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Recherche automatique d'architecture neuronale

Publications associées (159)

Learning to Predict Human Behavior in Crowded Scenes

Alexandre Massoud Alahi, Vignesh Ramanathan

Pedestrians follow different trajectories to avoid obstacles and accommodate fellow pedestrians. Any autonomous vehicle navigating such a scene should be able to foresee the future positions of pedestrians and accordingly adjust its path to avoid collision ...

Elsevier2017

Efﬁcient Feature Embeddings for Student Classiﬁcation with Variational Auto-encoders

Gathering labeled data in educational data mining (EDM) is a time and cost intensive task. However, the amount of available training data directly influences the quality of predictive models. Unlabeled data, on the other hand, is readily available in high ...

EDM2017

Neural Correlates of Sequential Decision Making

Michael Herzog, He Xu

In chess, a series of moves is made until a delayed sparse feedback (win, loss) is issued, which makes it impossible to evaluate the value of a single move. There are powerful reinforcement learning (RL) algorithms, which can cope with these sequential dec ...

2017

Neural Correlates of Sequential Decision Making

Michael Herzog, He Xu

2017

INTRA-CLASS COVARIANCE ADAPTATION IN PLDA BACK-ENDS FOR SPEAKER VERIFICATION

Petr Motlicek, Subhadeep Dey

Multi-session training conditions are becoming increasingly common in recent benchmark datasets for both text-independent and text-dependent speaker verification. In the state-of-the-art i-vector framework for speaker verification, such conditions are addr ...

Ieee2017

Neural assemblies as core elements for modeling neural networks in the brain

Hesam Setareh

How does the brain process and memorize information? We all know that the neuron (also known as nerve cell) is the processing unit in the brain. But how do neurons work together in networks? The connectivity structure of neural networks plays an important ...

EPFL2017

Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies

Alexandre Massoud Alahi

The majority of existing solutions to the Multi-Target Tracking (MTT) problem do not combine cues in a coherent end-to-end fashion over a long period of time. However, we present an online method that encodes long-term temporal dependencies across multiple ...

2017

Learning heat diffusion graphs

Pascal Frossard, Daniel Kressner, Xiaowen Dong

Effective information analysis generally boils down to properly identifying the structure or geometry of the data, which is often represented by a graph. In some applications, this structure may be partly determined by design constraints or pre-determined ...

Ieee-Inst Electrical Electronics Engineers Inc2017

Deep Convolutional Neural Network for Inverse Problems in Imaging

Michaël Unser, Kyong Hwan Jin, Michael Thompson McCann, Emmanuel Augustin Julien Froustey

In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. ...

2017

Boundary-aware Instance Segmentation

Mathieu Salzmann

We address the problem of instance-level semantic segmentation, which aims at jointly detecting, segmenting and classifying every individual object in an image. In this context, existing methods typically propose candidate objects, usually as bounding boxe ...

Ieee2017

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