209 citations · 275 across the 21 of their papers we have counts for
25 papers
Mixed-signal implementation of feedback-control optimizer for single-layer Spiking Neural Networks
Jonathan Haag, Christian Metzner, Dmitrii Zendrikov +4
On-chip learning is key to scalable and adaptive neuromorphic systems, yet existing training methods are either difficult to implement in hardware or overly restrictive. However, r…
Mechanistic Interpretability of RNNs emulating Hidden Markov Models
Elia Torre, Michele Viscione, Lucas Pompe +2
Recurrent neural networks (RNNs) provide a powerful approach in neuroscience to infer latent dynamics in neural populations and to generate hypotheses about the neural computations…
Robust-Sub-Gaussian Model Predictive Control for Safe Ultrasound-Image-Guided Robotic Spinal Surgery
Yunke Ao, Manish Prajapat, Yarden As +6
Safety-critical control using high-dimensional sensory feedback from optical data (e.g., images, point clouds) poses significant challenges in domains like autonomous driving and r…
mimic-one: a Scalable Model Recipe for General Purpose Robot Dexterity
Elvis Nava, Victoriano Montesinos, Erik Bauer +8
We present a diffusion-based model recipe for real-world control of a highly dexterous humanoid robotic hand, designed for sample-efficient learning and smooth fine-motor action in…
Local vs Global continual learning
Giulia Lanzillotta, Sidak Pal Singh, Benjamin F. Grewe +1
Continual learning is the problem of integrating new information in a model while retaining the knowledge acquired in the past. Despite the tangible improvements achieved in recent…
The Cooperative Network Architecture: Learning Structured Networks as Representation of Sensory Patterns
Pascal J. Sager, Jan M. Deriu, Benjamin F. Grewe +2
We introduce the Cooperative Network Architecture (CNA), a model that represents sensory signals using structured, recurrently connected networks of neurons, termed "nets." Nets ar…
Two types of pyramidal cells and their role in temporal processing
Anh Duong Vo, Elisabeth Abs, Pau Vilimelis Aceituno +2
Recent work has provided new insights into the temporal specialization of Intratelencephalic (IT) and Pyramidal tract neurons (PT). However, functional and anatomical differences o…
Towards guarantees for parameter isolation in continual learning
Giulia Lanzillotta, Sidak Pal Singh, Benjamin F. Grewe +1
Deep learning has proved to be a successful paradigm for solving many challenges in machine learning. However, deep neural networks fail when trained sequentially on multiple tasks…
A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions
Peng Yan, Ahmed Abdulkadir, Paul-Philipp Luley +4
Automating the monitoring of industrial processes has the potential to enhance efficiency and optimize quality by promptly detecting abnormal events and thus facilitating timely in…
Safe Deep RL for Intraoperative Planning of Pedicle Screw Placement
Yunke Ao, Hooman Esfandiari, Fabio Carrillo +5
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 anat…
Bio-Inspired, Task-Free Continual Learning through Activity Regularization
Francesco Lässig, Pau Vilimelis Aceituno, Martino Sorbaro +1
The ability to sequentially learn multiple tasks without forgetting is a key skill of biological brains, whereas it represents a major challenge to the field of deep learning. To a…
Active Learning for Imbalanced Civil Infrastructure Data
Thomas Frick, Diego Antognini, Mattia Rigotti +3
Aging civil infrastructures are closely monitored by engineers for damage and critical defects. As the manual inspection of such large structures is costly and time-consuming, we a…
Meta-Learning via Classifier(-free) Diffusion Guidance
Elvis Nava, Seijin Kobayashi, Yifei Yin +2
We introduce meta-learning algorithms that perform zero-shot weight-space adaptation of neural network models to unseen tasks. Our methods repurpose the popular generative image sy…
Homomorphism Autoencoder -- Learning Group Structured Representations from Observed Transitions
Hamza Keurti, Hsiao-Ru Pan, Michel Besserve +2
How can agents learn internal models that veridically represent interactions with the real world is a largely open question. As machine learning is moving towards representations c…
A Theory of Natural Intelligence
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 th…
Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models
Elvis Nava, John Z. Zhang, Mike Y. Michelis +5
Aquatic locomotion is a classic fluid-structure interaction (FSI) problem of interest to biologists and engineers. Solving the fully coupled FSI equations for incompressible Navier…
Minimizing Control for Credit Assignment with Strong Feedback
Alexander Meulemans, Matilde Tristany Farinha, Maria R. Cervera +2
The success of deep learning ignited interest in whether the brain learns hierarchical representations using gradient-based learning. However, current biologically plausible method…
Uncertainty estimation under model misspecification in neural network regression
Maria R. Cervera, Rafael Dätwyler, Francesco D'Angelo +3
Although neural networks are powerful function approximators, the underlying modelling assumptions ultimately define the likelihood and thus the hypothesis class they are parameter…
Are Bayesian neural networks intrinsically good at out-of-distribution detection?
Christian Henning, Francesco D'Angelo, Benjamin F. Grewe
The need to avoid confident predictions on unfamiliar data has sparked interest in out-of-distribution (OOD) detection. It is widely assumed that Bayesian neural networks (BNN) are…
Credit Assignment in Neural Networks through Deep Feedback Control
Alexander Meulemans, Matilde Tristany Farinha, Javier García Ordóñez +3
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 netwo…
Posterior Meta-Replay for Continual Learning
Christian Henning, Maria R. Cervera, Francesco D'Angelo +6
Learning a sequence of tasks without access to i.i.d. observations is a widely studied form of continual learning (CL) that remains challenging. In principle, Bayesian learning dir…
Neural networks with late-phase weights
Johannes von Oswald, Seijin Kobayashi, Alexander Meulemans +3
The largely successful method of training neural networks is to learn their weights using some variant of stochastic gradient descent (SGD). Here, we show that the solutions found…
A Theoretical Framework for Target Propagation
Alexander Meulemans, Francesco S. Carzaniga, Johan A. K. Suykens +2
The success of deep learning, a brain-inspired form of AI, has sparked interest in understanding how the brain could similarly learn across multiple layers of neurons. However, the…
Continual Learning in Recurrent Neural Networks
Benjamin Ehret, Christian Henning, Maria R. Cervera +3
While a diverse collection of continual learning (CL) methods has been proposed to prevent catastrophic forgetting, a thorough investigation of their effectiveness for processing s…