arXiv research

FSNet compresses CNNs by sharing weights in 1D vectors.

In this paper, we introduce an inclined curves according to parallel transport frame. Also, we define a vector field called Darboux vector field of an inclined curve in and we give a new characterization such as: "α: I \subset R \rightarrow E^4 is an inclined curve \Leftrightarrow k_1 \int k_1ds + k_2 \int \k_2 +k_3ds …

iSTFTNet2 improves iSTFTNet's speed and lightness with 1D-2D CNN.

The note answers a question about Betti numbers for 1D Euclidean space.

Improved robustness of 1D CNNs for heart arrhythmia classification.

Split learning preserves privacy in 1D CNN models for detecting heart abnormalities.

This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats. We design a 1D-CNN that directly learns features from raw heart-sound signals, and a 2D-CNN that takes inputs of two- dimensional time-frequency fea…

A new deep learning benchmark reduces resource needs.

Gradient descent trains shallow neural networks to approximate functions in 1D.

Study uses 1D-CNNs to forecast mortality in ELSA survey.

Study improves mortality prediction in ICU patients using feature engineering and 1D CNN.

Simple 1D-CNN network predicts electricity loads 36 hours ahead.

Develops a framework for 1D geometric field theories and proves they are equivalent to vector bundles.

Study on the complexity of 1D ReLU neural networks, proving growth in linear regions.

Study shows how 3+1D cosmologies can evolve to de Sitter space under certain conditions.

PAC-Bayes framework fails on simple 1D linear classification task.

Uniqueness of 1D bi-Schrödinger flow proven from flat torus to compact space.

Soil texture is important for many environmental processes. In this paper, we study the classification of soil texture based on hyperspectral data. We develop and implement three 1-dimensional (1D) convolutional neural networks (CNN): the LucasCNN, the LucasResNet which contains an identity block as residual network, a…

Supervised dimensionality reduction has emerged as an important theme in the last decade. Despite the plethora of models and formulations, there is a lack of a simple model which aims to project the set of patterns into a space defined by the classes (or categories). To this end, we set up a model in which each class i…

Estimates reliability of nuclear fuel using advanced modeling techniques.

Study shows inflation in 3+1D cosmologies with bounded scalar potential and specific symmetry.

Simulating fluid flow in geological formations requires mesh generation, lithology mapping to the cells, and computing geometric properties such as normal vectors and volume of cells. The purpose of this research work is to compute and process the geometrical information required for performing numerical simulations in…

Estimates latent positions in 1D torus from noisy pairwise affinities.

This study analyzes a non-orientable spacetime model in 1+1D quantum gravity.

Topological Quantum Field Theories (TQFTs) pertinent to some emergent low energy phenomena of condensed matter lattice models in 2+1 and 3+1D are explored. Many of our field theories are highly-interacting without free quadratic analogs. Some of our bosonic TQFTs can be regarded as the continuum field theory formulatio…

End-to-end CNN classifies environmental sounds from audio signals.

In this paper, we study the missing sample recovery problem using methods based on sparse approximation. In this regard, we investigate the algorithms used for solving the inverse problem associated with the restoration of missed samples of image signal. This problem is also known as inpainting in the context of image …

Study no-arbitrage conditions in 1D diffusion markets with interest rates.

Convolutional neural network (CNN) is a neural network that can make use of the internal structure of data such as the 2D structure of image data. This paper studies CNN on text categorization to exploit the 1D structure (namely, word order) of text data for accurate prediction. Instead of using low-dimensional word ve…

New method trains generative models without discriminators, improving stability and accuracy.

A simple block configures optimal kernel sizes for time series classification.

This paper proposes a novel kernel-based optimization scheme to handle tasks in the analysis, e.g., signal spectral estimation and single-channel source separation of 1D non-stationary oscillatory data. The key insight of our optimization scheme for reconstructing the time-frequency information is that when a nonparame…

Capsule networks improve temporal data understanding, achieving 96.21% ECG accuracy.

The paper proposes an ensemble of convolution-based methods for fault detection in gearboxes.

Paper proposes a faster RAE with sequence-aware encoding.

The author studies regions foliated by 1D families of functions and their applications.

This paper reconstructs equivalence classes in 1D neural networks.

Ridgeless ReLU networks interpolate datasets and extrapolate based on curvature signs.

Deep neural networks continue to show improved performance with increasing depth, an encouraging trend that implies an explosion in the possible permutations of network architectures and hyperparameters for which there is little intuitive guidance. To address this increasing complexity, we propose Evolutionary DEep Net…

Paper proposes a method to reduce hallucinations in diffusion models using Laplacian score sharpening.

A new method for efficient optimal partial transport in 1D.

Logistic Gaussian process (LGP) priors provide a flexible alternative for modelling unknown densities. The smoothness properties of the density estimates can be controlled through the prior covariance structure of the LGP, but the challenge is the analytically intractable inference. In this paper, we present approximat…

State-of-the-art speaker diarization systems utilize knowledge from external data, in the form of a pre-trained distance metric, to effectively determine relative speaker identities to unseen data. However, much of recent focus has been on choosing the appropriate feature extractor, ranging from pre-trained $i-$vectors…

New method for neural networks to predict histogram data.

A new method to compress CNNs without changing architectures.

A new tensor network method for image classification reduces computation cost.

DPI quantifies phase differences in 1D and multidimensional signals using Riesz transform.

CNN improves OSA diagnosis accuracy from PSG data.