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Found 4 papers, 1 papers with code
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.
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.
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.
RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection
Each iterative unit is a neural computation module comprising of 3 sub-modules: the likelihood module, the encoder module, and the predictor module.
