A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Capsule networks can only represent symmetric functions due to routing limitations.
Capsule networks improve performance on image classification tasks with fewer parameters.
Capsules learn from expert neurons using dynamic routing.
An unsupervised learning algorithm trains capsule networks for generating realistic images.
Project learns to model Capsule Networks' routing procedures for better expressiveness.
A new capsule routing algorithm derived from Variational Bayes improves performance in neural networks.
Capsule networks improve with dynamic routing using Wasserstein objective.
Capsule networks improve at detecting changes in compositionality with routing.
3D capsule network for point clouds handles rotations and translations.
Researchers share pitfalls and improvements in implementing Hinton's capsule network.
A new capsule network framework that preserves input transformations.
A capsule is a collection of neurons which represents different variants of a pattern in the network. The routing scheme ensures only certain capsules which resemble lower counterparts in the higher layer should be activated. However, the computational complexity becomes a bottleneck for scaling up to larger networks, …
Capsule Networks improve clothing retrieval without landmark info.
Convolutional neural networks (CNNs) have shown remarkable results over the last several years for a wide range of computer vision tasks. A new architecture recently introduced by Sabour et al., referred to as a capsule networks with dynamic routing, has shown great initial results for digit recognition and small image…
Deep CapsNet improves sign language recognition from wearable IMUs.
In this paper we introduce a new inductive bias for capsule networks and call networks that use this prior -capsule networks. Our inductive bias that is inspired by TE neurons of the inferior temporal cortex increases the adversarial robustness and the explainability of capsule networks. A theoretical framework with…
Convolutional neural networks are the most widely used deep learning algorithms for traffic signal classification till date but they fail to capture pose, view, orientation of the images because of the intrinsic inability of max pooling layer.This paper proposes a novel method for Traffic sign detection using deep lear…
Capsule networks improve anomaly detection in high-dimensional datasets.
Caps2NE learns node embeddings in graphs using capsule layers.
X-Caps improves medical diagnosis explainability by encoding visual attributes in capsules.
Capsule network (CapsNet) was introduced as an enhancement over convolutional neural networks, supplementing the latter's invariance properties with equivariance through pose estimation. CapsNet achieved a very decent performance with a shallow architecture and a significant reduction in parameters count. However, the …
Text classification is a challenging problem which aims to identify the category of texts. In the process of training, word embeddings occupy a large part of parameters. Under the limitation of limited computing resources, it indirectly limits the ability of subsequent network designs. In order to reduce the number of …
Classification of audio samples is an important part of many auditory systems. Deep learning models based on the Convolutional and the Recurrent layers are state-of-the-art in many such tasks. In this paper, we approach audio classification tasks using capsule networks trained by recently proposed dynamic routing-by-ag…
Paper proposes a capsule attention mechanism for EEG-EOG vigilance estimation.
MIND models user interests with multiple vectors for better recommendation.
A methodology for resilience analysis of Capsule Networks under approximation errors.
MKCapsnet improves schizophrenia identification using multi-kernels and dropout.
In recent years, convolutional neural networks (CNN) have played an important role in the field of deep learning. Variants of CNN's have proven to be very successful in classification tasks across different domains. However, there are two big drawbacks to CNN's: their failure to take into account of important spatial h…
In this paper, we propose Dynamic Self-Attention (DSA), a new self-attention mechanism for sentence embedding. We design DSA by modifying dynamic routing in capsule network (Sabouretal.,2017) for natural language processing. DSA attends to informative words with a dynamic weight vector. We achieve new state-of-the-art …
NASCaps automates CapsNet design for better accuracy and hardware efficiency.
Capsule network has shown various advantages over convolutional neural network (CNN). It keeps more precise spatial information than CNN and uses equivariance instead of invariance during inference and highly potential to be a new effective tool for visual tasks. However, the current capsule networks have incompatible …
Capsule networks improve on traditional neural networks by using vector activations.
Improved capsule networks with kernel methods for robustness.
Capsule networks are vulnerable to adversarial attacks, similar to convolutional neural networks.
Capsule networks improve temporal data understanding, achieving 96.21% ECG accuracy.
Stacked Capsule Autoencoders reconstruct objects from images using part relationships.
Capsule Networks have great potential to tackle problems in structural biology because of their attention to hierarchical relationships. This paper describes the implementation and application of a Capsule Network architecture to the classification of RAS protein family structures on GPU-based computational resources. …
Geometric Capsule Autoencoders group 3D points into parts and objects.
Capsule models enforce object pose relationships for robustness, explored with probabilistic generative and variational methods.
Automatic recognition of the historical letters (XI-XVIII centuries) carved on the stoned walls of St.Sophia cathedral in Kyiv (Ukraine) was demonstrated by means of capsule deep learning neural network. It was applied to the image dataset of the carved Glagolitic and Cyrillic letters (CGCL), which was assembled and pr…
Capsule Networks preserve the hierarchical spatial relationships between objects, and thereby bears a potential to surpass the performance of traditional Convolutional Neural Networks (CNNs) in performing tasks like image classification. A large body of work has explored adversarial examples for CNNs, but their effecti…
Method screens similar capsule endoscopic images, reducing doctor workload and improving accuracy.
We present a simple technique that allows capsule models to detect adversarial images. In addition to being trained to classify images, the capsule model is trained to reconstruct the images from the pose parameters and identity of the correct top-level capsule. Adversarial images do not look like a typical member of t…
Capsule Networks have shown encouraging results on \textit{defacto} benchmark computer vision datasets such as MNIST, CIFAR and smallNORB. Although, they are yet to be tested on tasks where (1) the entities detected inherently have more complex internal representations and (2) there are very few instances per class to …
Capsule network improves polyp diagnosis accuracy.
Graph Convolutional Neural Networks (GCNNs) are the most recent exciting advancement in deep learning field and their applications are quickly spreading in multi-cross-domains including bioinformatics, chemoinformatics, social networks, natural language processing and computer vision. In this paper, we expose and tackl…
Capsule Networks improve autonomous navigation in sparse environments.
We present Generative Adversarial Capsule Network (CapsuleGAN), a framework that uses capsule networks (CapsNets) instead of the standard convolutional neural networks (CNNs) as discriminators within the generative adversarial network (GAN) setting, while modeling image data. We provide guidelines for designing CapsNet…