arXiv research

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

This paper reconstructs equivalence classes in 1D neural networks.

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

Improved robustness of 1D CNNs for heart arrhythmia classification.

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

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

A new deep learning benchmark reduces resource needs.

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

A new metric-based principal curve method learns 1D manifolds from spatial data.

We study \emph{TV regularization}, a widely used technique for eliciting structured sparsity. In particular, we propose efficient algorithms for computing prox-operators for $\ell_p$-norm TV. The most important among these is $\ell_1$-norm TV, for whose prox-operator we present a new geometric analysis which unveils a …

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

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

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.

Paper aims to find joint representation between vocal tract geometry and speech sound acoustics.

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…

Estimates reliability of nuclear fuel using advanced modeling techniques.

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

New gradient flows improve high-dimensional sampling.

The Madelung transform is known to relate Schrödinger-type equations in quantum mechanics and the Euler equations for barotropic-type fluids. We prove that, more generally, the Madelung transform is a Kähler map (i.e. a symplectomorphism and an isometry) between the space of wave functions and the cotangent bundle to t…

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

Capsule networks excel in understanding spatial relationships in 2D data for vision related tasks. Even though they are not designed to capture 1D temporal relationships, with TimeCaps we demonstrate that given the ability, capsule networks excel in understanding temporal relationships. To this end, we generate capsule…

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…

CEDA analyzes large categorical datasets using tree geometry and binary codes.

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

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…

Develops MENT for interpreting and detecting changes in network trajectories.

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

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 …

Paper proposes a faster RAE with sequence-aware encoding.

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…

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…

This paper shows the susceptibility of spectrogram-based audio classifiers to adversarial attacks and the transferability of such attacks to audio waveforms. Some commonly used adversarial attacks to images have been applied to Mel-frequency and short-time Fourier transform spectrograms, and such perturbed spectrograms…

Self attention mechanisms have become a key building block in many state-of-the-art language understanding models. In this paper, we show that the self attention operator can be formulated in terms of 1x1 convolution operations. Following this observation, we propose several novel operators: First, we introduce a 2D ve…

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…

CNN improves OSA diagnosis accuracy from PSG data.

NNs accurately predict energy eigenvalues and other physical phenomena in 1D quantum mechanics.

New method uses neural networks to interpolate stellar atmospheres with high precision.

Paper models Bitcoin market dynamics using 1+1D field theory.

SCORE technique reduces BO's high-dimensional search costs.

3D topological order linked to Seifert manifolds and gauge groups.