65 citations · 107 across the 15 of their papers we have counts for
6 papers · 1 filter
Visualizing Deep Neural Networks with Topographic Activation Maps
Valerie Krug, Raihan Kabir Ratul, Christopher Olson +1
Machine Learning with Deep Neural Networks (DNNs) has become a successful tool in solving tasks across various fields of application. However, the complexity of DNNs makes it diffi…
Differentiable Generalised Predictive Coding
André Ofner, Sebastian Stober
This paper deals with differentiable dynamical models congruent with neural process theories that cast brain function as the hierarchical refinement of an internal generative model…
Predictive coding, precision and natural gradients
Andre Ofner, Raihan Kabir Ratul, Suhita Ghosh +1
There is an increasing convergence between biologically plausible computational models of inference and learning with local update rules and the global gradient-based optimization…
PredProp: Bidirectional Stochastic Optimization with Precision Weighted Predictive Coding
André Ofner, Sebastian Stober
We present PredProp, a method for optimization of weights and states in predictive coding networks (PCNs) based on the precision of propagated errors and neural activity. PredProp…
Gradient-Adjusted Neuron Activation Profiles for Comprehensive Introspection of Convolutional Speech Recognition Models
Andreas Krug, Sebastian Stober
Deep Learning based Automatic Speech Recognition (ASR) models are very successful, but hard to interpret. To gain better understanding of how Artificial Neural Networks (ANNs) acco…
Transfer Learning for Speech Recognition on a Budget
Julius Kunze, Louis Kirsch, Ilia Kurenkov +3
End-to-end training of automated speech recognition (ASR) systems requires massive data and compute resources. We explore transfer learning based on model adaptation as an approach…