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07/06/2023
Pruning vs Quantization: Which is Better?
Neural network pruning and quantization techniques are almost as old as ...
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11/27/2022
Spatially Sparse Precoding in Wideband Hybrid Terahertz Massive MIMO Systems
In terahertz (THz) massive multiple-input multiple-output (MIMO) systems...
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10/24/2022
A PAC-Bayesian Generalization Bound for Equivariant Networks
Equivariant networks capture the inductive bias about the symmetry of th...
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09/15/2022
Multi-Modal Beam Prediction Challenge 2022: Towards Generalization
Beam management is a challenging task for millimeter wave (mmWave) and s...
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09/01/2022
Learning Perturbations for Soft-Output Linear MIMO Demappers
Tree-based demappers for multiple-input multiple-output (MIMO) detection...
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07/22/2022
Quantized Sparse Weight Decomposition for Neural Network Compression
In this paper, we introduce a novel method of neural network weight comp...
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06/28/2022
Equivariant Priors for Compressed Sensing with Unknown Orientation
In compressed sensing, the goal is to reconstruct the signal from an und...
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05/18/2022
Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?
Millimeter-wave (mmWave) communication systems rely on narrow beams for ...
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05/10/2022
Deep Learning-based Channel Estimation for Wideband Hybrid MmWave Massive MIMO
Hybrid analog-digital (HAD) architecture is widely adopted in practical ...
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03/16/2022
MIMO-GAN: Generative MIMO Channel Modeling
We propose generative channel modeling to learn statistical channel mode...
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03/15/2022
Neural RF SLAM for unsupervised positioning and mapping with channel state information
We present a neural network architecture for jointly learning user locat...
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12/08/2021
Generalization Error Bounds for Iterative Recovery Algorithms Unfolded as Neural Networks
Motivated by the learned iterative soft thresholding algorithm (LISTA), ...
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09/26/2021
Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking
We propose Hypernetwork Kalman Filter (HKF) for tracking applications wi...
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10/29/2020
Generalization bounds for deep thresholding networks
We consider compressive sensing in the scenario where the sparsity basis...
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10/05/2020
Tight bounds on the mutual coherence of sensing matrices for Wigner D-functions on regular grids
Many practical sampling patterns for function approximation on the rotat...
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02/18/2020
Gradient ℓ_1 Regularization for Quantization Robustness
We analyze the effect of quantizing weights and activations of neural ne...
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06/03/2019
Adversarial Risk Bounds for Neural Networks through Sparsity based Compression
Neural networks have been shown to be vulnerable against minor adversari...
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05/24/2019
A Deep Learning Wireless Transceiver with Fully Learned Modulation and Synchronization
In this paper, we present a deep learning based wireless transceiver. We...
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04/25/2019
Sensing Matrix Design and Sparse Recovery on the Sphere and the Rotation Group
In this paper, the goal is to design random or regular samples on the sp...
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01/29/2019
On the Effect of Low-Rank Weights on Adversarial Robustness of Neural Networks
Recently, there has been an abundance of works on designing Deep Neural ...
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01/18/2019
The Restricted Isometry Property of Block Diagonal Matrices for Group-Sparse Signal Recovery
Group-sparsity is a common low-complexity signal model with widespread a...
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12/15/2018
Perturbation Analysis of Learning Algorithms: A Unifying Perspective on Generation of Adversarial Examples
Despite the tremendous success of deep neural networks in various learni...
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03/21/2018
Learning the Localization Function: Machine Learning Approach to Fingerprinting Localization
Considered as a data-driven approach, Fingerprinting Localization Soluti...
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03/09/2018