activity
20162023
most citedFedIL: Federated Incremental Learning from Decentralized Unlabeled Data with Convergence Analysis

5 citations · 16 across the 6 of their papers we have counts for

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Showing 2023Show all

8 papers · 1 filter

cs.ET2023

Heterogeneous Receptors - Based Molecule Harvesting in MC: Analysis for ISI Mitigation and Energy Efficiency

Xinyu Huang, Yu Huang, Miaowen Wen +2

This paper investigates a spherical transmitter (TX) with a membrane covered by heterogeneous receptors of varying sizes and arbitrary locations for molecular communication (MC), w…

cs.CL20234 cited

Multiview Identifiers Enhanced Generative Retrieval

Yongqi Li, Nan Yang, Liang Wang +2

Instead of simply matching a query to pre-existing passages, generative retrieval generates identifier strings of passages as the retrieval target. At a cost, the identifier must b…

cs.LG2023

Combining Adversaries with Anti-adversaries in Training

Xiaoling Zhou, Nan Yang, Ou Wu

Adversarial training is an effective learning technique to improve the robustness of deep neural networks. In this study, the influence of adversarial training on deep learning mod…

cs.CL202314 cited

Inference with Reference: Lossless Acceleration of Large Language Models

Nan Yang, Tao Ge, Liang Wang +5

We propose LLMA, an LLM accelerator to losslessly speed up Large Language Model (LLM) inference with references. LLMA is motivated by the observation that there are abundant identi…

cs.LG20233 cited

FedMAE: Federated Self-Supervised Learning with One-Block Masked Auto-Encoder

Nan Yang, Xuanyu Chen, Charles Z. Liu +3

Latest federated learning (FL) methods started to focus on how to use unlabeled data in clients for training due to users' privacy concerns, high labeling costs, or lack of experti…

cs.AI20233 cited

Real-time scheduling of renewable power systems through planning-based reinforcement learning

Shaohuai Liu, Jinbo Liu, Weirui Ye +8

The growing renewable energy sources have posed significant challenges to traditional power scheduling. It is difficult for operators to obtain accurate day-ahead forecasts of rene…