New method separates noisy auto-correlated components from multi-channel measurements.
problem Separating independent auto-correlated components from noisy multi-channel data.
method Simultaneous reconstruction and separation of components considering all channels, using information field theory.
result Significant improvement in signal-to-noise ratio, allowing separations even in high noise conditions.
In this paper, we investigate a new compressive sensing model for multi-channel sparse data where each channel can be represented as a hierarchical tree and different channels are highly correlated. Therefore, the full data could follow the forest structure and we call this property as \emph{forest sparsity}. It exploi…
Quaternion neural networks improve distant speech recognition.
problem Challenges in distant speech recognition due to noise and reverberation.
method Quaternion neural networks process multi-channel audio signals as quaternion entities, capturing internal and external dependencies.
result QLSTM outperforms real-valued LSTM on multi-channel distant speech recognition tasks.
Quantum CNNs improve on multi-channel data processing.
problem Lack of efficient processing for multi-channel data in QCNNs.
method Developed hardware-adaptable quantum circuit ansatzes for convolutional kernels.
result Quantum CNNs outperform existing QCNNs on multi-channel data classification tasks.
Dual U-net models improve multi-channel MRI image reconstruction.
problem Improving MRI image reconstruction from multi-channel data.
method Two-element U-nets (W-nets) in k-space and image domains, evaluated for four configurations.
result Dual domain methods are more advantageous for simultaneous reconstruction of all channels.
Goal: This paper deals with the problems that some EEG signals have no good sparse representation and single channel processing is not computationally efficient in compressed sensing of multi-channel EEG signals. Methods: An optimization model with L0 norm and Schatten-0 norm is proposed to enforce cosparsity and low r…
DeepcomplexMRI uses deep residual networks for faster MRI imaging.
problem Accelerating parallel MR imaging with high accuracy.
method Deep complex convolutional neural network with residual connections and k-space consistency.
result The method can accurately reconstruct multi-channel MRI images.
Biological neural network mimics CCA for multi-channel data.
problem Implementing CCA in a biologically plausible neural network.
method Derive an online CCA algorithm with local synaptic updates for multi-compartmental neurons.
result The derived neural network architecture and synaptic updates resemble cortical pyramidal neuron behavior.
BankGCN improves graph convolution networks by handling multi-channel signals with adaptive filter banks.
problem Handling multi-channel graph signals with limited architectures.
method BankGCN decomposes multi-channel signals into subspaces and uses adapted filters for each subspace.
result BankGCN achieves excellent performance in graph classification on benchmark datasets.
Paper presents techniques to classify UWB SAR imagery, distinguishing targets from clutter.
problem Distinguishing obscured targets from clutter in UWB SAR imagery.
method Three novel sparsity-driven techniques exploiting tensor coefficients and polarization diversity.
result Tensor sparsity models enhance classification accuracy of multi-channel SAR data.
Team MIE-Lab forecasts city traffic using past hour's multi-channel images.
problem Predict city-wide traffic status within 15 mins using past hour's multi-channel images.
method Evaluated network architectures, analyzed data, considered spatio-temporal context.
result Best submission in IARAI competition traffic4cast.
Characterizes inductive bias in multi-channel linear CNNs with bounded weight norm.
problem Understanding the inductive bias in multi-channel linear convolutional networks.
method Function space characterization and empirical testing of gradient descent.
result The inductive bias depends on the number of output channels for multi-channel inputs but not for single-channel inputs.
WaveletGAN improves GANs by homogenizing noise through multi-channel wavelet filtering.
problem Current noise generation models in GANs struggle with homogenizing noise, leading to low-fidelity samples.
method Proposes a multi-channel wavelet-based filtering method to homogenize noise in GANs.
result WaveletGAN generates high-fidelity samples with the smallest FIDs on Fashion-MNIST, KMNIST, and SVHN datasets.
EMG analysis quantifies bipedal standing quality in SCI patients.
problem Quantifying the quality of bipedal standing in spinal cord injury patients.
method Multi-channel surface EMG recordings during spinal stimulation therapy sessions.
result Multi-channel EMG recording can provide accurate, fast, and robust estimation for standing quality in SCI patients.
System separates sounds from mixtures without ground truth info.
problem Sound separation from multi-channel mixtures without labeled data.
method Deep clustering on multi-channel mixtures, projecting bins to spatially correlated clusters.
result Performance matches ground truth separation using only multi-channel mixtures.
LSTM neural networks improve fiber nonlinearities in coherent systems.
problem Compensating fiber nonlinearities in digital coherent systems.
method Utilization of Long short-term memory (LSTM) neural networks.
result LSTM neural networks provide superior performance compared to digital back propagation, especially in multi-channel scenarios.
Deep learning models predict epileptic seizures with high accuracy.
problem Predicting epileptic seizures for better patient care.
method Developed Temporal Multi-Channel Transformer (TMC-T) and Vision Transformer (TMC-ViT) models for EEG signals.
result TMC-ViT model outperformed CNN in seizure prediction.
DNAMTA model improves media channel attribution accuracy.
problem Measuring the impact of each advertising channel in multi-channel marketing.
method Deep Neural Net with Attention (DNAMTA) model for multi-touch attribution.
result DNAMTA model outperforms existing methods in conversion prediction and media influence evaluation.
Proposes a new deep learning framework for financial stock trading.
problem Lack of effective techniques to fuse multi-channel financial time-series data.
method Inspired by convolution transform learning, SDCF processes channels through 1-D convolutions, fuses outputs with fully-connected layers, and applies softmax classification.
result Proposed framework yields better results than state-of-the-art techniques for stock trading.
TM-CNN predicts lane-level traffic speeds considering volume impact.
problem Aggregated lane-level traffic speed prediction and volume impact.
method Two-stream multi-channel CNN, data conversion, two-stream deep neural network, loss function.
result TM-CNN outperforms existing models in multi-lane traffic speed prediction.
MOGPTK simplifies multi-channel data modeling with Gaussian processes.
problem Modeling multi-channel data efficiently and accurately.
method Python package with TensorFlow backend, supporting various GP kernels and parameter initialization strategies.
result Enables GPU-accelerated training and comprehensive GP modeling pipeline.
Detects adversarial samples using density ratio estimation.
problem Adversarial samples lead to incorrect classifications in machine learning models.
method Direct density ratio estimation for model agnostic detection of adversarial samples.
result Effective detection of adversarial samples with various methods and constraints.
Proposes a deep neural network for event intensity estimation.
problem Modeling irregular event sequences with historical dependencies.
method Non-parametric deep neural network with multi-channel RNN and fake event epochs.
result Outperforms state-of-the-art baselines on model fitting tasks.
GANs generate biological cell images capturing protein relationships.
problem Synthesize cells from fluorescence microscopy images.
method Adapted GANs with casual dependencies for multi-channel image generation.
result Demonstrated ability to predict temporal evolution from static images.
Improved Granger causality method for dynamic time series data.
problem Traditional Granger causality method assumes constant causalities, failing to model dynamic causalities.
method Dynamic window-level Granger causality (DWGC) method with causality indexing.
result Improved DWGC method better detects window-level causalities.
Paper solves complex signal processing problem efficiently.
problem Learning an unknown filter from multiple sparse convolutions.
method Nonconvex optimization over the sphere manifold using manifold gradient descent.
result Manifold gradient descent provably recovers the filter under random data model.
Unified framework for speech separation using deep learning.
problem Extracting individual speech sources from mixed signals.
method Unified framework combining spectrogram and waveform separations.
result Unified framework achieves competitive performance.
TIMeSynC combines financial service interactions for intent prediction.
problem Aligning and learning from multi-domain, multi-resolution sequences for accurate intent prediction.
method An encoder-decoder transformer model addressing sequence alignment, temporal dynamics, and dynamic/static sequence combination.
result Significant improvement in intent prediction over existing methods.
Proposes KDA to protect deep nets from adversarial attacks.
problem Machine learning system vulnerability to adversarial attacks.
method Key based diversified aggregation with pre-filtering.
result Demonstrates high robustness and universality against various attacks.
This paper optimizes multi-channel sequential advertising to maximize cumulative revenue.
problem Maximizing cumulative revenue in multi-channel sequential advertising under a budget constraint.
method Formulated as a dynamic knapsack problem, proposed a bilevel optimization framework with action space reduction.
result Significantly improved cumulative revenue compared to state-of-the-art baselines.
Jointly analyzes EEG and fMRI to understand schizophrenia.
problem Understanding neurological changes in schizophrenia using neuroimaging.
method Used a coupled matrix and tensor factorization (CMTF) model to analyze fMRI and EEG signals.
result Captures meaningful temporal and spatial signatures of patterns that differ between patients and controls.
New deep network derived from rate reduction principles, explaining features and efficiency.
problem Understanding and optimizing deep learning architectures.
method Gradient ascent scheme for rate reduction leading to multi-layer deep network.
result Explicitly constructed multi-layer network with precise optimization and interpretation.
RoyalFlush system improves multi-speaker ASR in M2MeT challenge.
problem Improving multi-speaker automatic speech recognition in noisy environments.
method Front-end processing with WPE and beamforming, data augmentation, and fusion of two ASR models.
result 12.22% absolute CER reduction on validation set and 12.11% on test set compared to baseline.
Randomized diversification defends machine learning models against adversarial attacks.
problem Vulnerability of machine learning systems to adversarial attacks.
method Multi-channel architecture with shared secret key for randomized transforms in a gray-box scenario.
result Increased robustness against various adversarial attacks.
This paper proposes a stochastic model using the concept of Markov chains for the inter-state transitions of the millisecond order quasi-stable phase synchronized patterns or synchrostates, found in multi-channel Electroencephalogram (EEG) signals. First and second order transition probability matrices are estimated fo…
Characterizes and projects convolutional layers' singular values for improved deep learning performance.
problem Improving deep learning models' performance using convolutional layers.
method Characterizes and projects the singular values of convolutional layers, providing an effective regularizer.
result Improves test error of a deep residual network on CIFAR-10 from 6.2% to 5.3%.
A new method extracts linguistic objects from text using CNNs.
problem Lack of interpretability in deep learning models for text.
method Weighted extension of Text Deconvolution Saliency (wTDS) measure.
result Extracts interpretable linguistic objects from text.
New approach improves human activity recognition with wearables.
problem Improving human activity recognition with wearables.
method Exploiting latent relationships between multi-channel sensor modalities, data-agnostic augmentation, and a classification loss criterion.
result Achieves new state-of-the-art performance on four diverse activity recognition benchmarks.
Picard-O improves ICA for faster, robust separation of signals.
problem Efficiently separating signals in multi-channel data.
method Preconditioned L-BFGS over orthogonal matrices.
result Picard-O outperforms FastICA in speed and robustness.
The paper analyzes deep ReLU CNNs' approximation properties in 2D space.
problem Establishing L2 approximation properties for deep ReLU CNNs. method Analysis based on decomposition theorem for convolutional kernels, properties of ReLU activation, and connections with one-hidden-layer ReLU NNs.
result Universal approximation theorem for deep ReLU CNNs with classic structure.
Kernel-based RL learns efficient spectrum access with budget constraints.
problem Efficient spectrum access in congested bands.
method Kernel-based reinforcement learning with budget-constrained sparsification.
result Performance gains over carrier-sense systems.
Faster ICA for real data using Hessian approximations.
problem Efficiently solving ICA on large real datasets.
method Preconditioned ICA with sparse Hessian approximations.
result Superior performance on real data compared to other algorithms.
2D CNNs approximate Korobov functions with near-optimal rates.
problem Approximating Korobov functions using 2D CNNs.
method Constructive approach for 2D CNNs with ReLU activations and fully connected layers.
result 2D CNNs achieve near-optimal approximation rates for Korobov functions.
Memory-efficient learning for large-scale imaging systems.
problem Memory limitations in GPUs for real-world large-scale inverse problems.
method Exploits reversibility of network layers to enable data-driven design.
result Demonstrated on small-scale and large-scale real-world systems.
Develops a multichannel deep network for faster, artifact-free image CS.
problem Block-wise sampling artifacts in image CS with multiple sampling rates.
method Multichannel deep network for block-based image CS, removing blocking artifacts.
result Significantly outperforms state-of-the-art CS methods in objective and subjective metrics.
A deep clustering model learns to separate audio sources without supervision.
problem Training deep clustering models requires supervision, limiting their applicability.
method Proposes an unsupervised spatial clustering approach to train a deep clustering system.
result The deep clustering model achieves similar performance to a multi-channel teacher without supervision.
Investigates scaling deep neural networks to avoid capacity issues.
problem Avoiding the shattering problem in deep neural networks.
method Formulates conjecture and studies various architectures to determine scaling relations.
result Reveals scaling relations for deep residual networks and recurrent networks.
Employs granular data to create a multilayer network for euro area banks, revealing distinct risk patterns.
problem Lack of comprehensive, granular data integration for systemic risk assessment in euro area banks.
method Constructs an empirically grounded multilayer network integrating various supervisory and statistical datasets, each layer representing a distinct transmission channel.
result Cross-layer heterogeneity in connectivity and centrality reveals economically relevant structure and misidentifies systemically important institutions.