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Found 11 papers, 1 papers with code
Analysis and Utilization of Entrainment on Acoustic and Emotion Features in User-agent Dialogue
Entrainment is the phenomenon by which an interlocutor adapts their speaking style to align with their partner in conversations.
Low-rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density Estimation
Learning generative probabilistic models is a core problem in machine learning, which presents significant challenges due to the curse of dimensionality.
Multi-version Tensor Completion for Time-delayed Spatio-temporal Data
Recovering such missing or noisy (under-reported) elements of the input tensor can be viewed as a generalized tensor completion problem.
STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization
Accurate prediction of the transmission of epidemic diseases such as COVID-19 is crucial for implementing effective mitigation measures.
Information-theoretic Feature Selection via Tensor Decomposition and Submodularity
By indirectly aiming to predict the latent variable of the naive Bayes model instead of the original target variable, it is possible to formulate the feature selection problem as maximization of a monotone submodular function subject to a cardinality constraint - which can be tackled using a greedy algorithm that comes with performance guarantees.
Low-rank Characteristic Tensor Density Estimation Part I: Foundations
Any multivariate density can be represented by its characteristic function, via the Fourier transform.
Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms
The data deluge comes with high demands for data labeling.
Nonlinear System Identification via Tensor Completion
Deep neural networks are currently the most popular method for learning to mimic the input-output relationship of a general nonlinear system, as they have proven to be very effective in approximating complex highly nonlinear functions.
Learning Mixtures of Smooth Product Distributions: Identifiability and Algorithm
We study the problem of learning a mixture model of non-parametric product distributions.
Tensors, Learning, and 'Kolmogorov Extension' for Finite-alphabet Random Vectors
This paper shows, perhaps surprisingly, that if the joint PMF of any three variables can be estimated, then the joint PMF of all the variables can be provably recovered under relatively mild conditions.
Completing a joint PMF from projections: a low-rank coupled tensor factorization approach
There has recently been considerable interest in completing a low-rank matrix or tensor given only a small fraction (or few linear combinations) of its entries.
