High signal to noise ratio (SNR) consistency of model selection criteria in linear regression models has attracted a lot of attention recently. However, most of the existing literature on high SNR consistency deals with model order selection. Further, the limited literature available on the high SNR consistency of subs…
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arXiv research
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.
169,051 papers · 148 categories
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48 results for “SNR adaptation”
Improved WBP decoding with simple scaling and SNR adaptation.
problem Efficiently decoding weighted Tanner graphs with reduced complexity.
method Simple-scaling models with machine learning for edge weights, and parameter adapter networks.
result Simple scaling with few parameters can achieve near-maximum-likelihood performance.
DGPs with variational inference suffer from SNR issues that degrade gradient estimates, leading to unreliable training.
problem SNR issues in gradient estimates for DGPs with variational inference.
method Adapted doubly reparameterized gradient estimators for DGP training.
result Fix improves predictive performance of DGP models.
The interplay between computational efficiency and statistical accuracy in high-dimensional inference has drawn increasing attention in the literature. In this paper, we study computational and statistical boundaries for submatrix localization. Given one observation of (one or multiple non-overlapping) signal submatrix…
This paper proposes a simple adaptive sensing and group testing algorithm for sparse signal recovery. The algorithm, termed Compressive Adaptive Sense and Search (CASS), is shown to be near-optimal in that it succeeds at the lowest possible signal-to-noise-ratio (SNR) levels, improving on previous work in adaptive comp…
Efficient Training of Deep Classifiers for Wireless Source Identification using Test SNR Estimatescs.LG
Efficient deep learning for wireless source identification using test SNR estimates.
problem Training deep classifiers for wireless signal modulation and technology identification.
method Greedy training SNR Boosting and bootstrap aggregating (Bagging) based on training SNR values.
result Uniform improvement in accuracy across all SNR values with small subsets of training SNR values.
Recovering the support of sparse vectors in underdetermined linear regression models, \textit{aka}, compressive sensing is important in many signal processing applications. High SNR consistency (HSC), i.e., the ability of a support recovery technique to correctly identify the support with increasing signal to noise rat…
Improving scalability and stability of Stein discrepancies for scalable goodness-of-fit testing
problem Improving scalability and stability of Stein discrepancies for scalable goodness-of-fit testing
method Reformulating Stein discrepancy construction as an explicit SNR^2 maximisation problem
result Avoiding exponential SNR^2 collapse and achieving stable SNR^2
We study the community detection and recovery problem in partially-labeled stochastic block models (SBM). We develop a fast linearized message-passing algorithm to reconstruct labels for SBM (with nodes, blocks, intra and inter block connectivity) when proportion of node labels are revealed. The signa…
On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regressionstat.ML
EM algorithm achieves optimal sample complexity for learning two-component mixed linear regression.
problem Learning two-component mixed linear regression under varying signal-to-noise ratios.
method Analysis of EM algorithm convergence rates under different SNR regimes.
result EM algorithm achieves minimax optimal sample complexity in all SNR regimes.
When do Random Forests work?stat.ML
Random forest performance depends on SNR and covariate characteristics.
problem Understanding when random forests perform well.
method Systematic analysis of out-of-sample MSE for different SNR scenarios.
result Randomization effectiveness depends on SNR and covariate characteristics.
Optimizing over-the-air convex optimization, analog schemes are nearly optimal at low SNR.
problem Optimizing over-the-air convex optimization with coded gradients.
method Analyzes coded gradients over an additive Gaussian noise channel, considers analog coding schemes.
result Analog coding schemes nearly match the optimal convergence rate at low SNR, but a slowdown is inevitable.
The paper connects DNN generalization to node SNR using information theory.
problem Exploring the reasons behind DNN generalization performance.
method Using information theory, the paper derives SNR expressions for DNN nodes and uses them to quantify weight optimization.
result Good SNR performance in DNN nodes correlates with good generalization.
Tyler's M-estimator's phase transition at DS-SNR = 1 is resolved.
problem Robust Subspace Recovery
method Tyler's M-estimator
result TME converges exactly to the true subspace for DS-SNR >= 1 under a new stability condition.
This paper demonstrates two novel methods to estimate the global SNR of speech signals. In both methods, Deep Neural Network-Hidden Markov Model (DNN-HMM) acoustic model used in speech recognition systems is leveraged for the additional task of SNR estimation. In the first method, the entropy of the DNN-HMM output is c…
Bayesian approach improves AdaLoRA's performance and efficiency.
problem Improving the efficiency and performance of adaptive low-rank adaptation.
method Utilized Bayesian metrics and the Improved Variational Online Newton (IVON) optimizer for adaptive parameter budget allocation.
result Bayesian counterpart outperforms sensitivity-based importance metric and is faster than AdaLoRA.
Spectrum selectively trains LLMs based on SNR to save resources.
problem Efficiently training large language models with limited resources.
method Targeting layer modules based on SNR for selective training.
result Spectrum achieves similar performance to full fine-tuning but with reduced VRAM usage.
Indoor localization is a supporting technology for a broadening range of pervasive wireless applications. One promis- ing approach is to locate users with radio frequency fingerprints. However, its wide adoption in real-world systems is challenged by the time- and manpower-consuming site survey process, which builds a …
Random forests reduce bias and variance, especially in low SNR settings.
problem Reducing bias and variance in machine learning models, particularly in low SNR scenarios.
method Empirical study of random forests and bagging ensembles, focusing on the importance of tuning.
result Random forests reduce both bias and variance, outperforming bagging ensembles in high SNR settings.
KLT picker automates particle picking for cryo-EM, especially for low SNR images.
problem Challenges in particle picking, especially for low SNR micrographs.
method Data-driven optimal templates learned via Karhunen Loeve Transform.
result High-quality results with minimal manual effort on publicly available data sets.
In this paper, we propose a general framework for tensor singular value decomposition (tensor SVD), which focuses on the methodology and theory for extracting the hidden low-rank structure from high-dimensional tensor data. Comprehensive results are developed on both the statistical and computational limits for tensor …
New loss function improves accuracy of MRI parameter estimation.
problem Systematic errors in parameter estimates at low SNR.
method Developed and implemented negative log Rician likelihood (NLR) loss.
result NLR loss shows higher accuracy in parameter estimation than MSE loss at low SNR.
This paper analyzes AJIVE for estimating shared subspace across multiple datasets, revealing its strengths and limitations.
problem Estimating shared subspace across multiple datasets with varying degrees of misalignment.
method Angle-based Joint and Individual Variation Explained (AJIVE) method, a two-stage spectral approach.
result AJIVE's performance in high signal-to-noise ratio (SNR) regimes and its non-diminishing error in low-SNR settings.
The performance of a modulation classifier is highly sensitive to channel signal-to-noise ratio (SNR). In this paper, we focus on amplitude-phase modulations and propose a modulation classification framework based on centralized data fusion using multiple radios and the hybrid maximum likelihood (ML) approach. In order…
Improved predictive posterior density estimation through optimized importance sampling.
problem Low signal-to-noise ratio in posterior predictive densities.
method Optimized importance sampling using a test-time variational proxy.
result Significantly improved estimates of predictive posterior densities.
The problem of Poisson denoising appears in various imaging applications, such as low-light photography, medical imaging and microscopy. In cases of high SNR, several transformations exist so as to convert the Poisson noise into an additive i.i.d. Gaussian noise, for which many effective algorithms are available. Howev…
Point source detection at low signal-to-noise is challenging for astronomical surveys, particularly in radio interferometry images where the noise is correlated. Machine learning is a promising solution, allowing the development of algorithms tailored to specific telescope arrays and science cases. We present DeepSourc…
We propose a joint source and channel coding (JSCC) technique for wireless image transmission that does not rely on explicit codes for either compression or error correction; instead, it directly maps the image pixel values to the complex-valued channel input symbols. We parameterize the encoder and decoder functions b…
Improves diffusion models by controlling total variance and signal-to-noise-ratio.
problem Long sampling time in diffusion models.
method Total-Variance/Signal-to-Noise-Ratio (TV/SNR) disentangled framework.
result Improves generation performance by controlling TV and SNR independently.
Study recovers spike order in noisy tensor estimation without SNR assumptions.
problem Estimating multiple signal vectors from noisy tensor observations.
method Gradient flow optimization of a nonconvex function.
result Determines sample complexity for efficient permutation recovery.
New scheme optimizes BMI through probabilistic and geometric shaping.
problem Optimizing bit-wise mutual information (BMI) for coded modulation.
method Joint optimization of BMI through probabilistic and geometric shaping.
result Joint optimization enables a continuum of constellation geometries and probability distributions.
Obtaining magnetic resonance images (MRI) with high resolution and generating quantitative image-based biomarkers for assessing tissue biochemistry is crucial in clinical and research applications. How- ever, acquiring quantitative biomarkers requires high signal-to-noise ratio (SNR), which is at odds with high-resolut…
HKF uses neural networks to adapt Kalman filters for dynamic channel tracking.
problem Tracking channels with varying dynamics and Doppler values.
method Combines Kalman filters with hypernetworks for dynamic adaptation.
result HKF achieves up to 2dB gain over Kalman filters at high Doppler values.
New method clusters tensors with heteroskedastic noise.
problem Clustering tensors with varying noise levels.
method Two-stage method: subspace estimation followed by approximate -means.
result Proves exact clustering for SNR above computational limit.
Deep learning identifies wireless interference sources efficiently.
problem Interference source identification in 2.4 GHz ISM Band.
method Deep learning algorithms trained on 10 MHz band samples.
result CNN architecture reduces training time by 60% with minimal accuracy loss.
Method-of-Moments Inference for GLMs and Doubly Robust Functionals under Proportional Asymptoticsmath.ST
Paper develops methods for estimating GLMs and SNR under proportional asymptotics.
problem Estimation of regression coefficients and SNR in high-dimensional GLMs.
method Method-of-Moments type estimators that bypass nuisance function estimation.
result Consistent and asymptotically normal estimators derived for targets of inference.
EM converges for mixtures of many linear regressions with SNR > Ω(k).
problem Convergence of EM algorithm for mixtures of linear regressions.
method Analysis of EM algorithm convergence for mixtures of linear regressions with arbitrary number of components.
result EM converges to true parameters with SNR > Ω(k), independent of parameter norms.
Deep learning improves automatic modulation classification from subsampled data.
problem Automatic recognition of wireless communication signal modulations from imperfect data.
method Developed and tested Convolutional Long Short-term Deep Neural Network (CLDNN), LSTM, and ResNet architectures.
result Achieved high classification accuracy (around 90%) at high SNR with minimal training data.
A DL autoencoder tackles interference channels, improving SNR and INR.
problem Improving performance in interference channels with varying interference levels.
method Designing a DL neural network autoencoder for a k-user Gaussian interference channel, classifying interferences as weak to very strong.
result DL autoencoder significantly mitigates interference effects, especially with known α and low offset.
Optimal Variance Control of the Score Function Gradient Estimator for Importance Weighted Boundsstat.ML
Paper improves variance control in importance weighted variational bounds.
problem Improving the variance of gradient estimators for IWAE.
method Develops a novel control variate that grows SNR as √K for large K.
result Empirically, the method yields superior variance reduction for generative models.
Ridge regression analysis under varying sample size and dimensionality.
problem Prediction error analysis in asymptotic ridge regression.
method Characterization of prediction error based on covariance and parameter structure.
result Interpolation can be optimal even with bounded SNR if true parameter coefficients are larger on high-variance directions.
The paper proposes an efficient estimator for linear regression with shuffled labels.
problem Linear regression with shuffled labels, focusing on sensing results and corresponding information.
method A one-step estimator to reconstruct from and .
result Sufficient conditions for correct permutation recovery under different regimes of signal-to-noise ratio (snr).
Implicitly Normalized Online PCA: A Regularized Algorithm with Exact High-Dimensional Dynamicsstat.ML
Improved online PCA algorithm learns from evolving norm of parameter vector.
problem Discarding evolving norm in online PCA leads to suboptimal learning.
method Implicitly Normalized Online PCA (INO-PCA) removes unit-norm constraint.
result Parameter norm evolution leads to improved learning behavior.
The fundamental frequency (F0) represents pitch in speech that determines prosodic characteristics of speech and is needed in various tasks for speech analysis and synthesis. Despite decades of research on this topic, F0 estimation at low signal-to-noise ratios (SNRs) in unexpected noise conditions remains difficult. T…
SGD recovers multiple signal vectors in noisy tensor PCA.
problem Estimating multiple signal vectors from noisy tensor observations.
method Online stochastic gradient descent (SGD) in high dimensions with detailed analysis of correlations.
result Sequential elimination of correlations allows recovery of all spikes from samples.
SVO improves particle diversity and variational bounds in filtering SMC.
problem Improving variational bounds in particle filtering with limited samples.
method Introduces Particle Smoothing Variational Objectives (SVO) for smoothed approximate posterior through subsampling.
result SVO outperforms filtered objectives with fewer Monte Carlo samples on nonlinear systems.
We consider the problem of learning a dictionary matrix from a number of observed signals, which are assumed to be generated via a linear model with a common underlying dictionary. In particular, we derive lower bounds on the minimum achievable worst case mean squared error (MSE), regardless of computational complexity…
A new memory-efficient Adam variant reduces second moments when feasible.
problem Memory constraints in training machine learning models.
method Signal-to-Noise Ratio (SNR) analysis to identify dimensions where second moments can be replaced by means.
result Memory-efficient Adam variant (SlimAdam) matches performance and stability of Adam while saving up to 98% of second moments.