New DL algorithm estimates OFDM channels without pilots.
problem Estimating OFDM channels in deep fading conditions.
method Deep learning (DL) for blind channel estimation.
result First theory on MSE performance of DL-based estimator.
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169,181 papers · 148 categories
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48 results for “Channel selection”
New CNN layer selects important channels to improve model capacity.
problem Improving model capacity under resource constraints.
method Selective allocation of channels in convolutional layers.
result New layer allows new optima that generalize better.
Efficient fetal-maternal ECG signal separation from two channel maternal abdominal ECG via diffusion-based channel selectionphysics.med-ph
There is a need for affordable, widely deployable maternal-fetal ECG monitors to improve maternal and fetal health during pregnancy and delivery. Based on the diffusion-based channel selection, here we present the mathematical formalism and clinical validation of an algorithm capable of accurate separation of maternal …
Efficiently reduces training and inference costs by dynamically selecting important channels.
problem Reducing memory and computational demands for neural network training and inference.
method Integrates pruning into training by selecting only highly salient channels for execution, using combinatorial upper confidence bound algorithm.
result Reduces computational cost up to 4x and parameter count up to 9x.
CM algorithm matches Shannon's and semantic channels for multi-label classification.
problem Tackles label learning and selection for multi-label classification.
method Adheres to maximum semantic information criterion, uses Bayes' theorem, and trains truth functions.
result Shows improved performance and adaptability to changing source distributions.
Reduced-channel EEG systems struggle with artifact detection, highlighting the importance of referential channels.
problem Artifact detection in EEG signals with fewer channels.
method Investigated a deep learning algorithm, CNN-LSTM, on various channel configurations.
result False alarms increase dramatically when fewer channels are used, emphasizing the importance of referential channels.
Proposes a channel pruning method using attention statistics for deep networks.
problem Manual setting of compression ratios in each layer for deep neural networks.
method Channel pruning based on attention statistics with automatic selection of compression ratio.
result Improved performance in terms of accuracy and computational costs compared to conventional methods.
New algorithm reduces age of information in wireless networks with unknown channel reliability.
problem Learning optimal source-channel pairs to minimize age of information in wireless networks.
method Introduces AoI regret, novel learning algorithm with bounded AoI regret.
result Developed a learning algorithm with AoI regret, improving upon .
A nonlinear channel estimator using complex Least Square Support Vector Machines (LS-SVM) is proposed for pilot-aided OFDM system and applied to Long Term Evolution (LTE) downlink under high mobility conditions. The estimation algorithm makes use of the reference signals to estimate the total frequency response of the …
A new channel locality block improves CNN performance.
problem Improving the performance of convolutional neural networks.
method Proposed a variant of Squeeze-and-Excitation block using convolutional layers to learn nearby channel correlation.
result Our C-Local block achieved higher accuracy than the standard SE block on the cifar-10 dataset.
Optimal channel assignment in ad hoc networks with heterogeneous users.
problem Optimal allocation of channels in a decentralized network with unknown and varying channel characteristics.
method Developed algorithms for explore-and-commit and logarithmic regret, leveraging specific user transmissions and sensing.
result Achieved near-optimal allocation with high probability and logarithmic regret, significantly outperforming state-of-the-art methods.
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.
Free Energy Mixercs.CL
FEM improves attention mechanisms by applying value-driven log-linear tilts.
problem Standard attention mechanisms read via convex average, limiting channel-wise selection.
method Free Energy Mixer (FEM) applies a value-driven, per-channel log-linear tilt to a fast prior over indices.
result FEM outperforms strong baselines on NLP, vision, and time-series tasks.
Truncated Singular Value Decomposition (SVD) calculates the closest rank- approximation of a given input matrix. Selecting the appropriate rank defines a critical model order choice in most applications of SVD. To obtain a principled cut-off criterion for the spectrum, we convert the underlying optimization prob…
Proposes dynamic channel pruning during neural network training.
problem Pruning neural networks during training to reduce computational cost and improve efficiency.
method Dynamic channel propagation to update channel utility values and selectively prune channels.
result Our scheme trains and prunes neural networks simultaneously, achieving superior performance.
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.
Proposes HOTA-FedGradNorm for faster and robust PFL in noisy channels.
problem Statistical heterogeneity across clients in federated learning.
method Dynamic weighting, OTA aggregation, hierarchical federated learning.
result Training speed improvement and robustness against channel effects.
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.
Paper defends deep learning classifiers against channel-aware adversarial attacks.
problem Deep learning classifiers are vulnerable to adversarial attacks.
method Channel-aware adversarial attacks are presented and defended against.
result Certified defense based on randomized smoothing makes classifiers robust.
Motivated by an important insight from neural science, we propose a new framework for understanding the success of the recently proposed "maxout" networks. The framework is based on encoding information on sparse pathways and recognizing the correct pathway at inference time. Elaborating further on this insight, we pro…
New framework uses MABs for better WLAN performance.
problem Maximizing enterprise WLAN performance with complex AP and station interactions.
method Empowered APs and stations with agents using Thompson sampling to learn optimal channel and AP selection.
result Adaptive framework using MABs outperforms static configurations in various scenarios.
Auto-Compressing Subset Pruning reduces model size for faster inference.
problem High parameter counts and slow inference times in semantic segmentation models.
method Learning a channel selection mechanism based on temperature annealing schedule.
result Significant compression of segmentation models with acceptable inference performance.
Information Content of Financial Youtube Channel: Case Study of 3PROTV and Korean Stock Marketq-fin.PM
Study finds financial YouTube channel 3PROTV predicts stock market performance and sentiment changes.
problem Determining the informational value of financial YouTube channels.
method Analyzing 3PROTV's content and its impact on stock market performance and sentiment.
result 3PROTV's content, particularly negative sentiment, predicts stock market performance and sentiment changes.
Paper tackles federated linear bandit learning with AirComp for noisy channels.
problem Minimize cumulative regret in federated linear bandit learning.
method Proposes a federated linear bandits scheme using over-the-air computation (AirComp) over noisy fading channels.
result Determines the regret bound of the proposed scheme.
This paper uses entropy to derive stock price dynamics and option valuation.
problem Deriving stock price dynamics and option valuation from information constraints.
method Develops an entropic inference framework to derive stochastic processes from information constraints, representing price changes through two channels: continuous and jump.
result The derived dynamics is the Merton jump diffusion, with Geometric Brownian Motion as the no jump limit.
New algorithm for active learning from feedback coding.
problem Efficiently selecting examples for labeling in active machine learning.
method Formalized structural similarities between active learning and feedback channel coding, developed an optimal transport-based algorithm called Approximate Posterior Matching (APM).
result Learning performance comparable to existing methods at reduced computational cost.
IDP dynamically adjusts neural network computation during inference.
problem Dynamic scaling of neural network computation costs.
method Add monotonically non-increasing coefficients (profile) to channels during training; select only a subset at inference.
result IDP reduces computation by 75% on MNIST and CIFAR-10 without significant accuracy loss.
A genetic algorithm-based method prunes deep neural networks for resource-constrained devices.
problem Resource constraints in deploying deep neural networks on embedded devices.
method Genetic algorithm for channel pruning of deep CNNs.
result Significant reduction in model size with minimal accuracy loss.
Dual-Channel Tensor Neural Network (DC-TNN) decomposes tensor data into low-rank and sparse components for better estimation and inference.
problem Tensor-valued data with multilinear dependencies are challenging to process due to loss of multiway geometry under vectorization.
method DC-TNN decomposes tensors into a low-rank core and a sparse refinement, processing them through coupled neural channels.
result Established non-asymptotic risk bounds and developed structure-aware conformal ROC and AUC confidence bands.
Hierarchical Spatio-Channel Clustering for Efficient Model Compression in Medical Image Analysiscs.CV
This paper proposes a new method to compress CNNs for medical image analysis, improving efficiency and accuracy.
problem Large memory and computational requirements of CNNs in resource-constrained environments.
method Hierarchical spatio-channel low-rank compression framework that partitions feature maps into spatial regions and groups channels according to co-activation patterns within each region.
result The proposed method achieves significant FLOP reduction, inference speed-up, and improved classification accuracy compared to existing methods.
PixelHop++: A Small Successive-Subspace-Learning-Based (SSL-based) Model for Image Classificationeess.IV
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.
SCBF preserves medical data privacy by training models without sharing inputs.
problem Privacy concerns in medical data collection and training.
method Stochastic Channel-Based Federated Learning (SCBF) with pruning.
result SCBF outperforms Federated Averaging with better performance and faster saturating speed.
This work improves communication efficiency in federated learning over wireless networks by optimizing energy consumption.
problem Optimizing energy consumption in federated learning over wireless networks.
method Adopting SignSGD for gradient sign exchange, considering channel capacity with outage, and proposing a stochastic sign-based algorithm for uneven data distribution.
result Proposed methods achieve a balance between learning performance and energy consumption.
The study reveals flaws in pruning criteria and proposes a new assumption for better filter selection.
problem Flaws in existing pruning criteria for CNNs.
method Empirical experiments and Convolutional Weight Distribution Assumption.
result The Convolutional Weight Distribution Assumption improves filter selection in pruning.
Develops a framework for distributional Granger causality
problem Identifying predictive dependence in time series beyond Gaussian settings
method Using finite collection of channel-specific restrictions
result Identifies distributional Granger non-causality through testable hypotheses
Sparse group matrix completion reduces complexity and improves performance.
problem Matrix completion with non-informative side features.
method Group-Lasso regularization for feature selection in matrix factorization.
result Theoretical sample complexity is significantly lower than competitors.
This paper tackles resource allocation in the Lightning Network using DRL.
problem Complex combinatorial problem of node selection and resource allocation in the Lightning Network.
method Attention-based Deep Reinforcement Learning framework.
result Improved resource allocation leads to better performance and decentralization in the LN.
The paper introduces tools to assess data transformation quality using information entropy.
problem Assessing the quality of data transformations in machine learning tasks.
method Introduces a decomposition of maximal potential entropy into non-transferable, transferable but not transferred, and transferred parts.
result Developed a visual exploratory tool called the Channel Multivariate Entropy Triangle to assess information transfer efficiency.
Deep learning predicts V2I channel responses for high-mobility wireless communications.
problem Efficient channel estimation for high-mobility V2I communications.
method Developed a deep neural network-based channel prediction method.
result Deep neural networks can learn and predict V2I channel properties in real-time.
The paper applies communication theory to improve language model reranking.
problem Ensuring safety and accuracy in language model outputs.
method Drawing parallels between communication theory and language model reranking, the authors propose a protocol to improve reliability.
result The proposed protocol can achieve asymptotically error-free performance in noisy communication scenarios.
Discrete channel surfacesmath.DG
Defines discrete channel surfaces in Lie sphere geometry.
problem Defining discrete channel surfaces in Lie sphere geometry.
method Definition and associated data sets for reconstruction.
result Proof of a discrete version of Vessiot's Theorem for isothermic discrete channel surfaces.
DNA improves graph neural networks by selectively aggregating node embeddings.
problem Static neighborhood aggregation limits graph neural networks' performance.
method Dynamic neighborhood aggregation guided by attention and controlled channel connections.
result DNA outperforms current methods in transductive node classification.
The study characterizes channel surfaces in Lie sphere geometry and their transformations.
problem Characterizing channel surfaces in Lie sphere geometry.
method Using -surfaces and Dupin cyclide congruences to study transformations and Ribaucour pairs.
result Characterization of channel surfaces and their transformations.
CN normalizes channels for better time series model performance.
problem Improper channel identification in time series models.
method Channel Normalization (CN) and its variants (ACN, PCN) using distinct affine transformations.
result Significant performance gains across various time series models.
A new DVAE architecture improves channel estimation by incorporating temporal correlations.
problem Improving the estimation of time-varying channels.
method Introducing k-MemoryMarkovVAE (k-MMVAE) architecture to learn temporal correlations.
result The k-MMVAE aided channel estimator outperforms other ML aided estimators.
Generative diffusion models improve channel sampling from limited data.
problem Challenges in channel modelling and data collection for wireless systems.
method Diffusion model with U-Net architecture for frequency domain synthesis.
result Stable training and diverse high-fidelity samples generated from true channel distribution.
Unsupervised Linear and Nonlinear Channel Equalization and Decoding using Variational Autoencoderscs.LG
New method uses VAEs for blind channel equalization and decoding.
problem Blind channel equalization and decoding without pilot symbols.
method Variational autoencoders (VAEs) for blind channel equalization and decoding.
result Significant improvement in error rate compared to existing methods.
BestChanID identifies the channel with maximal capacity using training sequences.
problem Identifying the channel with maximal capacity among several discrete memoryless channels.
method Formulated as a multi-armed bandit problem, proposed a capacity estimator, and developed gap-elimination algorithms.
result Guaranteed to output the DMC with the largest capacity with a desired confidence.