Bilinear Sequence Regression: A Model for Learning from Long Sequences of High-Dimensional Tokens

À propos
Confidentialité
Mentions légales

Graph Chatbot

Publications associées (26)

Page 2 sur 2

Use of machine learning for unraveling hidden correlations between particle size distributions and the mechanical behavior of granular materials

Pablo Antolin Sanchez

A data-driven framework was used to predict the macroscopic mechanical behavior of dense packings of polydisperse granular materials. The discrete element method, DEM, was used to generate 92,378 sphere packings that covered many different kinds of particl ...

SPRINGER HEIDELBERG2022

Adversarial Confidence Estimation Networks for Robust Stereo Matching

Seungryong Kim

Stereo matching aiming to perceive the 3-D geometry of a scene facilitates numerous computer vision tasks used in advanced driver assistance systems (ADAS). Although numerous methods have been proposed for this task by leveraging deep convolutional neural ...

IEEE Institute of Electrical and Electronics Engineers2021

Traffic Signal Prediction on Transportation Networks Using Spatio-Temporal Correlations on Graphs

Pascal Frossard, Nikolaos Geroliminis, Semin Kwak

Multivariate time series forecasting poses challenges as the variables are intertwined in time and space, like in the case of traffic signals. Defining signals on graphs relaxes such complexities by representing the evolution of signals over a space using ...

2021

CLaiMS-Net: cortical lesions assessment in multiple sclerosis patients via a convolutional neural network and a single 3T MRI acquisition

Jean-Philippe Thiran, Meritxell Bach Cuadra, Cristina Granziera, Muhamed Barakovic, Francesco La Rosa

SAGE PUBLICATIONS LTD2021

Accelerating Inference on Binary Neural Networks with Digital RRAM Processing

David Atienza Alonso, Marina Zapater Sancho, Yasir Mahmood Qureshi

The need for efficient Convolutional Neural Network (CNNs) targeting embedded systems led to the popularization of Binary Neural Networks (BNNs), which significantly reduce execution time and memory requirements by representing the operands using only one ...

Springer Nature2020

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