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Found 14 papers, 2 papers with code
Tensor Convolutional Sparse Coding with Low-Rank activations, an application to EEG analysis
Recently, there has been growing interest in the analysis of spectrograms of ElectroEncephaloGram (EEG), particularly to study the neural correlates of (un)-consciousness during General Anesthesia (GA).
Fusing Vector Space Models for Domain-Specific Applications
We address the problem of tuning word embeddings for specific use cases and domains.
Multivariate Convolutional Sparse Coding with Low Rank Tensor
This paper introduces a new multivariate convolutional sparse coding based on tensor algebra with a general model enforcing both element-wise sparsity and low-rankness of the activations tensors.
Post-training for Deep Learning
One of the main challenges of deep learning methods is the choice of an appropriate training strategy.
Bandits Dueling on Partially Ordered Sets
We propose an algorithm, UnchainedBandits, that efficiently finds the set of optimal arms, or Pareto front, of any poset even when pairs of comparable arms cannot be a priori distinguished from pairs of incomparable arms, with a set of minimal assumptions.
Post Training in Deep Learning with Last Kernel
One of the main challenges of deep learning methods is the choice of an appropriate training strategy.
Decoy Bandits Dueling on a Poset
We adress the problem of dueling bandits defined on partially ordered sets, or posets.
Cornering Stationary and Restless Mixing Bandits with Remix-UCB
We study the restless bandit problem where arms are associated with stationary $\varphi$-mixing processes and where rewards are therefore dependent: the question that arises from this setting is that of carefully recovering some independence by `ignoring' the values of some rewards.
Operator-valued Kernels for Learning from Functional Response Data
In this paper we consider the problems of supervised classification and regression in the case where attributes and labels are functions: a data is represented by a set of functions, and the label is also a function.
Stationary Mixing Bandits
To do so, we provide a UCB strategy together with a general regret analysis for the case where the size of the independence blocks (the ignored rewards) is fixed and we go a step beyond by providing an algorithm that is able to compute the size of the independence blocks from the data.
Equivalence of Learning Algorithms
The purpose of this paper is to introduce a concept of equivalence between machine learning algorithms.
Online Learning with Multiple Operator-valued Kernels
We consider the problem of learning a vector-valued function f in an online learning setting.
M-Power Regularized Least Squares Regression
Regularization is used to find a solution that both fits the data and is sufficiently smooth, and thereby is very effective for designing and refining learning algorithms.
Stability of Multi-Task Kernel Regression Algorithms
We show that multi-task kernel regression algorithms are uniformly stable in the general case of infinite-dimensional output spaces.
