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Found 17 papers, 11 papers with code
Towards guarantees for parameter isolation in continual learning
Deep learning has proved to be a successful paradigm for solving many challenges in machine learning.
A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions
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.
Safe Deep RL for Intraoperative Planning of Pedicle Screw Placement
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
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.
Meta-Learning via Classifier(-free) Diffusion Guidance
Our methods repurpose the popular generative image synthesis techniques of natural language guidance and diffusion models to generate neural network weights adapted for tasks.
Homomorphism Autoencoder -- Learning Group Structured Representations from Observed Transitions
How can agents learn internal models that veridically represent interactions with the real world is a largely open question.
A Theory of Natural Intelligence
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.
Minimizing Control for Credit Assignment with Strong Feedback
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
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).
Uncertainty estimation under model misspecification in neural network regression
Although neural networks are powerful function approximators, the underlying modelling assumptions ultimately define the likelihood and thus the hypothesis class they are parameterizing.
Are Bayesian neural networks intrinsically good at out-of-distribution detection?
The need to avoid confident predictions on unfamiliar data has sparked interest in out-of-distribution (OOD) detection.
Credit Assignment in Neural Networks through Deep Feedback Control
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
We offer a practical deep learning implementation of our framework based on probabilistic task-conditioned hypernetworks, an approach we term posterior meta-replay.
Neural networks with late-phase weights
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)
A Theoretical Framework for Target Propagation
Here, we analyze target propagation (TP), a popular but not yet fully understood alternative to BP, from the standpoint of mathematical optimization.
Continual Learning in Recurrent Neural Networks
Here, we provide the first comprehensive evaluation of established CL methods on a variety of sequential data benchmarks.
Continual learning with hypernetworks
Artificial neural networks suffer from catastrophic forgetting when they are sequentially trained on multiple tasks.
Ranked #4 on
Continual Learning
on F-CelebA (10 tasks)
