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Found 12 papers, 0 papers with code
MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification
The objective of search result diversification (SRD) is to ensure that selected documents cover as many different subtopics as possible.
Nonlinear functional regression by functional deep neural network with kernel embedding
With the rapid development of deep learning in various fields of science and technology, such as speech recognition, image classification, and natural language processing, recently it is also widely applied in the functional data analysis (FDA) with some empirical success.
Distributed Gradient Descent for Functional Learning
In recent years, different types of distributed learning schemes have received increasing attention for their strong advantages in handling large-scale data information.
Approximation of Nonlinear Functionals Using Deep ReLU Networks
In recent years, functional neural networks have been proposed and studied in order to approximate nonlinear continuous functionals defined on $L^p([-1, 1]^s)$ for integers $s\ge1$ and $1\le p<\infty$.
PILE: Pairwise Iterative Logits Ensemble for Multi-Teacher Labeled Distillation
In this paper, we focus on two key questions in knowledge distillation for ranking models: 1) how to ensemble knowledge from multi-teacher; 2) how to utilize the label information of data in the distillation process.
Optimal prediction for kernel-based semi-functional linear regression
In this paper, we establish minimax optimal rates of convergence for prediction in a semi-functional linear model that consists of a functional component and a less smooth nonparametric component.
A q-binomial extension of the CRR asset pricing model
We propose an extension of the Cox-Ross-Rubinstein (CRR) model based on $q$-binomial (or Kemp) random walks, with application to default with logistic failure rates.
Long Short-Term Memory Neuron Equalizer
In this work we propose a neuromorphic hardware based signal equalizer by based on the deep learning implementation.
Comparison theorems on large-margin learning
This paper studies binary classification problem associated with a family of loss functions called large-margin unified machines (LUM), which offers a natural bridge between distribution-based likelihood approaches and margin-based approaches.
Solving Poisson's Equation using Deep Learning in Particle Simulation of PN Junction
Simulating the dynamic characteristics of a PN junction at the microscopic level requires solving the Poisson's equation at every time step.
A Statistical Learning Approach to Modal Regression
However, it outperforms these regression models in terms of robustness as shown in our study from a re-descending M-estimation view.
Consistency Analysis of an Empirical Minimum Error Entropy Algorithm
The error entropy consistency, which requires the error entropy of the learned function to approximate the minimum error entropy, is shown to be always true if the bandwidth parameter tends to 0 at an appropriate rate.
