Search Results for author:
Found 26 papers, 4 papers with code
Vision-Assisted Digital Twin Creation for mmWave Beam Management
In the context of communication networks, digital twin technology provides a means to replicate the radio frequency (RF) propagation environment as well as the system behaviour, allowing for a way to optimize the performance of a deployed system based on simulations.
Algebraic Topological Networks via the Persistent Local Homology Sheaf
The intermediate features of our networks live in these vector spaces and we leverage the associated sheaf Laplacian to construct more complex linear messages between them.
Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach
Lattice reduction is a combinatorial optimization problem aimed at finding the most orthogonal basis in a given lattice.
Transformer-Based Neural Surrogate for Link-Level Path Loss Prediction from Variable-Sized Maps
The transformer model attends to the regions that are relevant for path loss prediction and, therefore, scales efficiently to maps of different size.
Pruning vs Quantization: Which is Better?
We provide an extensive comparison between the two techniques for compressing deep neural networks.
Beyond Codebook-Based Analog Beamforming at mmWave: Compressed Sensing and Machine Learning Methods
Analog beamforming is the predominant approach for millimeter wave (mmWave) communication given its favorable characteristics for limited-resource devices.
A PAC-Bayesian Generalization Bound for Equivariant Networks
Equivariant networks capture the inductive bias about the symmetry of the learning task by building those symmetries into the model.
Multi-Modal Beam Prediction Challenge 2022: Towards Generalization
In this paper, along with the detailed descriptions of the problem statement and the development dataset, we provide a baseline solution that utilizes the user position data to predict the optimal beam indices.
Quantized Sparse Weight Decomposition for Neural Network Compression
In this paper, we introduce a novel method of neural network weight compression.
Equivariant Priors for Compressed Sensing with Unknown Orientation
In compressed sensing, the goal is to reconstruct the signal from an underdetermined system of linear measurements.
Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?
Millimeter-wave (mmWave) communication systems rely on narrow beams for achieving sufficient receive signal power.
MIMO-GAN: Generative MIMO Channel Modeling
We propose generative channel modeling to learn statistical channel models from channel input-output measurements.
Neural RF SLAM for unsupervised positioning and mapping with channel state information
The model is based on an encoder-decoder architecture.
Generalization Error Bounds for Iterative Recovery Algorithms Unfolded as Neural Networks
Motivated by the learned iterative soft thresholding algorithm (LISTA), we introduce a general class of neural networks suitable for sparse reconstruction from few linear measurements.
Quantized sparse PCA for neural network weight compression
In this paper, we introduce a novel method of weight compression.
Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking
Through extensive experiments on CDL-B channel model, we show that the HKF can be used for tracking the channel over a wide range of Doppler values, matching Kalman filter performance with genie Doppler information.
Compressive Sensing and Neural Networks from a Statistical Learning Perspective
The neural networks in this class are obtained by unfolding iterations of ISTA and learning some of the weights.
Gradient $\ell_1$ Regularization for Quantization Robustness
We analyze the effect of quantizing weights and activations of neural networks on their loss and derive a simple regularization scheme that improves robustness against post-training quantization.
Adversarial Risk Bounds for Neural Networks through Sparsity based Compression
To study how robustness generalizes, recent works assume that the inputs have bounded $\ell_2$-norm in order to bound the adversarial risk for $\ell_\infty$ attacks with no explicit dimension dependence.
On the Trajectory of Stochastic Gradient Descent in the Information Plane
Studying the evolution of information theoretic quantities during Stochastic Gradient Descent (SGD) learning of Artificial Neural Networks (ANNs) has gained popularity in recent years.
Sensing Matrix Design and Sparse Recovery on the Sphere and the Rotation Group
It is first shown that random sensing matrices, which consists of random samples of Wigner D-functions, satisfy the Restricted Isometry Property (RIP) with a proper preconditioning and can be used for sparse recovery on the rotation group.
Information Theory Information Theory
On the Effect of Low-Rank Weights on Adversarial Robustness of Neural Networks
In this work, this problem is studied through the lens of compression which is captured by the low-rank structure of weight matrices.
Perturbation Analysis of Learning Algorithms: A Unifying Perspective on Generation of Adversarial Examples
The framework can be used to propose novel attacks against learning algorithms for classification and regression tasks under various new constraints with closed form solutions in many instances.
Learning the Localization Function: Machine Learning Approach to Fingerprinting Localization
Considered as a data-driven approach, Fingerprinting Localization Solutions (FPSs) enjoy huge popularity due to their good performance and minimal environment information requirement.
On Generation of Adversarial Examples using Convex Programming
Moreover, this framework is capable of explaining various existing adversarial methods and can be used to derive new algorithms as well.
A Tensor Analysis on Dense Connectivity via Convolutional Arithmetic Circuits
We carry out a tensor analysis on the expressive power inter-connections on convolutional arithmetic circuits (ConvACs) and relate our results to standard convolutional networks.
