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.
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.
Study models growth of unorganized retail in Indian pharma sector amid organized and e-retail competition.
problem Survival strategies of unorganized retailers challenged by organized and e-retailers.
method Field investigation, conjoint analysis, design of experiments, agent-based simulation.
result Unorganized retailers' performance influenced by their own attribute levels and price discounts.
Spinal cord stimulation has enabled humans with motor complete spinal cord injury (SCI) to independently stand and recover some lost autonomic function. Quantifying the quality of bipedal standing under spinal stimulation is important for spinal rehabilitation therapies and for new strategies that seek to combine spina…
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.
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.
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.
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…
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.
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.
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.
Neuroimaging modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) provide information about neurological functions in complementary spatiotemporal resolutions; therefore, fusion of these modalities is expected to provide better understanding of brain activity. In this paper, …
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.
We characterize the singular values of the linear transformation associated with a standard 2D multi-channel convolutional layer, enabling their efficient computation. This characterization also leads to an algorithm for projecting a convolutional layer onto an operator-norm ball. We show that this is an effective regu…
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.
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.
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…
This paper presents a Semantic Attribute Modulation (SAM) for language modeling and style variation. The semantic attribute modulation includes various document attributes, such as titles, authors, and document categories. We consider two types of attributes, (title attributes and category attributes), and a flexible a…
Independent Component Analysis (ICA) is a technique for unsupervised exploration of multi-channel data widely used in observational sciences. In its classical form, ICA relies on modeling the data as a linear mixture of non-Gaussian independent sources. The problem can be seen as a likelihood maximization problem. We i…
Proposes a Taylor framework to unify and analyze attribution methods.
problem Lack of a unified guideline for feature contribution assignment in machine learning models.
method Introduces a Taylor attribution framework to model the attribution problem and reformulates fourteen mainstream methods.
result Empirically validates the Taylor reformulations and reveals a positive correlation between performance and principles followed.
We present a new method for the separation of superimposed, independent, auto-correlated components from noisy multi-channel measurement. The presented method simultaneously reconstructs and separates the components, taking all channels into account and thereby increases the effective signal-to-noise ratio considerably…
Unified framework for analyzing machine learning model attributions.
problem Lack of a general and theoretical framework for understanding attribution methods.
method Proposes a Taylor attribution framework to unify and analyze seven mainstream attribution methods.
result Established three principles for good attribution and empirically validated the Taylor reformulations.
Improved deep learning models using new attribution priors and expected gradients.
problem Improving interpretability and performance of deep learning models.
method Introducing new attribution priors and expected gradients method that satisfies interpretability axioms.
result Improves model performance across various real-world tasks.
Facial attribute editing aims to manipulate single or multiple attributes of a face image, i.e., to generate a new face with desired attributes while preserving other details. Recently, generative adversarial net (GAN) and encoder-decoder architecture are usually incorporated to handle this task with promising results.…
Paper tackles attribute pattern learning in high-dimensional SLAMs.
problem Learning significant attribute patterns from high-dimensional SLAMs.
method Proposes a penalized likelihood method for selecting attribute patterns.
result Establishes selection consistency in overfitted SLAMs.
Wavelet Attribution Method (WAM) improves feature attribution for deep models.
problem Inability of pixel-based heatmaps to capture data structure and variability in feature attribution.
method Wavelet domain for feature attribution, leveraging spatial and scale-localized properties of wavelet coefficients.
result WAM provides quantitatively superior explanations across audio, image, and volume modalities.
Proposes VCLANC for attributed network clustering using node and attribute embeddings.
problem Lack of mutual affinity exploitation between nodes and attributes in graph convolution.
method Dual variational auto-encoders for node and attribute embeddings, Gaussian mixture model priors, mutual distance and clustering assignment hardening losses.
result Demonstrates effectiveness on real-world attributed network datasets.
Generates missing node attributes for better graph-based tasks.
problem Missing or incomplete node attributes degrade graph-based algorithms' performance.
method Deep adversarial learning-based method (NANG) to generate node attributes.
result Generated node attributes improve node classification and link prediction.
Proposes a method to predict node attributes using network topology.
problem Predicting node attributes in graphs for various applications.
method Creates a feature map using all attributes of neighbors to predict attributes values for a node.
result Significantly improves prediction accuracy compared to baseline approaches.
Investors seek to attribute performance to various features using Shapley value method.
problem Attributing performance to different features in an investment process.
method Use Shapley value method for attribution, either exactly or approximately.
result Shapley value method provides a preferred attribution approach.