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Found 11 papers, 0 papers with code
AutoHLS: Learning to Accelerate Design Space Exploration for HLS Designs
High-level synthesis (HLS) is a design flow that leverages modern language features and flexibility, such as complex data structures, inheritance, templates, etc., to prototype hardware designs rapidly.
Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?
In practical applications, such a worst-case guarantee may be overkill: practical attackers may lack exact knowledge of (nearly all of) the private data, and our data set might be easier to defend, in some sense, than the worst-case data set.
Stabilizing Subject Transfer in EEG Classification with Divergence Estimation
Classification models for electroencephalogram (EEG) data show a large decrease in performance when evaluated on unseen test sub jects.
Mobility, Communication and Computation Aware Federated Learning for Internet of Vehicles
To take the high mobility of vehicles into account, we consider the delay as a learning parameter and restrict it to be less than a tolerable threshold.
Learning to Learn Quantum Turbo Detection
This paper investigates a turbo receiver employing a variational quantum circuit (VQC).
Variational Quantum Compressed Sensing for Joint User and Channel State Acquisition in Grant-Free Device Access Systems
This paper introduces a new quantum computing framework integrated with a two-step compressed sensing technique, applied to a joint channel estimation and user identification problem.
DNN-assisted optical geometric constellation shaped PSK modulation for PAM4-to-QPSK format conversion gateway node
An optical gateway to convert four-level pulse amplitude modulation to quadrature phase shift keying modulation format having shaping gain was proposed for flexible intensity to phase mapping which exploits non-uniform phase noise.
Huffman-Coded Sphere Shaping for Extended-Reach Single-Span Links
Huffman-coded sphere shaping (HCSS) is an algorithm for finite-length probabilistic constellation shaping, which provides nearly optimal energy efficiency at low implementation complexity.
Huffman-coded Sphere Shaping and Distribution Matching Algorithms via Lookup Tables
In this paper, we study amplitude shaping schemes for the probabilistic amplitude shaping (PAS) framework as well as algorithms for constant-composition distribution matching (CCDM).
Analysis of Nonlinear Fiber Interactions for Finite-Length Constant-Composition Sequences
This temporal property results in weaker nonlinear interactions, and thus higher SNR, for short CC sequences.
Neural Turbo Equalization: Deep Learning for Fiber-Optic Nonlinearity Compensation
Recently, data-driven approaches motivated by modern deep learning have been applied to optical communications in place of traditional model-based counterparts.
