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Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism

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Advances in soft sensors coupled with machine learning are enabling increasingly capable wearable systems. Since hand motion in particular can convey useful information for developing intuitive interfaces, glove-based systems can have a significant impact ...

Institute of Electrical and Electronics Engineers Inc.2022

Kernel Memory Networks: A Unifying Framework for Memory Modeling

Wulfram Gerstner, Johanni Michael Brea, Georgios Iatropoulos

We consider the problem of training a neural network to store a set of patterns with maximal noise robustness. A solution, in terms of optimal weights and state update rules, is derived by training each individual neuron to perform either kernel classifica ...

2022

Geometry-Aware Deep Recurrent Neural Networks for Hyperspectral Image Classification

Mathieu Salzmann, Wei Wang, Siyuan Hao

Variants of deep networks have been widely used for hyperspectral image (HSI)-classification tasks. Among them, in recent years, recurrent neural networks (RNNs) have attracted considerable attention in the remote sensing community. However, complex geomet ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Optical flow is a method aimed at predicting the movement velocity of any pixel in the image and is used in medicine and biology to estimate flow of particles in organs or organelles. However, a precise optical flow measurement requires images taken at hig ...

IEEE2021

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Human pose forecasting involves complex spatiotemporal interactions between body parts (e.g., arms, legs, spine). State-of-the-art approaches use Long Short-Term Memories (LSTMs) or Variational AutoEncoders (VAEs) to solve the problem. Yet, they do not eff ...

IEEE2021

Our work addresses long-term motion context issues for predicting future frames. To predict the future precisely, it is required to capture which long-term motion context (e.g., walking or running) the input motion (e.g., leg movement) belongs to. The bott ...

IEEE COMPUTER SOC2021

Reward biases spontaneous neural reactivation during sleep

Dimitri Nestor Alice Van De Ville, Maximilien Catsiyannis

Sleep favors the reactivation and consolidation of newly acquired memories. Yet, how our brain selects the noteworthy information to be reprocessed during sleep remains largely unknown. From an evolutionary perspective, individuals must retain information ...

NATURE RESEARCH2021

A reduced basis method based on a physics-informed machine learning framework is developed for efficient reduced-order modeling of parametrized partial differential equations (PDEs). A feedforward neural network is used to approximate the mapping from the ...

2021

Serial dependence under memory load

Michael Herzog, David Pascucci, Ayberk Ozkirli, Gizay Ceylan, Yury Markov, Natalia Tiurina

Perceptual decisions are biased towards previous stimuli, a phenomenon known as serial dependence. However, the mechanisms of this bias are unknown. For example, it is debated whether serial dependence reflects a perceptual bias or interference of prior st ...

SAGE Publishing2021

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The problem of estimating a surface shape from its observed reflectance properties still remains a challenging task in computer vision. The presence of global illumination effects such as inter-reflections or cast shadows makes the task particularly diffic ...

IEEE COMPUTER SOC2021