Publications associées à Semi-differentiability

PURIFY: a new approach to radio-interferometric imaging

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

In a recent article series, the authors have promoted convex optimization algorithms for radio-interferometric imaging in the framework of compressed sensing, which leverages sparsity regularization priors for the associated inverse problem and defines a m ...

Wiley-Blackwell2014

MATHICSE Technical Report : On the dynamically orthogonal approximation of time dependent random PDEs

Fabio Nobile, Eleonora Musharbash

In this work we discuss the Dynamically Orthogonal (DO) approximation of time dependent partial differential equations with random data. The approximate solution is expanded at each time instant on a time dependent orthonormal basis in the physical domain ...

MATHICSE2014

Active Learning of Multi-Index Function Models

Volkan Cevher, Hemant Tyagi

We consider the problem of actively learning \textit{multi-index} functions of the form

f(x) = g(Ax)= \sum_{i=1}^k g_i(a_i^Tx)

from point evaluations of

f

. We assume that the function

f

is defined on an

\ell_2

-ball in

\Real^d

g

is twice contin ...

2012

An optimal first-order solver for the TV-$L_1$ optical flow problem

Pierre Vandergheynst, Gilles Puy, Emmanuel D'Angelo

In this study, we address the problem of computing efficiently a dense optical flow between two images under a total variation (TV) regularization and an

L_1

norm data fidelity constraint using a variational method. We build upon Nesterov's framework for ...

2010

Dynamic network loading: a differentiable model that derives link state distributions

Michel Bierlaire, Gunnar Flötteröd, Carolina Osorio Pizano

We present a dynamic network loading model that yields queue length distributions, accounts for spillbacks, and maintains a differentiable mapping from the dynamic demand on the dynamic queue lengths. The approach builds upon an existing stationary queuein ...

2010

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Active Learning of Multi-Index Function Models

Volkan Cevher, Hemant Tyagi

We consider the problem of actively learning \textit{multi-index} functions of the form

f(x) = g(Ax)= \sum_{i=1}^k g_i(a_i^Tx)

from point evaluations of

f

. We assume that the function

f

is defined on an

\ell_2

-ball in

\Real^d

g

is twice contin ...

2012

An optimal first-order solver for the TV-$L_1$ optical flow problem

Pierre Vandergheynst, Gilles Puy, Emmanuel D'Angelo

In this study, we address the problem of computing efficiently a dense optical flow between two images under a total variation (TV) regularization and an

L_1

norm data fidelity constraint using a variational method. We build upon Nesterov's framework for ...

2010

Semi-differentiability

PURIFY: a new approach to radio-interferometric imaging

MATHICSE Technical Report : On the dynamically orthogonal approximation of time dependent random PDEs

Learning non-parametric basis independent models from point queries via low-rank methods

Active Learning of Multi-Index Function Models

Learning Ridge Functions With Randomized Sampling In High Dimensions

An optimal first-order solver for the TV-$L_1$ optical flow problem

Dynamic network loading: a differentiable model that derives link state distributions

Graph Chatbot

Chattez avec Graph Search

Learning non-parametric basis independent models from point queries via low-rank methods

Dynamic network loading: a differentiable model that derives link state distributions

PURIFY: a new approach to radio-interferometric imaging

MATHICSE Technical Report : On the dynamically orthogonal approximation of time dependent random PDEs

Learning Ridge Functions With Randomized Sampling In High Dimensions

Active Learning of Multi-Index Function Models

An optimal first-order solver for the TV-$L_1$ optical flow problem