• Referenced in 0 articles [sw16550]
• LFADS - Latent Factor Analysis via Dynamical Systems. Neuroscience is experiencing a data revolution in which ... analyzed. Here we introduce LFADS (Latent Factor Analysis via Dynamical Systems), a method to infer ... latent dynamics from simultaneously recorded, single-trial, high-dimensional neural spiking data. LFADS ... dimensional temporal factors, per-trial initial conditions, and inferred inputs. We compare LFADS to existing...
• # DrBats

• Referenced in 0 articles [sw18014]
• Feed longitudinal data into a Bayesian Latent Factor Model to obtain a low-rank representation ... Hilgert and S. Holmes, ”Bayesian Latent Factor Model for Functional Data Analysis”, Actes...
• # SupCP

• Referenced in 1 article [sw24588]
• factorization. We describe a probabilistic PARAFAC/CANDECOMP (CP) factorization for multiway (i.e., tensor) data that incorporates ... fields. We use a novel likelihood-based latent ... variable representation of the CP factorization, in which the latent variables are informed by additional...
• # IMIFA

• Referenced in 0 articles [sw20290]
• conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model ... number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific...
• # bioNMF

• Referenced in 6 articles [sw14438]
• been given to Non-negative matrix factorization technique (NMF), due to its capability of providing ... insights and relevant information about the complex latent relationships in experimental data sets. This method...
• # SymNMF

• Referenced in 10 articles [sw12668]
• matrix by a product of two nonnegative factors. NMF has been shown to produce clustering ... graph clustering method that captures latent linear and nonlinear relationships in the data...
• # Turbo-SMT

• Referenced in 3 articles [sw35620]
• Turbo-SMT: parallel coupled sparse matrix-tensor factorizations and applications. How can we correlate ... hand’)? In short, we want to find latent variables, that jointly explain both the brain ... many settings of the Coupled Matrix-Tensor Factorization (CMTF) problem. Can we enhance any CMTF ... Turbo-SMT is able to find meaningful latent variables, as well as to predict brain...
• # lsa

• Referenced in 4 articles [sw19469]
• that text do have a higher order (=latent semantic) structure which, however, is obscured ... truncated singular value decomposition (a two-mode factor analysis) over a given document-term matrix...
• # Venture

• Referenced in 8 articles [sw14670]
• supports external models that do inference over latent variables hidden from Venture. Second, we describe ... partitions of execution histories called scaffolds that factor global inference problems into coherent sub-problems...
• # AMIDST

• Referenced in 5 articles [sw21741]
• financial dataset) and using several models (LDA, factor analysis, mixture of Gaussians and linear regression ... than one billion nodes and approx. $75%$ latent variables using a computer cluster with...
• # Morpho-MNIST

• Referenced in 1 article [sw31838]
• assessment and diagnostics for representation learning. Revealing latent structure in data is an active field ... model learned to represent specific factors of variation in the data? We extend the popular ... trained models, identification of the roles of latent variables, and characterisation of sample diversity...
• # recosystem

• Referenced in 0 articles [sw15860]
• www.csie.ntu.edu.tw/ cjlin/libmf/) for recommender system using matrix factorization. It is typically used to approximate ... product of two matrices in a latent space. Other common names for this task include...
• # hopit

• Referenced in 1 article [sw30888]
• gender, age-specific, linguistic and other cultural factors (Jylha 2009 ; Oksuzyan ... cultural differences in how the continuous latent health is projected onto the ordinal self-rated...