates useful priors for linear weights.
Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with small amount of data. We develop a latent variable model that can cope with imbalanced data by dividing the latent space into a shared space…
LMMVAE improves VAE for correlated data by separating latent variables into fixed and random parts.
problem Correlated data in tabular and image datasets.
method Integrates random effects into VAE architecture, separating latent variables into fixed and random parts.
result Significant improvement in reconstruction error and likelihood loss on unseen data.
Paper distinguishes causal structures under latent confounding and selection bias.
problem Distinguishing causal relationships when latent variables and selection bias are present.
method Formulated selected-marginalized directed graphs (smDGs) to distinguish causal structures.
result Two causal structures are indistinguishable if they have the same selected-marginalized directed graph.
Concept modulation models unify identifiability and extrapolation in conditional latent variable models.
problem Reliable generalization in conditional latent variable models
method Concept modulation models (CMMs) with structure AoΛoCoX result Lifts identifiability to conditional settings and controls extrapolation through attribute potentials.
AR-Flow VAE improves blind source separation with flexible autoregressive priors.
problem Unsupervised blind source separation of latent signals from mixtures.
method AR-Flow VAE uses autoregressive flows to model latent sources, enhancing flexibility and capturing complex dependencies.
result AR-Flow VAE effectively separates latent sources, demonstrating improved performance over conventional methods.
New graph types help identify complex relationships.
problem Understanding complex relationships in data.
method Introducing separable and essentially separable graphs to characterize and identify graphical models.
result Developed algorithms to identify equivalence classes of essentially separable graphs.
We present the Mixed Likelihood Gaussian process latent variable model (GP-LVM), capable of modeling data with attributes of different types. The standard formulation of GP-LVM assumes that each observation is drawn from a Gaussian distribution, which makes the model unsuited for data with e.g. categorical or nominal a…
An essential problem in domain adaptation is to understand and make use of distribution changes across domains. For this purpose, we first propose a flexible Generative Domain Adaptation Network (G-DAN) with specific latent variables to capture changes in the generating process of features across domains. By explicitly…
Proposes a new condition to estimate latent variable causal graphs from observed data.
problem Estimating causal structures when observed variables are not the underlying causal variables.
method Introduces Generalized Independent Noise (GIN) condition and a recursive learning algorithm.
result Shows that GIN helps locate latent variables and identify their causal structure.
Proposes a VAE variant for ordinal content factors.
problem Isolating ordinal-valued content factors in deep latent variable models.
method Introduces a partially ordered set (poset) structure and a conditional Gaussian spacing prior model.
result Significant improvements in content-style separation over previous non-ordinal approaches.
The Trek Separation Theorem (Sullivant et al. 2010) states necessary and sufficient conditions for a linear directed acyclic graphical model to entail for all possible values of its linear coefficients that the rank of various sub-matrices of the covariance matrix is less than or equal to n, for any given n. In this pa…
Current methods for learning graphical models with latent variables and a fixed structure estimate optimal values for the model parameters. Whereas this approach usually produces overfitting and suboptimal generalization performance, carrying out the Bayesian program of computing the full posterior distributions over t…
We study the problem of discovering the simplest latent variable that can make two observed discrete variables conditionally independent. The minimum entropy required for such a latent is known as common entropy in information theory. We extend this notion to Renyi common entropy by minimizing the Renyi entropy of the …
Improved Monte-Carlo models by constraining mutual information between latent and observable variables.
problem Training density models leads to latent variables being useless.
method Weave tighter Monte-Carlo bounds with mutual information constraints.
result Improved training of models with continuous and discrete latent variables.
This paper improves level generation using VAEs for coherent, logically following segments.
problem Generating coherent levels of non-fixed length and blending levels from different games.
method Sequential segment-based level generation using VAEs with a classifier for logical placement.
result Generated levels are more coherent and capable of blending levels from different games.
D-GCCA improves multi-view data analysis by separating common and distinctive components.
problem Analyzing multi-view high-dimensional data with latent factors.
method Decomposes each view's data matrix into common and distinctive sources with orthogonality constraints.
result Consistent estimators with good performance and efficient computation.
LaCIM avoids spurious correlation by modeling latent causal factors.
problem Avoiding spurious correlation in supervised learning.
method Introducing latent variables for causal prediction and optimizing over latent space.
result Improved interpretability, robustness, and prediction power on OOD scenarios.
Researchers study how teachers' advising relationships influence their perceptions of satisfaction and students, not policy influence.
problem Understanding the relationship between teachers' advising relationships and their perceptions of satisfaction and students.
method Proposed a novel joint model of network and item responses (JNIRM) with correlated latent variables.
result Teachers' advising relationships contribute more to satisfaction and students than to influence over educational policies.
Learning representations that disentangle the underlying factors of variability in data is an intuitive way to achieve generalization in deep models. In this work, we address the scenario where generative factors present a multimodal distribution due to the existence of class distinction in the data. We propose N-VAE, …
This paper presents an unsupervised method that trains neural source separation by using only multichannel mixture signals. Conventional neural separation methods require a lot of supervised data to achieve excellent performance. Although multichannel methods based on spatial information can work without such training …
Generalizes underlap coefficient for multivariate group separation.
problem Quantifying distributional separation across groups in statistical learning.
method Generalizes underlap coefficient (UNL) to multivariate variables, establishes key properties, interprets as dependence measure, proposes efficient estimator.
result Highlights the UNL's utility in clustering for evaluating group structure dependence on covariates.
New method uses cycle consistency to enforce invariance in latent space.
problem Learning meaningful and independent factors of variation in datasets.
method Two separate latent subspaces, cycle consistency constraints, deep information bottleneck.
result Identifies more meaningful factors leading to sparser and interpretable models.
Double InfoGAN improves CA by generating clearer, more accurate images.
problem Improper latent variable models in CA lead to sub-optimal solutions.
method Double InfoGAN, a GAN-based method enforcing fundamental assumptions and improving image quality.
result Double InfoGAN outperforms SOTA CA-VAEs in latent separation and image quality.
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.
Detects causal scenarios with inequality constraints among classical correlations.
problem Classifying causal structures and identifying those with inequality constraints.
method Using d-separation, e-separation, incompatible supports, and HLP condition.
result Resolved all but three causal scenarios with up to 4 observed variables.
This paper proposes a multichannel source separation technique called the multichannel variational autoencoder (MVAE) method, which uses a conditional VAE (CVAE) to model and estimate the power spectrograms of the sources in a mixture. By training the CVAE using the spectrograms of training examples with source-class l…
RFN models urban mobility demand by separating temporal and spatial variability.
problem Aligning supply and demand in MoD systems for efficient transportation.
method Recurrent flow networks with latent variables and normalizing flows.
result RFN models explicitly disentangle temporal and spatial variability in urban mobility.
A-VI can approximate F-VI under certain conditions, improving inference in some models.
problem Approximate Bayesian inference using factorized distributions.
method Amortized variational inference (A-VI) learns a common inference function for latent variables.
result A-VI can close the amortization gap in simple hierarchical models.
BGM-IV uses AI to estimate causal effects in complex data.
problem Estimating causal effects in high-dimensional, nonlinear settings with endogeneity.
method Structured latent generative modeling for posterior inference in a causally structured latent space.
result BGM-IV outperforms existing methods in high-dimensional covariate regimes.
We propose a novel neural sequence prediction method based on \textit{error-correcting output codes} that avoids exact softmax normalization and allows for a tradeoff between speed and performance. Instead of minimizing measures between the predicted probability distribution and true distribution, we use error-correcti…
We present a syntax-infused variational autoencoder (SIVAE), that integrates sentences with their syntactic trees to improve the grammar of generated sentences. Distinct from existing VAE-based text generative models, SIVAE contains two separate latent spaces, for sentences and syntactic trees. The evidence lower bound…
A new IC-Connection improves disentanglement in conditional GANs.
problem Poor disentanglement of latent variables in conditional GANs.
method Information Compensation Connection (IC-Connection) for disentanglement.
result Our method achieves better disentanglement than state-of-the-art GANs.
Improved neural population modeling using shared features and ensemble detection.
problem Missing shared coding properties in neural latent variable models.
method Feature sharing across tuning curves and soft clustering of neurons.
result More interpretable and better-performing neural population models.
A network supporting deep unsupervised learning is presented. The network is an autoencoder with lateral shortcut connections from the encoder to decoder at each level of the hierarchy. The lateral shortcut connections allow the higher levels of the hierarchy to focus on abstract invariant features. While standard auto…
A new method for separating mixed signals in space and time.
problem Nonlinear and nonstationary spatio-temporal data challenges.
method Identifiable autoregressive variational autoencoder.
result The method outperforms existing techniques in blind source separation and spatio-temporal prediction.
We propose a novel parameterized family of Mixed Membership Mallows Models (M4) to account for variability in pairwise comparisons generated by a heterogeneous population of noisy and inconsistent users. M4 models individual preferences as a user-specific probabilistic mixture of shared latent Mallows components. Our k…
Hybrid model improves traffic flow prediction accuracy.
problem Predicting traffic flow with high accuracy in short-term future.
method A hybrid model combining hidden Markov model and LSTM.
result Significant performance gains over conventional methods.
Change detection involves segmenting sequential data such that observations in the same segment share some desired properties. Multivariate change detection continues to be a challenging problem due to the variety of ways change points can be correlated across channels and the potentially poor signal-to-noise ratio on …
Neural Decomposition breaks down VAE latent structure for better interpretability.
problem Limited interpretability of VAE latent representations.
method Adapted functional ANOVA to VAEs, applying constraints for identifiability.
result Decomposes data variation into latent and fixed input effects.
Generalizes underlap coefficient for multivariate group separation.
problem Quantifying distributional separation across groups in statistical learning.
method Generalizes underlap coefficient (UNL) to multivariate settings, studies its relationship with Bayes risk and mutual information, proposes an efficient importance sampling estimator.
result UNL as a measure of dependence between group labels and variables of interest, interpretable measure of partition-covariate dependence in clustering.
Spectral learning extends matrix methods to tensors for better latent variable modeling.
problem Limitations of matrix-based spectral methods in capturing non-Gaussian data.
method Extend spectral decomposition to tensor-based methods for higher-order moments.
result Tensor decomposition can identify latent effects missed by matrix methods.
Model-based clustering defines population level clusters relative to a model that embeds notions of similarity. Algorithms tailored to such models yield estimated clusters with a clear statistical interpretation. We take this view here and introduce the class of G-block covariance models as a background model for varia…
A crucial challenge in image-based modeling of biomedical data is to identify trends and features that separate normality and pathology. In many cases, the morphology of the imaged object exhibits continuous change as it deviates from normality, and thus a generative model can be trained to model this morphological con…