Search Results for author:
Found 11 papers, 9 papers with code
ANLS* -- A Universal Document Processing Metric for Generative Large Language Models
However, evaluating GLLMs presents a challenge as the binary true or false evaluation used for discriminative models is not applicable to the predictions made by GLLMs.
Affordance detection with Dynamic-Tree Capsule Networks
In the experimental evaluation we will show that our algorithm is superior to current affordance detection methods when faced with grasping previously unseen objects thanks to our Capsule Network enforcing a parts-to-whole representation.
Improving the Trainability of Deep Neural Networks through Layerwise Batch-Entropy Regularization
We show empirically that we can therefore train a "vanilla" fully connected network and convolutional neural network -- no skip connections, batch normalization, dropout, or any other architectural tweak -- with 500 layers by simply adding the batch-entropy regularization term to the loss function.
Momentum Capsule Networks
MoCapsNets are inspired by Momentum ResNets, a type of network that applies reversible residual building blocks.
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.
Arguments for the Unsuitability of Convolutional Neural Networks for Non--Local Tasks
Convolutional neural networks have established themselves over the past years as the state of the art method for image classification, and for many datasets, they even surpass humans in categorizing images.
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.
