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no code implementations • 22 Nov 2021 • Zhengxin Zhang, Youssef Mroueh, Ziv Goldfeld, Bharath K. Sriperumbudur

Discrepancy measures between probability distributions are at the core of statistical inference and machine learning.

no code implementations • 10 Nov 2021 • Raphaël Pestourie, Youssef Mroueh, Chris Rackauckas, Payel Das, Steven G. Johnson

We present a "physics-enhanced deep-surrogate ("PEDS") approach towards developing fast surrogate models for complex physical systems described by partial differential equations (PDEs) and similar models: we show how to combine a low-fidelity "coarse" solver with a neural network that generates "coarsified'' inputs, trained end-to-end to globally match the output of an expensive high-fidelity numerical solver.

no code implementations • ICLR 2022 • Carles Domingo-Enrich, Youssef Mroueh

A well-known line of work (Barron, 1993; Breiman, 1993; Klusowski & Barron, 2018) provides bounds on the width $n$ of a ReLU two-layer neural network needed to approximate a function $f$ over the ball $\mathcal{B}_R(\mathbb{R}^d)$ up to error $\epsilon$, when the Fourier based quantity $C_f = \frac{1}{(2\pi)^{d/2}} \int_{\mathbb{R}^d} \|\xi\|^2 |\hat{f}(\xi)| \ d\xi$ is finite.

no code implementations • 17 Jun 2021 • Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, Inkit Padhi, Youssef Mroueh, Payel Das

Various representation learning methods in a supervised setting, including the features extracted using graph neural nets, have emerged for such tasks.

no code implementations • NeurIPS 2021 • Carles Domingo-Enrich, Youssef Mroueh

Several works in implicit and explicit generative modeling empirically observed that feature-learning discriminators outperform fixed-kernel discriminators in terms of the sample quality of the models.

1 code implementation • NeurIPS 2021 • Ching-Yao Chuang, Youssef Mroueh, Kristjan Greenewald, Antonio Torralba, Stefanie Jegelka

Understanding the generalization of deep neural networks is one of the most important tasks in deep learning.

no code implementations • 1 Jun 2021 • David Alvarez-Melis, Yair Schiff, Youssef Mroueh

Gradient flows are a powerful tool for optimizing functionals in general metric spaces, including the space of probabilities endowed with the Wasserstein metric.

1 code implementation • ICLR 2021 • Ching-Yao Chuang, Youssef Mroueh

Training classifiers under fairness constraints such as group fairness, regularizes the disparities of predictions between the groups.

no code implementations • 21 Dec 2020 • Pierre Dognin, Igor Melnyk, Youssef Mroueh, Inkit Padhi, Mattia Rigotti, Jarret Ross, Yair Schiff

Image captioning systems have made substantial progress, largely due to the availability of curated datasets like Microsoft COCO or Vizwiz that have accurate descriptions of their corresponding images.

1 code implementation • 21 Dec 2020 • Pierre Dognin, Igor Melnyk, Youssef Mroueh, Inkit Padhi, Mattia Rigotti, Jarret Ross, Yair Schiff, Richard A. Young, Brian Belgodere

Image captioning has recently demonstrated impressive progress largely owing to the introduction of neural network algorithms trained on curated dataset like MS-COCO.

no code implementations • 4 Nov 2020 • Youssef Mroueh, Truyen Nguyen

We then derive an explicit condition which ensures that gradient descent on the parameter space of the generator in gradient regularized $\mathrm{MMD}$ GAN is globally convergent to the target distribution.

1 code implementation • 3 Nov 2020 • Inkit Padhi, Yair Schiff, Igor Melnyk, Mattia Rigotti, Youssef Mroueh, Pierre Dognin, Jerret Ross, Ravi Nair, Erik Altman

This results in two architectures for tabular time series: one for learning representations that is analogous to BERT and can be pre-trained end-to-end and used in downstream tasks, and one that is akin to GPT and can be used for generation of realistic synthetic tabular sequences.

1 code implementation • NeurIPS 2020 • Youssef Mroueh, Mattia Rigotti

USD transports particles along gradient flows of the witness function of the Sobolev-Fisher discrepancy (advection step) and reweighs the mass of particles with respect to this witness function (reaction step).

no code implementations • 24 Aug 2020 • Raphaël Pestourie, Youssef Mroueh, Thanh V. Nguyen, Payel Das, Steven G. Johnson

Surrogate models for partial-differential equations are widely used in the design of meta-materials to rapidly evaluate the behavior of composable components.

no code implementations • 6 Jul 2020 • Wei-Cheng Chang, Chun-Liang Li, Youssef Mroueh, Yiming Yang

NCK is crucial for successful inference with SVGD in high dimension, as it adapts the kernel to the noise level of the score estimate.

no code implementations • 19 Jun 2020 • Adam Block, Youssef Mroueh, Alexander Rakhlin, Jerret Ross

Recently, the task of image generation has attracted much attention.

no code implementations • ACL 2020 • Inkit Padhi, Pierre Dognin, Ke Bai, Cicero Nogueira dos santos, Vijil Chenthamarakshan, Youssef Mroueh, Payel Das

Generative feature matching network (GFMN) is an approach for training implicit generative models for images by performing moment matching on features from pre-trained neural networks.

no code implementations • ICLR Workshop DeepDiffEq 2019 • Thanh V. Nguyen, Youssef Mroueh, Samuel Hoffman, Payel Das, Pierre Dognin, Giuseppe Romano, Chinmay Hegde

We consider the problem of optimizing by sampling under multiple black-box constraints in nano-material design.

no code implementations • 4 Feb 2020 • Wei Zhang, Xiaodong Cui, Abdullah Kayi, Mingrui Liu, Ulrich Finkler, Brian Kingsbury, George Saon, Youssef Mroueh, Alper Buyuktosunoglu, Payel Das, David Kung, Michael Picheny

Decentralized Parallel SGD (D-PSGD) and its asynchronous variant Asynchronous Parallel SGD (AD-PSGD) is a family of distributed learning algorithms that have been demonstrated to perform well for large-scale deep learning tasks.

no code implementations • 31 Jan 2020 • Adam Block, Youssef Mroueh, Alexander Rakhlin

We show that both DAE and DSM provide estimates of the score of the Gaussian smoothed population density, allowing us to apply the machinery of Empirical Processes.

no code implementations • ICLR 2020 • Mingrui Liu, Youssef Mroueh, Jerret Ross, Wei zhang, Xiaodong Cui, Payel Das, Tianbao Yang

Then we propose an adaptive variant of OSG named Optimistic Adagrad (OAdagrad) and reveal an \emph{improved} adaptive complexity $O\left(\epsilon^{-\frac{2}{1-\alpha}}\right)$, where $\alpha$ characterizes the growth rate of the cumulative stochastic gradient and $0\leq \alpha\leq 1/2$.

no code implementations • 6 Nov 2019 • David Alvarez-Melis, Youssef Mroueh, Tommi S. Jaakkola

This paper focuses on the problem of unsupervised alignment of hierarchical data such as ontologies or lexical databases.

1 code implementation • NeurIPS 2019 • Youssef Mroueh, Tom Sercu, Mattia Rigotti, Inkit Padhi, Cicero dos Santos

In the kernel version we show that SIC can be cast as a convex optimization problem by introducing auxiliary variables that play an important role in feature selection as they are normalized feature importance scores.

no code implementations • NeurIPS 2020 • Mingrui Liu, Wei zhang, Youssef Mroueh, Xiaodong Cui, Jerret Ross, Tianbao Yang, Payel Das

Despite recent progress on decentralized algorithms for training deep neural networks, it remains unclear whether it is possible to train GANs in a decentralized manner.

no code implementations • 25 Sep 2019 • Thanh V Nguyen, Youssef Mroueh, Samuel C. Hoffman, Payel Das, Pierre Dognin, Giuseppe Romano, Chinmay Hegde

We consider the problem of generating configurations that satisfy physical constraints for optimal material nano-pattern design, where multiple (and often conflicting) properties need to be simultaneously satisfied.

1 code implementation • 30 May 2019 • Youssef Mroueh

Moreover interpolates between a content and a style image can be seen as geodesics in the Wasserstein Geometry.

no code implementations • ICLR 2019 • Cicero Nogueira dos Santos, Inkit Padhi, Pierre Dognin, Youssef Mroueh

We propose a non-adversarial feature matching-based approach to train generative models.

no code implementations • ICCV 2019 • Cicero Nogueira dos Santos, Youssef Mroueh, Inkit Padhi, Pierre Dognin

Perceptual features (PFs) have been used with great success in tasks such as transfer learning, style transfer, and super-resolution.

no code implementations • ICLR Workshop DeepGenStruct 2019 • Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jarret Ross, Tom Sercu

In this paper we study image captioning as a conditional GAN training, proposing both a context-aware LSTM captioner and co-attentive discriminator, which enforces semantic alignment between images and captions.

no code implementations • 26 Feb 2019 • Chun-Liang Li, Wei-Cheng Chang, Youssef Mroueh, Yiming Yang, Barnabás Póczos

While learning the kernel in a data driven way has been investigated, in this paper we explore learning the spectral distribution of kernel via implicit generative models parametrized by deep neural networks.

1 code implementation • 13 Feb 2019 • Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jerret Ross, Cicero dos Santos, Tom Sercu

In this paper we propose to perform model ensembling in a multiclass or a multilabel learning setting using Wasserstein (W.) barycenters.

no code implementations • 30 May 2018 • Youssef Mroueh, Tom Sercu, Anant Raj

We study a simplification of GAN training: the problem of transporting particles from a source to a target distribution.

no code implementations • 16 May 2018 • Youssef Mroueh

We show in this note that the Sobolev Discrepancy introduced in Mroueh et al in the context of generative adversarial networks, is actually the weighted negative Sobolev norm $||.||_{\dot{H}^{-1}(\nu_q)}$, that is known to linearize the Wasserstein $W_2$ distance and plays a fundamental role in the dynamic formulation of optimal transport of Benamou and Brenier.

no code implementations • 30 Apr 2018 • Pierre L. Dognin, Igor Melnyk, Youssef Mroueh, Jarret Ross, Tom Sercu

When evaluated on OOC and MS-COCO benchmarks, we show that SCST-based training has a strong performance in both semantic score and human evaluation, promising to be a valuable new approach for efficient discrete GAN training.

no code implementations • 7 Dec 2017 • Tom Sercu, Youssef Mroueh

We present an empirical investigation of a recent class of Generative Adversarial Networks (GANs) using Integral Probability Metrics (IPM) and their performance for semi-supervised learning.

1 code implementation • ICLR 2018 • Youssef Mroueh, Chun-Liang Li, Tom Sercu, Anant Raj, Yu Cheng

We show that the Sobolev IPM compares two distributions in high dimensions based on weighted conditional Cumulative Distribution Functions (CDF) of each coordinate on a leave one out basis.

2 code implementations • NeurIPS 2017 • Youssef Mroueh, Tom Sercu

In this paper we introduce Fisher GAN which fits within the Integral Probability Metrics (IPM) framework for training GANs.

no code implementations • ICML 2017 • Youssef Mroueh, Tom Sercu, Vaibhava Goel

We introduce new families of Integral Probability Metrics (IPM) for training Generative Adversarial Networks (GAN).

no code implementations • 6 Dec 2016 • Anant Raj, Abhishek Kumar, Youssef Mroueh, P. Thomas Fletcher, Bernhard Schölkopf

We consider transformations that form a \emph{group} and propose an approach based on kernel methods to derive local group invariant representations.

26 code implementations • CVPR 2017 • Steven J. Rennie, Etienne Marcheret, Youssef Mroueh, Jarret Ross, Vaibhava Goel

In this paper we consider the problem of optimizing image captioning systems using reinforcement learning, and show that by carefully optimizing our systems using the test metrics of the MSCOCO task, significant gains in performance can be realized.

no code implementations • 25 Oct 2016 • Youssef Mroueh, Etienne Marcheret, Vaibhava Goel

We introduce co-occurring directions sketching, a deterministic algorithm for approximate matrix product (AMM), in the streaming model.

no code implementations • NeurIPS 2015 • Youssef Mroueh, Stephen Voinea, Tomaso A. Poggio

Our analysis bridges invariant feature learning with kernel methods, as we show that this feature map defines an expected Haar-integration kernel that is invariant to the specified group action.

no code implementations • 19 Nov 2015 • Youssef Mroueh, Etienne Marcheret, Vaibhava Goel

Joint modeling of language and vision has been drawing increasing interest.

no code implementations • 11 Jun 2015 • Youssef Mroueh, Steven Rennie, Vaibhava Goel

In this paper, we propose and study random maxout features, which are constructed by first projecting the input data onto sets of randomly generated vectors with Gaussian elements, and then outputing the maximum projection value for each set.

no code implementations • NeurIPS 2015 • Youssef Mroueh, Stephen Voinea, Tomaso Poggio

Our analysis bridges invariant feature learning with kernel methods, as we show that this feature map defines an expected Haar integration kernel that is invariant to the specified group action.

1 code implementation • 13 Apr 2015 • Carlo Ciliberto, Youssef Mroueh, Tomaso Poggio, Lorenzo Rosasco

In this context a fundamental question is how to incorporate the tasks structure in the learning problem. We tackle this question by studying a general computational framework that allows to encode a-priori knowledge of the tasks structure in the form of a convex penalty; in this setting a variety of previously proposed methods can be recovered as special cases, including linear and non-linear approaches.

no code implementations • 22 Jan 2015 • Youssef Mroueh, Etienne Marcheret, Vaibhava Goel

In this paper, we present methods in deep multimodal learning for fusing speech and visual modalities for Audio-Visual Automatic Speech Recognition (AV-ASR).

Audio-Visual Speech Recognition
Automatic Speech Recognition
**+1**

no code implementations • 16 Nov 2013 • Qianli Liao, Joel Z. Leibo, Youssef Mroueh, Tomaso Poggio

The standard approach to unconstrained face recognition in natural photographs is via a detection, alignment, recognition pipeline.

no code implementations • NeurIPS 2012 • Youssef Mroueh, Tomaso Poggio, Lorenzo Rosasco, Jean-Jeacques Slotine

In this paper we dicuss a novel framework for multiclass learning, defined by a suitable coding/decoding strategy, namely the simplex coding, that allows to generalize to multiple classes a relaxation approach commonly used in binary classification.

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