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Found 11 papers, 3 papers with code
Accelerating Convergence of Stein Variational Gradient Descent via Deep Unfolding
Stein variational gradient descent (SVGD) is a prominent particle-based variational inference method used for sampling a target distribution.
Convergence Acceleration of Markov Chain Monte Carlo-based Gradient Descent by Deep Unfolding
This study proposes a trainable sampling-based solver for combinatorial optimization problems (COPs) using a deep-learning technique called deep unfolding.
Deep Unfolded Simulated Bifurcation for Massive MIMO Signal Detection
Recently, various MIMO signal detectors based on deep learning techniques and quantum(-inspired) algorithms have been proposed to improve the detection performance compared with conventional detectors.
Hubbard-Stratonovich Detector for Simple Trainable MIMO Signal Detection
Although DU has a lesser number of trainable parameters than conventional deep neural networks, the computational complexities related to training and execution have been problematic because DU-based MIMO detectors usually utilize matrix inversion to improve their detection performance.
Convergence Acceleration via Chebyshev Step: Plausible Interpretation of Deep-Unfolded Gradient Descent
In the second half of the study, %we apply the theory of Chebyshev steps and Chebyshev-periodical successive over-relaxation (Chebyshev-PSOR) is proposed for accelerating linear/nonlinear fixed-point iterations.
Deep Unfolded Multicast Beamforming
Multicast beamforming is a promising technique for multicast communication.
Theoretical Interpretation of Learned Step Size in Deep-Unfolded Gradient Descent
In this paper, we provide a theoretical interpretation of the learned step size of deep-unfolded gradient descent (DUGD).
Trainable Projected Gradient Detector for Sparsely Spread Code Division Multiple Access
In this paper, we propose a novel trainable multiuser detector called sparse trainable projected gradient (STPG) detector, which is based on the notion of deep unfolding.
Complex Trainable ISTA for Linear and Nonlinear Inverse Problems
Complex-field signal recovery problems from noisy linear/nonlinear measurements appear in many areas of signal processing and wireless communications.
Trainable Projected Gradient Detector for Massive Overloaded MIMO Channels: Data-driven Tuning Approach
This paper presents a deep learning-aided iterative detection algorithm for massive overloaded multiple-input multiple-output (MIMO) systems where the number of transmit antennas $n$ is larger than that of receive antennas $m$.
Deep Learning-Aided Projected Gradient Detector for Massive Overloaded MIMO Channels
The paper presents a deep learning-aided iterative detection algorithm for massive overloaded MIMO systems.
