15 citations · 53 across the 9 of their papers we have counts for
9 papers
Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners
Xiaojuan Tang, Zilong Zheng, Jiaqi Li +4
The emergent few-shot reasoning capabilities of Large Language Models (LLMs) have excited the natural language and machine learning community over recent years. Despite of numerous…
RMNet: Equivalently Removing Residual Connection from Networks
Fanxu Meng, Hao Cheng, Jiaxin Zhuang +2
Although residual connection enables training very deep neural networks, it is not friendly for online inference due to its multi-branch topology. This encourages many researchers…
Quantum Algorithm for DOA Estimation in Hybrid Massive MIMO
Fanxu Meng
The direction of arrival (DOA) estimation in array signal processing is an important research area. The effectiveness of the direction of arrival greatly determines the performance…
An Empirical Study and Analysis on Open-Set Semi-Supervised Learning
Huixiang Luo, Hao Cheng, Fanxu Meng +4
Pseudo-labeling (PL) and Data Augmentation-based Consistency Training (DACT) are two approaches widely used in Semi-Supervised Learning (SSL) methods. These methods exhibit great p…
A Novel RIS-Assisted Modulation Scheme
Liang Yang, Fanxu Meng, Mazen O. Hasna +1
In this work, in order to achieve higher spectrum efficiency, we propose a reconfigurable intelligent surface (RIS)-assisted multi-user communication uplink system. Different from…
Pruning Filter in Filter
Fanxu Meng, Hao Cheng, Ke Li +4
Pruning has become a very powerful and effective technique to compress and accelerate modern neural networks. Existing pruning methods can be grouped into two categories: filter pr…
Accurate Closed-Form Approximations to Channel Distributions of RIS-Aided Wireless Systems
Liang Yang, Fanxu Meng, Qingqing Wu +2
This paper proposes highly accurate closed-form approximations to channel distributions of two different reconfigurable intelligent surface (RIS)-based wireless system setups, name…
Filter Grafting for Deep Neural Networks: Reason, Method, and Cultivation
Hao Cheng, Fanxu Meng, Ke Li +4
Filter is the key component in modern convolutional neural networks (CNNs). However, since CNNs are usually over-parameterized, a pre-trained network always contain some invalid (u…
Filter Grafting for Deep Neural Networks
Fanxu Meng, Hao Cheng, Ke Li +4
This paper proposes a new learning paradigm called filter grafting, which aims to improve the representation capability of Deep Neural Networks (DNNs). The motivation is that DNNs…