Search Results for author: Benjamin F. Grewe

Found 17 papers, 11 papers with code

Towards guarantees for parameter isolation in continual learning

no code implementations2 Oct 2023 Giulia Lanzillotta, Sidak Pal Singh, Benjamin F. Grewe, Thomas Hofmann

Deep learning has proved to be a successful paradigm for solving many challenges in machine learning.

Continual Learning

A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions

no code implementations11 Jul 2023 Peng Yan, Ahmed Abdulkadir, Paul-Philipp Luley, Matthias Rosenthal, Gerrit A. Schatte, Benjamin F. Grewe, Thilo Stadelmann

However, due to the dynamic nature of the industrial processes and environment, it is impractical to acquire large-scale labeled data for standard deep learning training for every slightly different case anew.

Anomaly Detection energy management +3

Safe Deep RL for Intraoperative Planning of Pedicle Screw Placement

no code implementations9 May 2023 Yunke Ao, Hooman Esfandiari, Fabio Carrillo, Yarden As, Mazda Farshad, Benjamin F. Grewe, Andreas Krause, Philipp Fuernstahl

Spinal fusion surgery requires highly accurate implantation of pedicle screw implants, which must be conducted in critical proximity to vital structures with a limited view of anatomy.


Bio-Inspired, Task-Free Continual Learning through Activity Regularization

no code implementations8 Dec 2022 Francesco Lässig, Pau Vilimelis Aceituno, Martino Sorbaro, Benjamin F. Grewe

We evaluate the new sparse-recurrent version of DFC on the split-MNIST computer vision benchmark and show that only the combination of sparsity and intra-layer recurrent connections improves CL performance with respect to standard backpropagation.

Continual Learning Split-MNIST

Meta-Learning via Classifier(-free) Diffusion Guidance

1 code implementation17 Oct 2022 Elvis Nava, Seijin Kobayashi, Yifei Yin, Robert K. Katzschmann, Benjamin F. Grewe

Our methods repurpose the popular generative image synthesis techniques of natural language guidance and diffusion models to generate neural network weights adapted for tasks.

Few-Shot Learning Image Generation +2

A Theory of Natural Intelligence

no code implementations22 Apr 2022 Christoph von der Malsburg, Thilo Stadelmann, Benjamin F. Grewe

Introduction: In contrast to current AI technology, natural intelligence -- the kind of autonomous intelligence that is realized in the brains of animals and humans to attain in their natural environment goals defined by a repertoire of innate behavioral schemata -- is far superior in terms of learning speed, generalization capabilities, autonomy and creativity.

Inductive Bias

Minimizing Control for Credit Assignment with Strong Feedback

2 code implementations14 Apr 2022 Alexander Meulemans, Matilde Tristany Farinha, Maria R. Cervera, João Sacramento, Benjamin F. Grewe

Building upon deep feedback control (DFC), a recently proposed credit assignment method, we combine strong feedback influences on neural activity with gradient-based learning and show that this naturally leads to a novel view on neural network optimization.

Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models

no code implementations30 Mar 2022 Elvis Nava, John Z. Zhang, Mike Y. Michelis, Tao Du, Pingchuan Ma, Benjamin F. Grewe, Wojciech Matusik, Robert K. Katzschmann

For the deformable solid simulation of the swimmer's body, we use state-of-the-art techniques from the field of computer graphics to speed up the finite-element method (FEM).

Computational Efficiency

Uncertainty estimation under model misspecification in neural network regression

1 code implementation23 Nov 2021 Maria R. Cervera, Rafael Dätwyler, Francesco D'Angelo, Hamza Keurti, Benjamin F. Grewe, Christian Henning

Although neural networks are powerful function approximators, the underlying modelling assumptions ultimately define the likelihood and thus the hypothesis class they are parameterizing.

Decision Making regression

Credit Assignment in Neural Networks through Deep Feedback Control

3 code implementations NeurIPS 2021 Alexander Meulemans, Matilde Tristany Farinha, Javier García Ordóñez, Pau Vilimelis Aceituno, João Sacramento, Benjamin F. Grewe

The success of deep learning sparked interest in whether the brain learns by using similar techniques for assigning credit to each synaptic weight for its contribution to the network output.

Posterior Meta-Replay for Continual Learning

3 code implementations NeurIPS 2021 Christian Henning, Maria R. Cervera, Francesco D'Angelo, Johannes von Oswald, Regina Traber, Benjamin Ehret, Seijin Kobayashi, Benjamin F. Grewe, João Sacramento

We offer a practical deep learning implementation of our framework based on probabilistic task-conditioned hypernetworks, an approach we term posterior meta-replay.

Continual Learning

Neural networks with late-phase weights

2 code implementations ICLR 2021 Johannes von Oswald, Seijin Kobayashi, Alexander Meulemans, Christian Henning, Benjamin F. Grewe, João Sacramento

The largely successful method of training neural networks is to learn their weights using some variant of stochastic gradient descent (SGD).

Ranked #70 on Image Classification on CIFAR-100 (using extra training data)

Image Classification

A Theoretical Framework for Target Propagation

2 code implementations NeurIPS 2020 Alexander Meulemans, Francesco S. Carzaniga, Johan A. K. Suykens, João Sacramento, Benjamin F. Grewe

Here, we analyze target propagation (TP), a popular but not yet fully understood alternative to BP, from the standpoint of mathematical optimization.

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