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Found 5 papers, 2 papers with code
Towards Large Language Models as Copilots for Theorem Proving in Lean
In this paper, we explore LLMs as copilots that assist humans in proving theorems.
LeanDojo: Theorem Proving with Retrieval-Augmented Language Models
Using this data, we develop ReProver (Retrieval-Augmented Prover): an LLM-based prover augmented with retrieval for selecting premises from a vast math library.
User Pairing and Power Allocation for FTN-based SC-NOMA and MIMO-NOMA Systems Considering User Fairness
As far as we know, this paper is the first solution to the issue of user pairing and power allocation in FTN-based NOMA, which proves the great advantage of the combination of these two state-of-the-art technologies.
For Intelligent and Higher Spectrum Efficiency: A Variable Packing Ratio Transmission System Based on Faster-than-Nyquist and Deep Learning
With the rapid development of various services in wireless communications, spectrum resource has become increasingly valuable.
Receiver Design for Faster-than-Nyquist Signaling: Deep-learning-based Architectures
Additionally, we propose a DL-based joint signal detection and decoding for FTN signaling to replace the complete baseband part in traditional FTN receivers.
