New method learns CTBN structures from incomplete data.
problem Learning CTBN structures from incomplete data.
method Cluster-variational approximations.
result Significantly improves scalability over existing methods.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,341 papers · 148 categories
Trend · papers per month
16 results for “Cluster-variational”
MFCVAE clusters data over multiple facets, improving disentanglement and generation.
problem Clustering high-dimensional data like images over multiple characteristics.
method Variational autoencoder with hierarchical latent variables and Mixture-of-Gaussians priors.
result MFCVAE learns and clusters over multiple aspects of data in a disentangled manner.
Provable Emergence of Deep Neural Collapse and Low-Rank Bias in -Regularized Nonlinear Networkscs.LG
The paper connects neural collapse and low-rank bias in networks with L2 regularization.
problem Understanding the emergence of low-rank bias and neural collapse in L2-regularized networks.
method Unified theoretical framework linking TCV and rank of weight matrices, proving global optimality of DNC1, and establishing a benign landscape property.
result Zero TCV across intermediate layers minimizes representation cost under natural architectural constraints, and DNC1 is globally optimal.
New algorithm clusters hyperspectral images at multiple scales.
problem Clustering hyperspectral images at various scales.
method M-SRDL algorithm using spectral-spatial diffusion distances.
result More accurate clustering labels achieved with spatial regularization.
Improved GPLVM model for single-cell RNA-seq data.
problem Lack of effective scalable models for clustering cell types in large-scale single-cell RNA-seq data.
method Introduces amortized stochastic variational Bayesian GPLVM (BGPLVM) tailored for single-cell RNA-seq.
result Matches the performance of scVI on synthetic and real-world datasets and reveals more interpretable latent structures.
New method for Bayesian SBM inference is scalable, accurate, and concise.
problem Bayesian inference in the stochastic block model for graph clustering.
method Combining variational approximation and Laplace's method, a tractable algorithm is derived.
result The method solves cluster assignment and model selection tasks concurrently.
Loopy belief propagation (LBP), which is equivalent to the Bethe approximation in statistical mechanics, is a message-passing-type inference method that is widely used to analyze systems based on Markov random fields (MRFs). In this paper, we propose a message-passing-type method to analytically evaluate the quenched a…
DKULENOVO team improves speech diarization by 27.5% and 31.7% in DIHARD II.
problem Challenges in speech diarization, especially in distinguishing overlapping speakers.
method Used a combination of VAD, speaker embedding extraction, similarity scoring, clustering, and resegmentation techniques.
result Achieved 18.84% DER in Track1 and 27.90% DER in Track2, reducing baseline DERs by 27.5% and 31.7% respectively.
Improves unsupervised clustering using deep generative models with a Gaussian mixture prior.
problem Cluster degeneracy in variational autoencoders (VAEs).
method Applying a heuristic called minimum information constraint to mitigate over-regularization in a VAE with a Gaussian mixture prior.
result Demonstrates improved performance in unsupervised clustering on synthetic and real datasets.
S3VDC improves DC methods for scalability, stability, and simplicity.
problem Poor scalability, instability, and lack of simplicity in DC methods.
method Four algorithmic improvements: initial -training, periodic -annealing, mini-batch GMM initialization, and inverse min-max transform. S3VDC incorporates all improvements.
result S3VDC outperforms state-of-the-art methods on benchmark and industrial datasets.
Proposes a variational framework for fair clustering.
problem Ensuring fairness in clustering algorithms.
method Integrates fairness term with clustering objectives, using variational approach.
result Derives tight upper bound for optimization, enabling scalable solution.
In a variety of research areas, the weighted bag of vectors and the histogram are widely used descriptors for complex objects. Both can be expressed as discrete distributions. D2-clustering pursues the minimum total within-cluster variation for a set of discrete distributions subject to the Kantorovich-Wasserstein metr…
Proposes VCLANC for attributed network clustering using node and attribute embeddings.
problem Lack of mutual affinity exploitation between nodes and attributes in graph convolution.
method Dual variational auto-encoders for node and attribute embeddings, Gaussian mixture model priors, mutual distance and clustering assignment hardening losses.
result Demonstrates effectiveness on real-world attributed network datasets.
Improved co-clustering for robust data analysis.
problem Noise and posterior collapse in co-clustering.
method Fully variational Co-clustering framework with Compositional ELBO.
result Enhanced robustness and accuracy in co-clustering.
Can clustering scale sublinearly with its clusters? A variational EM acceleration of GMMs and -meansstat.ML
Study shows -means and GMM can scale sublinearly with clusters using variational EM.
problem Improving clustering efficiency with sublinear complexity.
method Applying variational EM to reduce complexity of -means and GMM iterations.
result Reduces computational complexity from linear to sublinear with respect to the number of clusters.
New insights into spurious local minima in k-means clustering.
problem Understanding and mitigating spurious local minima in k-means clustering.
method Investigating spurious local minima under a probabilistic generative model.
result Proven structures of spurious local minima for k-means clustering.