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Found 9 papers, 5 papers with code
Greedy-layer Pruning: Speeding up Transformer Models for Natural Language Processing
Knowledge distillation maintains high performance and reaches high compression rates, nevertheless, the size of the student model is fixed after pre-training and can not be changed individually for a given downstream task and use-case to reach a desired performance/speedup ratio.
Training Deep Capsule Networks with Residual Connections
Capsule networks are a type of neural network that have recently gained increased popularity.
Auto-tuning of Deep Neural Networks by Conflicting Layer Removal
In the worst-case scenario, we prove that such a layer could lead to a network that cannot be trained at all.
Conflicting Bundles: Adapting Architectures Towards the Improved Training of Deep Neural Networks
In this paper, we introduce a novel theory and metric to identify layers that decrease the test accuracy of the trained models, this identification is done as early as at the beginning of training.
Limitation of capsule networks
A recently proposed method in deep learning groups multiple neurons to capsules such that each capsule represents an object or part of an object.
Increasing the adversarial robustness and explainability of capsule networks with $γ$-capsules
In this paper we introduce a new inductive bias for capsule networks and call networks that use this prior $\gamma$-capsule networks.
Guided Labeling using Convolutional Neural Networks
Over the last couple of years, deep learning and especially convolutional neural networks have become one of the work horses of computer vision.
Evaluation of Deep Learning on an Abstract Image Classification Dataset
Convolutional Neural Networks have become state of the art methods for image classification over the last couple of years.
Learning Abstract Classes using Deep Learning
Humans are generally good at learning abstract concepts about objects and scenes (e. g.\ spatial orientation, relative sizes, etc.).
