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Found 9 papers, 1 papers with code
Approximation of RKHS Functionals by Neural Networks
Motivated by the abundance of functional data such as time series and images, there has been a growing interest in integrating such data into neural networks and learning maps from function spaces to R (i. e., functionals).
Benefits of Transformer: In-Context Learning in Linear Regression Tasks with Unstructured Data
In practice, it is observed that transformer-based models can learn concepts in context in the inference stage.
A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models
In this article, we review the literature on statistical theories of neural networks from three perspectives.
AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizing
Diffusion model has become a main paradigm for synthetic data generation in many subfields of modern machine learning, including computer vision, language model, or speech synthesis.
On Excess Risk Convergence Rates of Neural Network Classifiers
The recent success of neural networks in pattern recognition and classification problems suggests that neural networks possess qualities distinct from other more classical classifiers such as SVMs or boosting classifiers.
Generalization of Overparametrized Deep Neural Network Under Noisy Observations
We study the generalization properties of the overparameterized deep neural network (DNN) with Rectified Linear Unit (ReLU) activations.
Asymptotic Theory of $\ell_1$-Regularized PDE Identification from a Single Noisy Trajectory
We provide a set of sufficient conditions which guarantee that, from a single trajectory data denoised by a Local-Polynomial filter, the support of $\mathbf{c}(\lambda)$ asymptotically converges to the true signed-support associated with the underlying PDE for sufficiently many data and a certain range of $\lambda$.
Factor Analysis on Citation, Using a Combined Latent and Logistic Regression Model
We propose a combined model, which integrates the latent factor model and the logistic regression model, for the citation network.
Review on Parameter Estimation in HMRF
In following section, we expand this idea on estimating parameters in Gaussian Hidden Markov Spatial-Temporal Random Field, and display results on two performed experiments.
