A new method learns high-frequency components for better image reconstruction.
problem Efficiently reconstructing feature details in under-sampled imaging.
method Proposes HF-DAEP, a denoising autoencoder using multi-profile high-frequency components.
result Demonstrates improved reconstruction of feature details in MRI and CT.
The paper introduces a method for interpretable principal component analysis of high-dimensional time series.
problem Inconsistent and difficult-to-interpret principal component estimates in high-dimensional regimes.
method Localized sparse principal component analysis of spectral density matrices in frequency domain.
result Efficient algorithm for sparse-localized estimates of principal subspaces.
New Fourier-based diffusion model improves high-frequency generation quality.
problem Diffusion models struggle with high-frequency details.
method Analyzed and modified the forward process in Fourier space to equalize noise corruption across frequencies.
result Improved generation quality for high-frequency components.
Low frequency perturbations improve model robustness, contrary to high frequency attacks.
problem Improving model robustness against adversarial attacks.
method Systematic control of frequency components in perturbations.
result Low frequency perturbations improve model robustness, especially in white-box and black-box settings.
Using recent advances in the econometrics literature, we disentangle from high frequency observations on the transaction prices of a large sample of NYSE stocks a fundamental component and a microstructure noise component. We then relate these statistical measurements of market microstructure noise to observable charac…
FMMNN combines sine activations with multi-component, multi-layer structure for high-frequency function approximation.
problem Effective representation and learning of high-frequency features in neural networks.
method Introduces FMMNN with sine-type activations and multi-component, multi-layer structure.
result FMMNN achieves strong accuracy and favorable convergence on oscillatory function-approximation benchmarks.
Novel method calculates complex correlation for high-frequency financial data.
problem Analyzing lead-lag relations in high-frequency financial data.
method Hilbert transform based complex correlation for unevenly spaced data.
result Identifies market components with small but significant delays.
This work analyzes how frequency components affect CNN predictions and robustness.
problem Lack of frequency-based explanation for CNNs leading to vulnerabilities.
method Frequency component analysis and quantification of their contribution to CNN predictions.
result Adversarial attacks exploit high-frequency features, while robustness comes from low-frequency associations.
SPGD improves adversarial training efficiency and accuracy.
problem Improving adversarial training efficiency and accuracy with fewer steps.
method Adversarial-sample generation from a frequency domain perspective, extending PGD to the frequency domain.
result SPGD achieves greater adversarial accuracy compared to PGD with fewer attack steps.
Study examines implied volatility smiles around jumps in high-frequency S&P500 index data.
problem Understanding implied volatility smiles around market jumps.
method High-frequency analysis of SPX S&P500 index option data using principal components.
result Volatility smiles exhibit abnormal properties around jumps, independent of maturity and option type.
DNNs initially capture low-frequency components before high-frequency ones, a phenomenon called F-Principle.
problem Understanding why DNNs generalize well despite overfitting.
method Empirical study on real and synthetic datasets, focusing on frequency components captured by DNNs.
result DNNs capture dominant low-frequency components first, then high-frequency ones, a phenomenon called F-Principle.
HyFAD improves time series imputation by combining time and frequency diffusion.
problem Improve time series imputation by handling frequency-sensitive denoising and balancing global and local dynamics.
method HyFAD is a hybrid time-frequency diffusion model with frequency-aware embedding, built on DDPM paradigm.
result HyFAD achieves state-of-the-art performance in time series imputation.
SSMs have a built-in bias towards low-frequency components, which can be adjusted.
problem Frequency bias in SSMs affects their performance on long-range sequences.
method Proposed two mechanisms to tune frequency bias: scaling initialization or applying a Sobolev-norm-based filter.
result Tuning frequency bias improves SSMs' performance on long-range sequence learning tasks.
Deeper neural networks learn lower frequency functions faster, according to a new principle.
problem Understanding why deeper learning is faster.
method Fourier analysis and filtering method to separate and analyze the frequency distribution of neural network outputs.
result Deeper hidden layers in neural networks bias towards lower frequency functions during training.
Generative adversarial networks improve speech synthesis from MFCCs.
problem Synthesizing speech from MFCCs, which are typically unusable for synthesis.
method Predict fundamental frequency and voicing from MFCCs, convert spectral envelope to filters, train excitation model, add noise.
result High quality speech can be reconstructed from MFCCs alone.
Deep neural networks are biased towards low frequencies, affecting global behavior.
problem Understanding the limitations of neural networks in capturing high-frequency patterns.
method Using Fourier analysis, the study examines the spectral bias of neural networks and their expressivity.
result Deep ReLU networks are biased towards low frequency functions, making it difficult to capture local fluctuations.
New DMEM models forecast volatility combining low- and high-frequency data.
problem Modeling realized volatility with both short- and long-term features.
method Doubly Multiplicative Error (DMEM) models combining daily and long-term data.
result DMEM models outperform existing GARCH-type models in forecasting.
The paper clusters stocks using high-frequency NSE data, identifying IT and banking sectors.
problem Describing joint behavior of stocks beyond regression and correlation.
method Applied Kernel Principal Component Analysis (KPCA) and Functional Principal Component Analysis (FPCA) to high-frequency data.
result Identified two prominent clusters: IT and banking sectors, with smaller clusters from automobile and energy sectors.
Robustly detects jumps in high-frequency CIR and CKLS models.
problem Jump detection in high-frequency jump-diffusion processes.
method MDPDE-based robust estimators for drift and diffusion coefficients.
result Maximum of normalized residuals converges to Gumbel distribution.
MSNet uses high frequency residual learning for efficient multi-scale image classification.
problem Efficient multi-scale image classification for mobile and embedded devices.
method Two network architecture: low resolution for low frequency, high resolution for high frequency residuals.
result MSNet achieves significant accuracy improvements over different base networks.
A new WNN framework selects wavelet bases for efficient learning.
problem Challenges in constructing accurate wavelet bases and high computational costs in WNN.
method Introduces a constructive WNN that selects initial bases and trains functions by introducing new bases for predefined accuracy while reducing computational costs.
result Significantly improves computational efficiency through a frequency estimator and wavelet-basis increase mechanism.
In this paper we will try to assess the multifractality displayed by the high-frequency returns of Madrid's Stock Exchange IBEX35 index. A Multifractal Detrended Fluctuation Analysis shows that this index has a wide singularity spectrum which is most likely caused by its long memory. Our findings also show that this lo…
Post-averaging improves neural network robustness against adversarial attacks.
problem Adversarial attacks on neural networks.
method Post-averaging technique to smooth high frequency components.
result Post-averaging method successfully defends over 95% of adversarial samples without significant performance degradation.
The paper proposes a mixed-frequency quantile regression model for VaR and ES forecasting.
problem Forecasting VaR and ES with mixed-frequency data.
method Mixed-frequency quantile regression model to estimate VaR and ES.
result The proposed model outperforms other models in VaR and ES backtesting tests.
GNIs induce a regulariser that penalizes high-frequency components in neural network activations.
problem Understanding the regularizing effect of Gaussian noise injections on neural network activations.
method Deriving the explicit regularizer by marginalizing out injected noise and analyzing its effect in the Fourier domain.
result GNIs induce a regularizer that produces calibrated classifiers with large margins.
Calibrates carbon futures option pricing using high-frequency data.
problem Estimating equity and variance risk premia for carbon futures options.
method Multifactor stochastic volatility framework with jumps, employing indirect inference.
result Provides insights into carbon futures and option dynamics.
A new search-control strategy improves Dyna's efficiency.
problem Improving sample efficiency in model-based reinforcement learning.
method Proposes a novel search-control strategy by sampling high frequency regions of the value function.
result Empirically shows that high frequency regions require more samples to approximate, suggesting a better search-control strategy.
New method constrains CNN filter frequencies to improve robustness.
problem CNN bias towards low frequency components, leading to poor performance in scenario transformations.
method Frequency domain regularization by constraining filter spectra, training valid frequency range end-to-end.
result Demonstrated effectiveness in defending adversarial perturbations, reducing generalization gap, and improving transfer learning.
A new neural network improves frequency estimation from noisy signals.
problem Estimating frequencies of sinusoidal components in noisy signals.
method A novel neural network architecture combined with a module to detect the number of frequencies.
result Significantly more accurate frequency estimation at medium-to-high noise levels.
FCN model enhances speech by processing raw waveforms, outperforming existing methods.
problem Restoring high frequency components in speech enhancement.
method End-to-end fully convolutional network (FCN) model for raw waveform-based speech enhancement.
result FCN model outperforms DNN and CNN in terms of intelligibility and quality metrics.
High-dimensional inference for sparse spectral precision matrices
problem Inference on the spectral precision matrix at a fixed frequency
method Full likelihood-based inference using neighboring discrete Fourier transforms
result Simultaneous control of regularization, finite-sample truncation, and smoothing biases
A new model forecasts optimal portfolio weights from high-frequency data.
problem Forecasting optimal portfolio weights from high-frequency data.
method Dynamic Conditional Weights (DCW) model for portfolio weights dynamics.
result DCW model outperforms other models in portfolio allocations and measures.
FRA-Attack improves adversarial transferability for closed-source MLLMs by aligning visual focus across models.
problem Improving adversarial transferability for closed-source MLLMs, especially with high accuracy.
method Unified frequency-domain regularization approach: high-pass DCT objective for feature alignment and Frequency-domain Gradient Regularization (FGR) for gradient optimization.
result FRA-Attack achieves superior cross-model transferability, especially on GPT-5.4, Claude-Opus-4.6, and Gemini-3-flash.
Paper proposes a risk index combining frequency and severity of abnormal driving patterns.
problem Assessing driver risk based on telematics data.
method Combines frequency of abnormal driving patterns with severity quantified through tail rarity.
result Developed a risk index that enables reliable discrimination and ranking of drivers.
A new model captures irregularly spaced high-frequency prices and their volatility.
problem Modeling high-frequency prices with irregular spacing and market noise.
method Observation-driven model using Skellam distribution with time-varying volatility and smoothing splines.
result The model provides a good fit to IBM stock data and measures daily realized volatility.
Bayesian method corrects misspecified volatility estimation in high-frequency financial data.
problem Volatility estimation in financial data with infinite jump activity and microstructure noise.
method Proposes a misspecified posterior corrected by a simple estimate of the location shift and re-scaling of the log likelihood.
result Establishes a Bernstein-von Mises theorem for the adjusted posterior, showing asymptotic Gaussianity and consistent estimation.
Hybrid model combines VAR and neural network for OFI prediction.
problem Accurate prediction of Order Flow Imbalance (OFI) in high frequency trading.
method Combines Vector Auto Regression (VAR) and a simple feedforward neural network (FNN).
result Hybrid model achieves superior predictive accuracy compared to standalone models.
Band-limited training reduces resource usage without sacrificing accuracy.
problem Resource constraints in training Convolutional Neural Networks (CNNs).
method Artificially constraining the frequency spectra of convolutional filters during training.
result CNNs can leverage lower-frequency components effectively, reducing resource usage.
Deep neural networks can generalize by reducing high-frequency noise over time, not always following a monotonic learning bias.
problem Understanding the learning dynamics and generalization of over-parameterized DNNs.
method Experimental analysis of deep double descent, focusing on the spectral bias of DNNs.
result The high-frequency components of DNNs diminish over training, leading to a second descent in test error.
Estimates graph process with high-frequency data, proving asymptotic properties.
problem Estimating graph process with high-frequency data.
method Discretized maximum likelihood estimators for GrOU process under high-frequency sampling.
result Asymptotic central limit theorems for estimators under finite and infinite jump activity.
Paper proposes a new loss function to improve image reconstruction quality.
problem Blurred images when using pixel loss for convolutional autoencoders.
method Introduces spatial frequency loss (SFL) to mitigate blurring.
result Reduced blurs in reconstructed images using SFL.
Study examines cryptocurrency volatility factors using high-frequency data.
problem Understanding factors affecting cryptocurrency volatility.
method High-frequency panel data analysis of 2020-2022, comparing to equity benchmarks.
result Positive market returns and volatility drivers impact cryptocurrency volatility.
FredNormer improves time series forecasting by adapting to frequency domain patterns.
problem Current normalization methods struggle with non-stationary time series due to their time-domain approach.
method FredNormer analyzes frequency components, adapts weights, and improves robustness.
result FredNormer boosts forecasting accuracy by 33.3% on ETTm2 dataset.
This paper models CSI 300 index volatility using machine learning and addresses jump prediction.
problem Volatility modeling and jump prediction for high-frequency CSI 300 index data.
method Generalized Barndorff-Nielsen and Shephard model with machine learning algorithms for parameter estimation and forecast evaluation.
result Deterministic component of stochastic volatility processes can be captured over short and longer-term windows.
Study predicts intraday stock trading volume using ML models.
problem Predicting intraday trading volumes in equity markets.
method Used machine learning models with HF predictors.
result Intraday stock trading volume is highly predictable.
Study uses multi-kernel Hawkes models to analyze high-frequency price dynamics.
problem Understanding responsive speeds of market participants in high-frequency trading.
method Multi-kernel Hawkes models with conditional Hessian analysis for optimization.
result Existence of multi-kernels (UHF, VHF, HF) in high-frequency price dynamics.
Fuzzy clustering reduces text data dimensions effectively.
problem Sparsity and high dimensionality in document-term frequency matrices.
method Fuzzy clustering applied as a new unsupervised feature transformation method.
result Fuzzy clustering outperforms PCA and SVD in reducing text data dimensions.
The paper explains how neural networks learn less salient frequency components during training.
problem Understanding the grokking phenomenon in neural networks.
method Empirical frequency analysis of training data.
result Neural networks initially learn less salient frequency components of the test data.