32 citations · 204 across the 33 of their papers we have counts for
36 papers
Pruning vs Quantization: Which is Better?
Andrey Kuzmin, Markus Nagel, Mart van Baalen +2
Neural network pruning and quantization techniques are almost as old as neural networks themselves. However, to date only ad-hoc comparisons between the two have been published. In…
Spatially Sparse Precoding in Wideband Hybrid Terahertz Massive MIMO Systems
Jiabao Gao, Caijun Zhong, Geoffrey Ye Li +2
In terahertz (THz) massive multiple-input multiple-output (MIMO) systems, the combination of huge bandwidth and massive antennas results in severe beam split, thus making the conve…
Beyond Codebook-Based Analog Beamforming at mmWave: Compressed Sensing and Machine Learning Methods
Hamed Pezeshki, Fabio Valerio Massoli, Arash Behboodi +6
Analog beamforming is the predominant approach for millimeter wave (mmWave) communication given its favorable characteristics for limited-resource devices. In this work, we aim at…
A PAC-Bayesian Generalization Bound for Equivariant Networks
Arash Behboodi, Gabriele Cesa, Taco Cohen
Equivariant networks capture the inductive bias about the symmetry of the learning task by building those symmetries into the model. In this paper, we study how equivariance relate…
Multi-Modal Beam Prediction Challenge 2022: Towards Generalization
Gouranga Charan, Umut Demirhan, João Morais +3
Beam management is a challenging task for millimeter wave (mmWave) and sub-terahertz communication systems, especially in scenarios with highly-mobile users. Leveraging external se…
Learning Perturbations for Soft-Output Linear MIMO Demappers
Daniel E. Worrall, Markus Peschl, Arash Behboodi +1
Tree-based demappers for multiple-input multiple-output (MIMO) detection such as the sphere decoder can achieve near-optimal performance but incur high computational cost due to th…