New algorithms improve signal processing in federated learning.
problem Efficiently process distributed signal samples with privacy and communication constraints.
method Proposes overpredictive signal approximations using convex optimization.
result Quantifies tradeoffs between communication cost, sampling rate, and approximation error.
Improved extreme learning machines for graph signal regression.
problem Regression tasks with graph signals and limited/noisy data.
method Graph signal processing regularization for smoothness.
result Regularization improves prediction accuracy with limited data.
This work uses encoder-decoder networks to denoise one-dimensional signals by aligning clean and noisy signal latent representations.
problem Noise removal in one-dimensional signals, especially in medical and motion signals.
method Encoder-decoder architecture with adversarial learning to align clean and noisy signal latent representations.
result Better performance on electrocardiogram and motion signal denoising compared to learning-based and non-learning approaches.
Proposes a novel graph signal model using narrowband kernels.
problem Graph signals with multiple concentrated frequency regions.
method Jointly learns graph signal model parameters and coefficients.
result Joint learning improves signal interpolation accuracy.
Unified deep learning for graph signals, simplifying existing models.
problem Efficiency of Convolutional Neural Networks on graph signals.
method Unified formalism for existing deep learning models on graph signals.
result Unified formalism simplifies and compares existing models.
Paper automates substation signal matching using machine learning.
problem Manual matching of customer data with substation signal names.
method Bagged token classifier that votes on signal names based on customer data.
result The method outperforms standard classifiers in accuracy and efficiency.
Research compares ML and Time Series methods for generating trading signals.
problem Efficiency of on-line learning Algorithms in generating trading signals.
method Used technical indicators and ensemble of Random Forests, also Kalman Filter.
result Kalman Filter outperformed Random Forests in on-line learning predictions of stock prices.
Deep learning models detect nanopore translocation events with high accuracy.
problem Manual parameter selection for nanopore signal analysis is prone to error.
method Developed a synthetic signal generator for training ML models.
result Deep learning models achieve over 99% true event detection.
Paper learns hypergraph structures from signals with smoothness priors.
problem Learning hypergraph structures from signals with high-order relationships.
method Proposes HGSL framework with dual smoothness prior to map signals to hypergraph structure.
result HGSL efficiently infers meaningful hypergraph topologies from signals.
Proposes a novel graph learning framework for robust graph topology learning from graph signals.
problem Graph learning for revealing node relationships in data entities.
method Functional learning with smoothness-promoting graph learning, incorporating Kronecker product kernel.
result Improves robustness against missing and incomplete information in graph signals.
GAN-based spoofing attacks improve wireless signal authentication.
problem Improving wireless signal authentication against sophisticated spoofing attacks.
method Generative Adversarial Network (GAN) for generating synthetic signals.
result GAN-based spoofing attacks significantly increase the success probability of wireless signal spoofing.
DeepCodec learns to take undersampled measurements and recover signals using deep neural networks.
problem Signal recovery from undersampled data.
method Adaptive deep convolutional neural networks for sensing and recovery.
result DeepCodec outperforms traditional ℓ1-minimization in signal recovery. Proposes a new dictionary learning method for high-dimensional graph signals.
problem Challenges of traditional sparse representation methods in high-dimensional graph signals.
method Integrates graph topology implicitly through sparse combinations of graph-wavelet functions and explicitly through graph constraints.
result Demonstrates effectiveness in high-dimensional graph signal processing.
Graph signal sampling improved using reinforcement learning.
problem Efficiently sampling and recovering graph signals with clustering.
method Formulated as a multi-armed bandit problem, optimized using gradient ascent.
result Sampling strategies based on gradient MAB outperform existing methods.
Machine learning detects foreign stock market signals for U.S. companies.
problem Detecting value-relevant foreign information for U.S. companies.
method Training over 100,000 models to capture stock-specific relationships.
result Foreign signals predict U.S. stock returns, especially in emerging markets.
Deep invertible networks decode EEG signals better than chance.
problem Decoding brain signals from EEG data.
method Deep invertible networks for generating and classifying brain signals.
result Deep invertible networks generate realistic EEG signals and classify novel signals above chance.
Generative adversarial network improves signal reconstruction from magnitude spectrograms.
problem Reconstructing a time-domain signal from a magnitude spectrogram.
method Deep neural network and generative adversarial network approach.
result Our method reconstructs signals faster with higher quality than the Griffin-Lim method.
In sparse signal representation, the choice of a dictionary often involves a tradeoff between two desirable properties -- the ability to adapt to specific signal data and a fast implementation of the dictionary. To sparsely represent signals residing on weighted graphs, an additional design challenge is to incorporate …
Semi-supervised learning identifies radio signals from sparse data.
problem Lack of labeled data for radio emitter recognition.
method Combines unsupervised and supervised learning for feature learning and clustering.
result Semi-supervised learning can identify new radio signals efficiently.
Missing data reduces signal-to-noise ratio, not sample size, for PCA.
problem Effect of missing data on PCA signal structure learning.
method Analytic and simulation studies of probabilistic PCA with missing data.
result Missing data effectively reduces signal-to-noise ratio, not sample size.
The construction of a meaningful graph plays a crucial role in the success of many graph-based representations and algorithms for handling structured data, especially in the emerging field of graph signal processing. However, a meaningful graph is not always readily available from the data, nor easy to define depending…
DeepRec uses deep learning to recover signals from one-bit measurements.
problem Signal recovery from one-bit noisy measurements.
method Deep unfolding of inference optimization into deep neural network layers.
result DeepRec improves accuracy and computational efficiency.
Paper solves the chicken-and-egg problem in unsupervised learning of signal models.
problem Learning signal models from incomplete data when the model is unknown.
method Necessary and sufficient sensing conditions for learning signal models from multiple measurement operators or group invariance.
result Agrees with the fundamental limitations of learning from incomplete data.
New neural network learns like humans without backpropagation.
problem Deep learning without backward error propagation.
method Biologically inspired feedforward supervisory signal.
result Effective learning from large amounts of feedforward information.
Survey on modern traffic signal control methods.
problem Minimizing travel time at road intersections.
method Machine learning, reinforcement learning.
result Promotes interdisciplinary research on traffic signal control.
This paper shows how learning the phase-amplitude coupling improves bio-signal classification.
problem Discarding phase component in bio-signal feature extraction leads to poor generalization.
method Introducing a novel self-supervised learning task called Phase-Swap to detect phase-amplitude coupling.
result Neural networks trained on Phase-Swap task generalize better across subjects and recording sessions.
Learn graph structure from data using signal processing methods.
problem Constructing meaningful graph topologies from data.
method Survey of classical and GSP-based graph inference methods.
result Advantages of GSP-based methods in various scenarios.
Machine learning predicts signaling peptides from protein star graphs.
problem Predicting signaling activity of proteins from molecular structure.
method Protein star graphs, S2SNet topological indices, Machine Learning (SVM-RFE, Laplacian kernel).
result Best model predicts 98.0% signaling pathways with AUROC 0.961.
Generalizes PCA and ICA for continuous-time signals using neural networks.
problem Low-rank decomposition of continuous-time vector-valued signals.
method Implicit neural network framework to learn numerical approximations of PCA and ICA.
result Unified approach to PCA and ICA in continuous domain, enforcing decorrelation and independence.
In this paper, we develop a new framework for sensing and recovering structured signals. In contrast to compressive sensing (CS) systems that employ linear measurements, sparse representations, and computationally complex convex/greedy algorithms, we introduce a deep learning framework that supports both linear and mil…
Paper reveals hidden convexities in deep learning models using sparse signal processing.
problem Non-convex loss functions in deep learning models complicate optimization and theoretical understanding.
method Developed convex equivalences of ReLU NNs and their connections to sparse signal processing models.
result Recent research has uncovered hidden convexities in certain NN architectures, notably two-layer ReLU networks and other architectures.
The paper infers graph structure from sparse signal observations.
problem Inferring graph structure from sparse signal observations.
method Formulates a non-convex graph learning problem and solves it via alternating signal sparse coding and graph update steps.
result The method generally outperforms other network inference algorithms in graph recovery.
Enhances reinforcement learning with partial state information.
problem Improving learning under partial observability with limited privileged signals.
method Introduced informed asymmetric actor-critic framework that uses arbitrary state-dependent privileged signals.
result Unbiased policy gradient estimates with arbitrary privileged signals.
Optimizes PnL using linear signals in quantitative finance.
problem Maximizing profit and loss in financial trading.
method Unsupervised machine learning approach that maximizes Sharpe Ratio through linear relationships and parameter optimization.
result Empirical validation and effectiveness of the model on U.S. Treasury ETF.
Novel Bayesian meta-reinforcement learning framework improves traffic signal control robustness.
problem Lack of robustness and stability in adaptation for traffic signal control.
method Value-based Bayesian meta-reinforcement learning framework BM-DQN with fast-adaptation variation and DQN fast-update advantage.
result Framework adapts more quickly and robustly to new scenarios than previous methods.
EEG signals enhance speaker verification system robustness.
problem Improving speaker verification in noisy environments.
method Used end-to-end deep learning model with EEG and speech features.
result EEG signals improve speaker verification robustness, especially in noisy conditions.
New method learns signals from binary measurements, surpassing existing techniques.
problem Learning signals from noisy, incomplete, and quantized binary measurements.
method Self-supervised learning approach (SSBM) for binary data.
result SSBM outperforms supervised learning and sparse reconstruction methods.
Graph signal processing improves machine learning for network data.
problem Handling structured data on graphs in machine learning.
method Graph filters and transforms for efficient data processing.
result Enhanced model interpretability and improved efficiency.
New method recovers block-sparse signals with common sparsity patterns.
problem Recovering block-sparse signals with common sparsity patterns in MMV.
method Pattern-coupled hierarchical Gaussian prior model with EM framework.
result Proposed method automatically captures block sparse structure.
CNN model for efficient wireless spectrum sensing and signal identification.
problem Efficient utilization of scarce wireless spectrum.
method Convolutional Neural Network (CNN) based on spectral correlation function.
result Significant performance gains over existing methods.
PerceptNet learns haptic signal similarity using human data.
problem Designing haptic icons requires accurate perceptual similarity estimation.
method Deep neural network projecting signals to an embedding space with a triplet loss.
result Our method effectively models perceptual dissimilarity compared to alternatives.
Improved signal processing for long-distance optical signals.
problem Compensating walk-off effect in long-distance optical signals.
method Sub-banded DSP architecture with deep learning for walk-off compensation.
result 2.8 dB SNR improvement over linear equalization.
Algorithm learns graph ARMA processes for missing signal estimation.
problem Missing signal estimation in time-varying graph signals.
method Learning joint time-vertex power spectral density through convex relaxations.
result High accuracy in time-vertex signal estimation.
Energy consumption is an important issue in continuous wireless telemonitoring of physiological signals. Compressed sensing (CS) is a promising framework to address it, due to its energy-efficient data compression procedure. However, most CS algorithms have difficulty in data recovery due to non-sparsity characteristic…
Improved Schizophrenia diagnosis using brain signal features with limited observations.
problem Ambulatory diagnoses of neuronal diseases with limited brain signal data.
method Pairwise distance learning approach using Siamese neural network and cosine contrastive loss.
result Improved accuracy and sensitivity in Schizophrenia diagnosis (+10pp).
A new model classifies lightning signals more accurately across different scales.
problem Classifying VLF lightning transients to reduce interference and improve navigation system reliability.
method Introduces a multi-scale residual transformer (MRTransformer) to classify lightning signals.
result Achieved 90% accuracy in lightning signal classification.
PHASE predicts surgical complications from physiological signals.
problem Predicting adverse surgical outcomes from physiological signals.
method Self-supervised transfer learning for physiological signals.
result PHASE outperforms other approaches in predicting five surgical complications.
Proposes GAGA algorithm for automatic hyperparameter learning in signal recovery.
problem Difficulty in selecting hyperparameters in traditional signal recovery methods.
method Global Adaptive Generative Adjustment (GAGA) algorithm for automatic hyperparameter learning and signal estimate.
result Consistency of model selection and signal estimate output.