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Found 29 papers, 8 papers with code
Statistical Performance of Convex Tensor Decomposition
We analyze the statistical performance of a recently proposed convex tensor decomposition algorithm.
Weighted Likelihood Policy Search with Model Selection
Reinforcement learning (RL) methods based on direct policy search (DPS) have been actively discussed to achieve an efficient approach to complicated Markov decision processes (MDPs).
Factorized Asymptotic Bayesian Inference for Latent Feature Models
This paper extends factorized asymptotic Bayesian (FAB) inference for latent feature models~(LFMs).
Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood
Our analysis provides a formal justification of FIC as a model selection criterion for LVMs and also a systematic procedure for pruning redundant latent variables that have been removed heuristically in previous studies.
Doubly Decomposing Nonparametric Tensor Regression
Nonparametric extension of tensor regression is proposed.
Bayesian Masking: Sparse Bayesian Estimation with Weaker Shrinkage Bias
A common strategy for sparse linear regression is to introduce regularization, which eliminates irrelevant features by letting the corresponding weights be zeros.
A Tractable Fully Bayesian Method for the Stochastic Block Model
The stochastic block model (SBM) is a generative model revealing macroscopic structures in graphs.
Making Tree Ensembles Interpretable
Tree ensembles, such as random forest and boosted trees, are renowned for their high prediction performance, whereas their interpretability is critically limited.
Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach
In this study, we present a method to make a complex tree ensemble interpretable by simplifying the model.
Minimizing Quadratic Functions in Constant Time
A sampling-based optimization method for quadratic functions is proposed.
On Tensor Train Rank Minimization: Statistical Efficiency and Scalable Algorithm
Tensor train (TT) decomposition provides a space-efficient representation for higher-order tensors.
Tensor Decomposition with Smoothness
Real data tensors are usually high dimensional but their intrinsic information is preserved in low-dimensional space, which motivates to use tensor decompositions such as Tucker decomposition.
Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words
Distributed word embeddings have shown superior performances in numerous Natural Language Processing (NLP) tasks.
On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm
Tensor train (TT) decomposition provides a space-efficient representation for higher-order tensors.
Fitting Low-Rank Tensors in Constant Time
Then, we show that the residual error of the Tucker decomposition of $\tilde{X}$ is sufficiently close to that of $X$ with high probability.
Why does PairDiff work? - A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection
We show that, if the word embed- dings are standardised and uncorrelated, such an operator will be independent of bilinear terms, and can be simplified to a linear form, where PairDiff is a special case.
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis
In this paper, we study random subsampling of Gaussian process regression, one of the simplest approximation baselines, from a theoretical perspective.
Data Interpolating Prediction: Alternative Interpretation of Mixup
Data augmentation by mixing samples, such as Mixup, has widely been used typically for classification tasks.
Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks
Tensor decomposition methods are widely used for model compression and fast inference in convolutional neural networks (CNNs).
Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks
Tensor decomposition methods are widely used for model compression and fast inference in convolutional neural networks (CNNs).
Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks
A graph neural network (GNN) is a good choice for predicting the chemical properties of molecules.
A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?
Synthetic-to-real transfer learning is a framework in which a synthetically generated dataset is used to pre-train a model to improve its performance on real vision tasks.
A Scaling Law for Syn-to-Real Transfer: How Much Is Your Pre-training Effective?
Synthetic-to-real transfer learning is a framework in which a synthetically generated dataset is used to pre-train a model to improve its performance on real vision tasks.
Fractional SDE-Net: Generation of Time Series Data with Long-term Memory
It generalizes the neural stochastic differential equation model by using fractional Brownian motion with a Hurst index larger than half, which exhibits the LRD property.
TabRet: Pre-training Transformer-based Tabular Models for Unseen Columns
We present \emph{TabRet}, a pre-trainable Transformer-based model for tabular data.
Neural Fourier Transform: A General Approach to Equivariant Representation Learning
Symmetry learning has proven to be an effective approach for extracting the hidden structure of data, with the concept of equivariance relation playing the central role.
Virtual Human Generative Model: Masked Modeling Approach for Learning Human Characteristics
In this paper, we propose Virtual Human Generative Model (VHGM), a machine learning model for estimating attributes about healthcare, lifestyles, and personalities.
CFTM: Continuous time fractional topic model
This approach incorporates fractional Brownian motion~(fBm) to effectively identify positive or negative correlations in topic and word distribution over time, revealing long-term dependency or roughness.
Extended Flow Matching: a Method of Conditional Generation with Generalized Continuity Equation
In this paper, we develop the theory of conditional generation based on Flow Matching, a current strong contender of diffusion methods.
