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.
Proposes a novel framework for multi-label text classification.
Capsule networks improve performance on image classification tasks with fewer parameters.
Paper proposes a capsule attention mechanism for EEG-EOG vigilance estimation.
Caps2NE learns node embeddings in graphs using capsule layers.
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 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. …
With the growth of deep learning, how to describe deep neural networks unifiedly is becoming an important issue. We first formalize neural networks mathematically with their directed graph representations, and prove a generation theorem about the induced networks of connected directed acyclic graphs. Then, we set up a …
Land use classification of low resolution spatial imagery is one of the most extensively researched fields in remote sensing. Despite significant advancements in satellite technology, high resolution imagery lacks global coverage and can be prohibitively expensive to procure for extended time periods. Accurately classi…
Graph classification is a significant problem in many scientific domains. It addresses tasks such as the classification of proteins and chemical compounds into categories according to their functions, or chemical and structural properties. In a supervised setting, this problem can be framed as learning the structure, f…
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 …
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…
An unsupervised learning algorithm trains capsule networks for generating realistic images.
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 …
A recently proposed method in deep learning groups multiple neurons to capsules such that each capsule represents an object or part of an object. Routing algorithms route the output of capsules from lower-level layers to upper-level layers. In this paper, we prove that state-of-the-art routing procedures decrease the e…
A new capsule routing algorithm derived from Variational Bayes improves performance in neural networks.
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.
Researchers share pitfalls and improvements in implementing Hinton's capsule network.
Capsule Networks improve clothing retrieval without landmark info.
Capsule networks improve anomaly detection in high-dimensional datasets.
Capsule networks improve with dynamic routing using Wasserstein objective.
A new capsule network framework that preserves input transformations.
Capsule networks improve temporal data understanding, achieving 96.21% ECG accuracy.
Stacked Capsule Autoencoders reconstruct objects from images using part relationships.
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, …
Capsules learn from expert neurons using dynamic routing.
Project learns to model Capsule Networks' routing procedures for better expressiveness.
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…
Geometric Capsule Autoencoders group 3D points into parts and objects.
3D capsule network for point clouds handles rotations and translations.
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…
X-Caps improves medical diagnosis explainability by encoding visual attributes in capsules.
Capsule networks improve at detecting changes in compositionality with routing.
Method screens similar capsule endoscopic images, reducing doctor workload and improving accuracy.
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…
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.
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…
Explains deep learning models and their geometric properties.
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 …
TreeCaps improves code comprehension for software developers.
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…
A novel capsule network model improves surrogate modeling and uncertainty quantification from sparse data.