Model classifies environment sounds using multiple feature channels and attention mechanisms.
problem Environment sound classification task.
method Multiple feature channels (MFCC, GFCC, CQT, Chromagram) and attention mechanism in a deep CNN.
result Achieves state-of-the-art performance on three benchmark datasets.
PixelHop++ improves image classification with a smaller model size.
problem Improving image classification models with smaller sizes.
method Decomposing input tensor, channel-wise Saab transform, successive subspace learning, feature ranking.
result PixelHop++ offers a flexible tradeoff between model size and performance.
Adversaries with multiple antennas can fool deep learning modulators more effectively.
problem Improving evasion attacks on deep learning-based modulation classifiers.
method Utilizing multiple antennas to enhance adversarial attacks on deep learning classifiers.
result Adversarial attacks with multiple antennas significantly improve classifier accuracy.
We propose channel charting (CC), a novel framework in which a multi-antenna network element learns a chart of the radio geometry in its surrounding area. The channel chart captures the local spatial geometry of the area so that points that are close in space will also be close in the channel chart and vice versa. CC w…
Proposes C2AF network for multi-view time series classification.
problem Improving multi-view time series classification performance.
method Two-stream structured encoder, graph-based correlation matrix, channel-aware fusion mechanism.
result Extensive experimental results show superior performance over state-of-the-art methods.
Optical DNNet boosts accuracy with multiple frequency-channels.
problem Improving the accuracy of optical neural networks.
method Developed a novel optical diffractive deep neural network with multiple frequency-channels.
result Multiple frequency-channels significantly increase network accuracy.
DeepSIC learns to detect multiple symbols in MIMO systems without assuming a specific channel model.
problem Challenges in multiuser MIMO detection due to non-linear channels and lack of accurate channel state information.
method Integrates machine learning into iterative soft interference cancellation (SIC) algorithm to learn from limited training samples.
result Significantly outperforms conventional methods in linear and non-linear channels, even with CSI uncertainty.
A new method for early stopping in neural networks without validation sets.
problem Determining when to stop training neural networks to avoid overfitting.
method Channel-wise DeepNNK (CW-DeepNNK) using non-negative kernel regression and polytope interpolation.
result The proposed early stopping criterion based on CW-DeepNNK performs better than standard validation-based methods.
Novel channel pruning method accelerates deep CNNs by removing redundant, similar features.
problem Limitations of existing magnitude-based pruning algorithms in similar magnitude cases.
method Hierarchical clustering of channels based on probabilistic similarity metrics, without sparsity training.
result 30% reduction in FLOPs for pruned ResNet-50 on ImageNet, outperforming baseline.
A new method quantifies feature-map discriminativeness for efficient pruning of deep neural networks.
problem Efficiently pruning deep neural networks to reduce computation while maintaining accuracy.
method Presented a novel mathematical formulation (Discriminant Information, DI) to quantify feature-map discriminativeness, enabling efficient pruning and intra-layer mixed precision quantization.
result Our pruned ResNet50 achieves 44% FLOPs reduction without any Top-1 accuracy loss.
CHEER boosts poor models using rich model knowledge.
problem Improving performance of models trained on limited data.
method Develops CHEER framework to infuse rich model knowledge into poor models.
result CHEER significantly improves model performance on physiological datasets.
BottleNet++ compresses deep learning features for efficient mobile inference.
problem Balancing computation and communication in mobile devices with deep learning models.
method End-to-end architecture with encoder, non-trainable channel layer, and decoder.
result Achieves high compression ratio and bandwidth reduction with minimal accuracy loss.
Studies in recent years have demonstrated that neural organization and structure impact an individual's ability to perform a given task. Specifically, individuals with greater neural efficiency have been shown to outperform those with less organized functional structure. In this work, we compare the predictive ability …
A new method detects hidden driving forces in systems with multiple observables.
problem Hidden driving forces in systems with multiple observables cannot be detected by scalar statistics.
method Cross-spectral witness for hidden nonequilibrium.
result Two simultaneously observed channels retain an off-diagonal cross-spectral sector inaccessible to scalar reductions.
ReQuestNet simplifies 5G channel estimation with a unified model.
problem Complex channel estimation in 5G systems with varying conditions.
method Unified neural architecture that handles dynamic resource blocks and transmit layers.
result Significantly outperforms legacy methods, achieving up to 10dB gain at high SNRs.
IETNet identifies important channels for MVTS classification.
problem Multivariate time series classification with blackbox deep networks.
method End-to-end network combining temporal feature extraction, variable selection, and interaction.
result IETNet improves model accuracy and reduces overfitting by identifying and removing non-predictive variables.
Data-driven symbol detection improves performance in complex channels.
problem Designing robust symbol detectors in systems with poorly understood channels.
method Hybrid approach combining model-based algorithms with machine learning.
result Near-optimal performance of model-based algorithms achieved without channel model knowledge.
DNNs improve localization from channel estimates, overcoming practical impairments.
problem Improving localization accuracy from channel estimates in Massive MIMO systems.
method Principled feature design for DNNs invariant to practical impairments.
result DNN achieves high localization accuracy and generalization capability.
After being trained, classifiers must often operate on data that has been corrupted by noise. In this paper, we consider the impact of such noise on the features of binary classifiers. Inspired by tools for classifier robustness, we introduce the same classification probability (SCP) to measure the resulting distortion…
Paper proposes neural network for efficient MIMO channel estimation and pilot reduction.
problem High overhead from pilot transmission in wideband MIMO systems.
method Neural network architecture for frequency-aware pilot design and channel estimation, with pruning technique.
result Neural network outperforms linear minimum mean square error (LMMSE) estimation.
A novel Gradient-Based Multiple Access algorithm for distributed learning over fading channels.
problem Distributed learning over multiple access fading channels.
method Gradient-Based Multiple Access (GBMA) algorithm, using analog gradients and common shaping waveforms.
result GBMA can approach the convergence rate of centralized gradient descent in large networks.
Personalizes query completion in digital commerce with image-based vectors.
problem High bounce rate and low recurring users in query completion.
method Injects dense vectors from product images into a noisy channel model.
result Image-based personalization improves query completion and user experience.
Symbol detection for Massive Multiple-Input Multiple-Output (MIMO) is a challenging problem for which traditional algorithms are either impractical or suffer from performance limitations. Several recently proposed learning-based approaches achieve promising results on simple channel models (e.g., i.i.d. Gaussian). Howe…
In this paper, we consider the use of deep neural networks in the context of Multiple-Input-Multiple-Output (MIMO) detection. We give a brief introduction to deep learning and propose a modern neural network architecture suitable for this detection task. First, we consider the case in which the MIMO channel is constant…
New exact tests detect changepoints in binary and count data, especially when normal approximations fail.
problem Detecting changepoints in multichannel binary and count data.
method Exact tests combining two-sample conditional tests with multiplicity correction.
result Exact tests are much more powerful than asymptotic tests in various settings.
Gradual pruning reduces inference cost by pruning least important channels during training.
problem Reduction of deep neural network inference cost.
method Gradual channel pruning using feature relevance scores during training.
result Achieved significant model compression with minimal accuracy loss.
Study online learning of quantum processes, showing feasibility for certain types.
problem Learning quantum processes adaptively, especially for bounded gate complexity and Pauli channels.
method Online learning, mistake-bounded model, multiplicative weights update algorithm, Bell sampling.
result Online learning feasible for quantum channels of bounded gate complexity and Pauli channels.
Paper proposes integrating wavelet transform, channel attention, and LSTM for better stock price prediction.
problem Inherently difficult stock price prediction due to low signal-to-noise ratio.
method Wavelet transform convolution, channel attention, and LSTM integration.
result Robust performance in post-pandemic market conditions.
Random orthogonalization improves FL in massive MIMO systems without CSI.
problem Efficient model aggregation in FL with minimal channel estimation overhead.
method Combining FL with massive MIMO's channel hardening and favorable propagation, random orthogonalization reduces channel estimation overhead.
result Achieves model aggregation without CSI, significantly reducing channel estimation overhead.
Two-layer NN with channel attention learns low-degree spherical polynomials efficiently.
problem Learning low-degree spherical polynomials with over-parameterized neural networks.
method Two-layer neural network with channel attention, vanilla gradient descent, learnable channel selection.
result Minimally improved sample complexity of $n \asymp Θ(d^{\ell_0}/\eps)$ for learning low-degree polynomials.
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.
In some speaker recognition scenarios we find conversations recorded simultaneously over multiple channels. That is the case of the interviews in the NIST SRE dataset. To take advantage of that, we propose a modification of the PLDA model that considers two different inter-session variability terms. The first term is t…
We propose the use of incomplete dot products (IDP) to dynamically adjust the number of input channels used in each layer of a convolutional neural network during feedforward inference. IDP adds monotonically non-increasing coefficients, referred to as a "profile", to the channels during training. The profile orders th…
Paper improves channel charting using autoencoders with spatial constraints.
problem Improving logical positioning of UEs using channel-state information.
method Representation-constrained autoencoders to enhance channel charts.
result Improved quality of learned channel charts for UE positioning.
FedRec learns universal receivers for fading channels without channel statistics.
problem Training neural network-based receivers for diverse fading channels without accurate statistics.
method Federated learning of a MAP detector for downlink fading channels.
result Performance approaches MAP without channel statistics, reduced communication overhead.
The Maximum Mutual Information (MMI) criterion is different from the Least Error Rate (LER) criterion. It can reduce failing to report small probability events. This paper introduces the Channels Matching (CM) algorithm for the MMI classifications of unseen instances. It also introduces some semantic information method…
Proposes CNN-based analog CSI feedback for FDD MIMO-OFDM systems.
problem High CSI feedback overhead in FDD MIMO systems.
method AnalogDeepCMC: maps downlink CSI to uplink channel input, reconstructs channel estimate.
result Significantly improves downlink spectral efficiency and simplifies operation.
Optimal joint separation condition for radar and communications channels in dual-blind deconvolution.
problem Recovering information from overlaid radar and communications signals with unknown channels.
method Extremal functions from Beurling-Selberg interpolation theory for joint separation, nuclear norm minimization for matrix retrieval, and MUSIC for parameter estimation.
result Guaranteed well-conditioned Vandermonde matrix for MUSIC, validating theoretical findings.
A central machine is interested in estimating the underlying structure of a sparse Gaussian Graphical Model (GGM) from datasets distributed across multiple local machines. The local machines can communicate with the central machine through a wireless multiple access channel. In this paper, we are interested in designin…
The present study proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG. Most of the existing methods rely on hand-engineered features which require prior knowledge of sleep analysis. Only a few of them encode the temporal information such as transition ru…
Proposes a deep reinforcement learning framework for dynamic multichannel access.
problem Efficient use of limited spectral resources in dynamic multichannel access.
method Deep actor-critic reinforcement learning framework for both single-user and multi-user scenarios.
result Demonstrates improved performance and adaptive ability compared to existing methods.
In this paper, we consider signal detection algorithms in a multiple-input multiple-output (MIMO) decode-forward (DF) relay channel with one source, one relay, and one destination. The existing suboptimal near maximum likelihood (NML) detector and the NML with two-level pair-wise error probability (NMLw2PEP) detector a…
A new method classifies color images using quaternion algebra.
problem Classifying color images with preserved intrinsic relationships.
method LSQMM model with quaternion nuclear norm regularization and ADMM algorithm.
result LSQMM outperforms state-of-the-art methods in classification accuracy and efficiency.
A new method prunes neural network channels based on operation characteristics.
problem Compressing deep neural networks efficiently and maintaining accuracy.
method Differentiable masks for channel pruning considering BN and ReLU.
result Outstanding performance in accuracy with less resources compared to state-of-the-art methods.
Using low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar (SAR) technology for detecting buried and obscured targets, e.g. bomb or mine, has been successfully demonstrated recently. Despite promising recent progress, a significant open challenge is to distinguish obscured targets from other (nat…
A Monte Carlo k-nearest neighbours (KNN) and a multi-resolution convolutional neural network (CNN) were developed to detect the presences of multiple gasses in near infrared (IR) spectrums. High Resolution Transmission database was used to synthesize the near IR spectrums. Monte Carlo KNN determined the optimal kernel …
New framework improves multivariate time series forecasting by minimizing redundant information.
problem Improving multivariate time series forecasting with deep learning techniques.
method Cross-variable Decorrelation Aware feature Modeling (CDAM) and Temporal correlation Aware Modeling (TAM) to refine Channel-mixing and exploit temporal correlations.
result Significantly surpasses existing models in comprehensive tests.
Enhances Transformer models for multivariate time series with dataset-specific channel masks.
problem Capturing channel dependency in multivariate time series models.
method Introduces partial channel dependence and channel masks to refine channel dependency in Transformers.
result Improves model performance across diverse tasks and datasets.