Generative Robust Optimisation defines uncertainty sets via neural network decoders over latent spaces and evaluates them with a five-point framework, validated on planning problems using Wasserstein autoencoders.
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Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
15 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We study a variant of the variational autoencoder model (VAE) with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep generative models. We observe that the known problem of over-regularisation that has been shown to arise in regular VAEs also manifests itself in our model and leads to cluster degeneracy. We show that a heuristic called minimum information constraint that has been shown to mitigate this effect in VAEs can also be applied to improve unsupervised clustering performance with our model. Furthermore we analyse the effect of this heuristic and provide an intuition of the various processes with the help of visualizations. Finally, we demonstrate the performance of our model on synthetic data, MNIST and SVHN, showing that the obtained clusters are distinct, interpretable and result in achieving competitive performance on unsupervised clustering to the state-of-the-art results.
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Behavioral INR adapts INRs to behavior by mapping states to actions with FiLM-modulated episode latents for self-supervised policy inference in unlabeled data, with new policy OOD definitions.
A constructive framework for topology-matched VAEs on product covering space manifolds with factorized priors and closed-form KL divergences.
CLUBench benchmark shows conventional clustering algorithms perform comparably to deep methods, pretrained embeddings boost image/text results, and low-rank performance matrices can approximate full evaluations.
For any fixed nonconstant teacher T, the best constant student has alignment cost exactly equal to the teacher mutual information I_T(X;T); a latent-only witness below this threshold with margin cannot be constant.
A manifold-anchored EM algorithm selects each prototype as the highest-diffusion-centrality medoid on a heat-kernel-weighted latent graph, plus a Dirichlet regularizer, yielding sharper and more stable prototypes than Euclidean GMM baselines on cardiac and brain MRI.
A meta-RL framework learns task-level knowledge on dynamics-simplified agents, organizes modes with Bayesian non-parametrics, and transfers via interfaces to heterogeneous agents, reporting 94.75-99.79% tracking error reduction with 23.8% of baseline data on locomotion tasks.
PDGMM-VAE recovers latent sources in nonlinear ICA by using jointly learned per-dimension GMM priors that fit source-specific marginals and reduce permutation symmetry.
GCVAE is a variational autoencoder that structures its latent space as a Gaussian mixture and optimizes a variational objective to make the representation maximally informative about a user-chosen guiding variable, enabling context-specific clusters.
A copula-integrated spatially regularized VAE is introduced to characterize geospatial comorbidity patterns of three childhood illnesses in West Africa from DHS data.
C-t³VAE introduces class-conditional Student's t priors and a gamma-power divergence objective to improve class-balanced generation in VAEs under severe imbalance.
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CellRefine adds a marker-gene-guided post-pretraining stage to single-cell models that refines the cell embedding manifold and improves downstream task performance by up to 15%.
X-VAE uses empirical statistics from a pretrained autoencoder to set a data-adaptive Gaussian prior and introduces a latent scaling factor for controllable generation.
GMAE learns disentangled view-specific and view-common embeddings via dual-path autoencoders and cross-view adversarial training to boost performance on complete and incomplete multi-view clustering tasks.
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Behavioral INR adapts INRs to behavior by mapping states to actions with FiLM-modulated episode latents for self-supervised policy inference in unlabeled data, with new policy OOD definitions.
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A Testable Certificate for Constant Collapse in Teacher-Guided VAEs
For any fixed nonconstant teacher T, the best constant student has alignment cost exactly equal to the teacher mutual information I_T(X;T); a latent-only witness below this threshold with margin cannot be constant.
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Deep Image Prototype Learning with Geometric Heat-Kernel Priors
A manifold-anchored EM algorithm selects each prototype as the highest-diffusion-centrality medoid on a heat-kernel-weighted latent graph, plus a Dirichlet regularizer, yielding sharper and more stable prototypes than Euclidean GMM baselines on cardiac and brain MRI.
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Knowledge Reutilization in Meta-Reinforcement Learning
A meta-RL framework learns task-level knowledge on dynamics-simplified agents, organizes modes with Bayesian non-parametrics, and transfers via interfaces to heterogeneous agents, reporting 94.75-99.79% tracking error reduction with 23.8% of baseline data on locomotion tasks.
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PDGMM-VAE: A Variational Autoencoder with Adaptive Per-Dimension Gaussian Mixture Model Priors for Nonlinear ICA
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From Unsupervised to Guided Clustering: A Variational Implementation
GCVAE is a variational autoencoder that structures its latent space as a Gaussian mixture and optimizes a variational objective to make the representation maximally informative about a user-chosen guiding variable, enabling context-specific clusters.
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Copula and spatial-regularized variational autoencoder for mapping disease comorbidity in West Africa
A copula-integrated spatially regularized VAE is introduced to characterize geospatial comorbidity patterns of three childhood illnesses in West Africa from DHS data.
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Heavy-Tailed Class-Conditional Priors for Long-Tailed Generative Modeling
C-t³VAE introduces class-conditional Student's t priors and a gamma-power divergence objective to improve class-balanced generation in VAEs under severe imbalance.
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Risk-Aware Aerocapture Guidance Through a Probabilistic Indicator Function
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Prototype Guided Post-pretraining for Single-Cell Representation Learning
CellRefine adds a marker-gene-guided post-pretraining stage to single-cell models that refines the cell embedding manifold and improves downstream task performance by up to 15%.
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eXact-Prior Variational Autoencoder (X-VAE): Learning Data-Adaptive Gaussian Mixture Priors for Latent Distributions
X-VAE uses empirical statistics from a pretrained autoencoder to set a data-adaptive Gaussian prior and introduces a latent scaling factor for controllable generation.
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Learning Disentangled Representations for Generalized Multi-view Clustering
GMAE learns disentangled view-specific and view-common embeddings via dual-path autoencoders and cross-view adversarial training to boost performance on complete and incomplete multi-view clustering tasks.