CNN-RNNs detect bird sounds with high accuracy.
problem Automated detection of bird sounds in varied environments.
method Convolutional Recurrent Neural Networks (CNN-RNNs) for feature extraction and dependency capture.
result 88.5% AUC score on unseen data.
PhaseDNN speeds up learning of high-dimensional functions across wide frequencies.
problem Training high-dimensional functions at wide frequencies is slow and inefficient.
method Parallel DNNs with frequency-specific training and phase shifts.
result PhaseDNN achieves uniform learning across wide frequencies.
FreSh shifts model's initial frequency spectrum to match target signal, improving neural representation performance.
problem MLPs' low-frequency bias limits capturing high-frequency details accurately.
method FreSh selects embedding hyperparameters to align model's initial output spectrum with target signal's spectrum.
result FreSh improves performance across various neural representation methods and tasks with minimal computational overhead.
A new model of V1 using orientation, frequency, and phase.
problem Understanding the complex behavior of V1 simple cells.
method Developed a sub-Riemannian model based on Gabor functions.
result The model enhances images using orientation, frequency, and phase.
New method improves music transcription by treating frequency distributions holistically.
problem Small frequency shifts and variations in sound timbre harm traditional fit measures.
method Optimal transportation and new holistic frequency distribution measure.
result Simplified note templates lead to faster, state-of-the-art performance.
Paper improves music transcription models with invariance and data augmentation.
problem Improving accuracy of frame-based music transcription models.
method Translation-invariant network combining filterbank and CNN, trained with pitch-shift augmented data.
result Top-performing model in MIREX evaluation, reducing model complexity and avoiding overfitting.
MCLNN improves sound event recognition with fewer parameters.
problem Improving sound event recognition with deep neural networks.
method Developed MCLNN to enforce sparseness and frequency shift invariance.
result MCLNN achieved competitive performance with 12% fewer parameters.
Local convolutions bias neural networks towards high-frequency adversarial examples.
problem High-frequency adversarial examples in neural networks.
method Analysis of different linear and nonlinear architectures, focusing on the impact of local convolution operations.
result Local convolutions induce an implicit bias towards high frequency features, leading to high-frequency adversarial examples.
FSL-Net detects and localizes feature shifts in large, high-dimensional datasets.
problem Feature shifts between data sources lead to erroneous features in various applications.
method FSL-Net is a neural network trained on multiple datasets to localize feature shifts.
result FSL-Net accurately localizes feature shifts from unseen datasets without re-training.
Paper extends SI method for detecting CPs in complex systems' frequency domain.
problem Identifying change points in complex systems' frequency domain.
method Extends SI framework to frequency domain using DFT properties and develops valid p-values.
result Reliable detection of genuine CPs with strong statistical guarantees.
New method calibrates models under covariate shifts.
problem Calibration of models can be lost under covariate shifts.
method Importance sampling based approach.
result Efficacy demonstrated on real-world and synthetic datasets.
A new convolution method stabilizes GANs by learning coarse structures first.
problem Mode collapse in GANs during training.
method Soft octave convolutions that split filters into high and low frequency parts, shifting weight updates.
result Reduces mode collapse and artifacts in generated images.
Using high-frequency time series of stock prices and share volumes sizes from January 2002-May 2009, this paper investigates whether the effects of the onset of high-frequency trading, most prominent since 2005, are apparent in the dynamics of the dollar traded volume. Indeed it is found in almost all of 14 heavily tra…
New moves transform any virtual knot to a trivial knot.
problem Transforming virtual knots to trivial knots.
method Introducing arc shift and region arc shift moves.
result Any virtual knot can be transformed into a trivial knot using these moves.
MCLNN improves music genre classification by learning frequency bands.
problem Classifying music genres using neural networks adapted from image recognition.
method MCLNN learns frequency bands, reducing susceptibility to frequency shifts and enabling concurrent exploration of feature combinations.
result MCLNN outperforms state-of-the-art Convolutional Neural Networks on the Ballroom music dataset.
Analyzes branch points of area-minimizing currents with non-2 planar frequency.
problem Understanding the structure of area-minimizing currents near branch points.
method Intrinsic frequency function and geometric arguments avoiding center manifolds.
result Establishes higher order asymptotics and topological control near branch points.
A new method to estimate local volatility from high-frequency data.
problem Quantitative trading risk management needs a better way to estimate volatility.
method Realized local volatility surface estimated via high-frequency data and Bayesian nonparametric estimation.
result The method can capture counterfactual volatility and improve risk management.
The paper detects special epochs in oil price data using wavelet analysis and multi-fractional modeling.
problem Detecting regime shifts in oil price data.
method Wavelet-based decomposition, multi-fractional modeling, and joint estimation of Hurst exponent and volatility.
result Special epochs emerge as a result of regime shifts and switching in oil price data.
Defines projective limits of local shift morphisms and applies to KdV equation.
problem Defining and studying projective limits of local shift morphisms.
method Introducing projective limits, defining shift Poisson tensors, and applying to KdV equation.
result Illustrates the application of projective limits to the KdV equation on the circle.
The paper addresses instability in CNNs' first layer by proving max pooling's shift invariance.
problem Instability in CNNs' first layer, leading to sensitivity to small input shifts.
method Establishing conditions for max pooling's shift invariance and deriving a measure of stability.
result Max pooling approximates a nearly shift-invariant complex modulus under certain conditions.
Alternative wavelet analysis method for financial signals.
problem Analyzing oscillations in financial signals with noise.
method Modeling financial signals as isolated events producing ripples of various frequencies.
result Element analysis distinguishes between noise and logically matched generators.
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.
Efficient clustering in high dimensions with Quick Shift and LSH.
problem Density-based clustering in high-dimensional data.
method Combines Quick Shift and LSH for efficient density estimation.
result Achieves almost linear time complexity for consistency.
Minimal token perturbations reveal how Transformer models process information.
problem Understanding information propagation in Transformer models for interpretability.
method Study of minimal token perturbations on embedding space.
result Rare tokens cause larger shifts, and input information mixes deeper.
Robust detection of transients and onset zones in noisy signals.
problem Detecting transients and onset zones in noisy signals robustly.
method Sparse and invariant representation based on wavelet transforms and scattering network.
result Frequency invariance improves robustness of transient detection.
The paper develops methods to estimate frequencies in large discrete data sets with improved coverage and robustness.
problem Estimating frequencies in large, discrete data sets with valid coverage and robustness.
method Conformal inference methods using discrete sketches, marginal coverage for queries, and novel conformal calibration.
result Improved empirical performance compared to existing methods in simulations and real data.
We analyze the structure of the \emph{frequency space} Q(F) of a nonabelian free group F=F(a1,...,ak) consisting of all shift-invariant Borel probability measures on ∂F and construct a natural action of Out(F) on Q(F). In particular we prove that for any outer automorphism φ of F the \emph{conju…
DSLOB creates synthetic LOB data for benchmarking forecasting algorithms under distributional shifts.
problem Challenges in dealing with out-of-distribution limit order book data.
method Multi-agent market simulator to create labeled synthetic LOB dataset with and without market stress.
result Demonstrates the need for robust forecasting algorithms to handle distributional shifts.
High frequency limit for most of wave phenomena is known as quasiclassical limit or ray optics limit. Propagation of waves in this limit is described in terms of wave fronts and rays. Wave front is a surface of constant phase whose points are moving along rays. As it appears, their motion can be described by Hamilton e…
Adaptive method for prediction sets under changing data distributions.
problem Forming prediction sets in an online setting with varying data distributions.
method Adaptive conformal inference that re-estimates the distribution shift parameter over time.
result Adaptive method achieves desired coverage frequency over long-time intervals.
Typically, operational risk losses are reported above some threshold. This paper studies the impact of ignoring data truncation on the 0.999 quantile of the annual loss distribution for operational risk for a broad range of distribution parameters and truncation levels. Loss frequency and severity are modelled by the P…
Proposes a new method to improve CNNs' shift invariance and accuracy.
problem Improving CNNs' shift invariance and prediction accuracy.
method Replaces RMax with CMod, a Gabor-like structure, to increase shift invariance and accuracy.
result Achieves superior accuracy on ImageNet and CIFAR-10 classification tasks.
Framework improves classifier calibration under differential privacy for domain shift.
problem Improving classifier calibration under domain shift with privacy constraints.
method Differential privacy framework for adapting recalibration algorithms.
result Novel accuracy temperature scaling algorithm outperforms existing methods on private datasets.
This work uses adversarial learning to detect and correct feature shifts in various datasets.
problem Detecting and correcting feature shifts in real-world datasets.
method Adversarial learning applied to multiple discriminators to detect and correct feature shifts.
result Mainstream classifiers can effectively localize and correct feature shifts, outperforming existing techniques.
We generalize the recently proposed quantum model for the stock market by Zhang and Huang to make it consistent with the discrete nature of the stock price. In this formalism, the price of the stock and its trend satisfy the generalized uncertainty relation and the corresponding generalized Hamiltonian contains an addi…
Convolutional neural networks learn phase-dependent frequency representations.
problem Capturing phase dependence in frequency representations for better signal analysis.
method Convolutional neural networks learn filters with different phases, which rectify to phase-dependent descriptors.
result Phase harmonics correlations can compressively represent signals with sparse wavelet coefficients.
MCLNN improves music genre classification with automated feature exploration.
problem Music genre classification using neural networks.
method MCLNN uses a mask to enforce sparseness and learn time-frequency representations.
result MCLNN achieves competitive accuracy compared to state-of-the-art methods.
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.
This paper improves conformal prediction for robust interval estimation under distribution shifts.
problem Robustness of conformal prediction under distribution shifts.
method Modeling distribution shifts using Levy-Prokhorov (LP) ambiguity sets, which capture both local and global perturbations.
result Constructs robust conformal prediction intervals that remain valid under distribution shifts.
Continues work on derived manifolds and symplectic schemes, constructing virtual classes.
problem Constructing virtual fundamental classes for derived manifolds and schemes.
method Cosection localization, reduced virtual fundamental classes, and applications to Donaldson-Thomas theory.
result Virtual fundamental classes for (−2)-shifted symplectic derived schemes are consistent with algebraic and differential geometric constructions. We introduce the functional mean-shift algorithm, an iterative algorithm for estimating the local modes of a surrogate density from functional data. We show that the algorithm can be used for cluster analysis of functional data. We propose a test based on the bootstrap for the significance of the estimated local modes …
New insights into SGD and generalization via shift-curvature and bias-curvature mechanisms.
problem Understanding the role of curvature in generalization and how SGD affects it.
method Derivation of new SGD steady-state distribution and analysis of shift-curvature and bias-curvature mechanisms.
result Shift-curvature is a significant factor in test performance, especially for small SGD noise.
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.
Study tail risk in high-frequency finance using L1-regularized regression.
problem Measuring tail risk dynamics in high-frequency financial markets.
method Dynamic extreme value regression model with L1-regularized maximum likelihood estimator. result Severity of extreme losses well predicted by low price impact in high volatility periods.
Modified Epanechnikov Mean Shift converges to cluster centroids.
problem Lack of theoretical support for convergence of Epanechnikov Mean Shift due to non-smooth kernel density functions.
method Proposed a simple remedy to fix convergence issues, ensuring termination at a local maximum of the estimated density.
result Modified Epanechnikov Mean Shift guarantees convergence to a cluster centroid within a finite number of iterations.
The basic model for high-frequency data in finance is considered, where an efficient price process is observed under microstructure noise. It is shown that this nonparametric model is in Le Cam's sense asymptotically equivalent to a Gaussian shift experiment in terms of the square root of the volatility function σ. A…
Detects which features have shifted in data distributions.
problem Identifying which specific features have caused a distribution shift.
method Formalizes the problem as multiple conditional distribution hypothesis tests, proposes non-parametric and parametric statistical tests, and uses a test statistic based on the density model score function.
result Demonstrates methods for identifying when and where a shift occurs in multivariate time-series data.
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.