Search Results for author: Lizhong Zheng

Found 20 papers, 2 papers with code

Learning at the Speed of Wireless: Online Real-Time Learning for AI-Enabled MIMO in NextG

no code implementations5 Mar 2024 Jiarui Xu, Shashank Jere, Yifei Song, Yi-Hung Kao, Lizhong Zheng, Lingjia Liu

At the air interface, multiple-input multiple-output (MIMO) and its variants such as multi-user MIMO (MU-MIMO) and massive/full-dimension MIMO have been key enablers across successive generations of cellular networks with evolving complexity and design challenges.

Scheduling

Operator SVD with Neural Networks via Nested Low-Rank Approximation

1 code implementation6 Feb 2024 J. Jon Ryu, Xiangxiang Xu, H. S. Melihcan Erol, Yuheng Bu, Lizhong Zheng, Gregory W. Wornell

Computing eigenvalue decomposition (EVD) of a given linear operator, or finding its leading eigenvalues and eigenfunctions, is a fundamental task in many machine learning and scientific computing problems.

2D-RC: Two-Dimensional Neural Network Approach for OTFS Symbol Detection

no code implementations14 Nov 2023 Jiarui Xu, Karim Said, Lizhong Zheng, Lingjia Liu

Orthogonal time frequency space (OTFS) is a promising modulation scheme for wireless communication in high-mobility scenarios.

Towards Explainable Machine Learning: The Effectiveness of Reservoir Computing in Wireless Receive Processing

no code implementations8 Oct 2023 Shashank Jere, Karim Said, Lizhong Zheng, Lingjia Liu

With this groundwork, we incorporate the available domain knowledge in the form of the statistics of the wireless channel directly into the weights of the ESN model.

A Geometric Framework for Neural Feature Learning

1 code implementation18 Sep 2023 Xiangxiang Xu, Lizhong Zheng

We present a novel framework for learning system design based on neural feature extractors.

Density Ratio Estimation

Universal Approximation of Linear Time-Invariant (LTI) Systems through RNNs: Power of Randomness in Reservoir Computing

no code implementations4 Aug 2023 Shashank Jere, Lizhong Zheng, Karim Said, Lingjia Liu

Reservoir computing (RC), a special RNN where the recurrent weights are randomized and left untrained, has been introduced to overcome these issues and has demonstrated superior empirical performance in fields as diverse as natural language processing and wireless communications especially in scenarios where training samples are extremely limited.

Learning to Estimate: A Real-Time Online Learning Framework for MIMO-OFDM Channel Estimation

no code implementations22 May 2023 Lianjun Li, Sai Sree Rayala, Jiarui Xu, Lizhong Zheng, Lingjia Liu

In this paper we introduce StructNet-CE, a novel real-time online learning framework for MIMO-OFDM channel estimation, which only utilizes over-the-air (OTA) pilot symbols for online training and converges within one OFDM subframe.

Binary Classification

Kernel Subspace and Feature Extraction

no code implementations4 Jan 2023 Xiangxiang Xu, Lizhong Zheng

We study kernel methods in machine learning from the perspective of feature subspace.

An Information-Theoretic Approach to Transferability in Task Transfer Learning

no code implementations20 Dec 2022 Yajie Bao, Yang Li, Shao-Lun Huang, Lin Zhang, Lizhong Zheng, Amir Zamir, Leonidas Guibas

Task transfer learning is a popular technique in image processing applications that uses pre-trained models to reduce the supervision cost of related tasks.

Model Selection Transfer Learning

On the Semi-supervised Expectation Maximization

no code implementations1 Nov 2022 Erixhen Sula, Lizhong Zheng

We focus on a semi-supervised case to learn the model from labeled and unlabeled samples.

Detect to Learn: Structure Learning with Attention and Decision Feedback for MIMO-OFDM Receive Processing

no code implementations17 Aug 2022 Jiarui Xu, Lianjun Li, Lizhong Zheng, Lingjia Liu

The DF mechanism further enhances detection performance by dynamically tracking the channel changes through detected data symbols.

A Mathematical Framework for Quantifying Transferability in Multi-source Transfer Learning

no code implementations NeurIPS 2021 Xinyi Tong, Xiangxiang Xu, Shao-Lun Huang, Lizhong Zheng

Current transfer learning algorithm designs mainly focus on the similarities between source and target tasks, while the impacts of the sample sizes of these tasks are often not sufficiently addressed.

Image Classification Transfer Learning

RC-Struct: A Structure-based Neural Network Approach for MIMO-OFDM Detection

no code implementations3 Oct 2021 Jiarui Xu, Zhou Zhou, Lianjun Li, Lizhong Zheng, Lingjia Liu

The binary classifier enables the efficient utilization of the precious online training symbols and allows an easy extension to high-order modulations without a substantial increase in complexity.

Learning with Knowledge of Structure: A Neural Network-Based Approach for MIMO-OFDM Detection

no code implementations1 Dec 2020 Zhou Zhou, Shashank Jere, Lizhong Zheng, Lingjia Liu

In this paper, we explore neural network-based strategies for performing symbol detection in a MIMO-OFDM system.

Binary Classification

Learning for Integer-Constrained Optimization through Neural Networks with Limited Training

no code implementations NeurIPS Workshop LMCA 2020 Zhou Zhou, Shashank Jere, Lizhong Zheng, Lingjia Liu

In this paper, we investigate a neural network-based learning approach towards solving an integer-constrained programming problem using very limited training.

On Universal Features for High-Dimensional Learning and Inference

no code implementations20 Nov 2019 Shao-Lun Huang, Anuran Makur, Gregory W. Wornell, Lizhong Zheng

We consider the problem of identifying universal low-dimensional features from high-dimensional data for inference tasks in settings involving learning.

Collaborative Filtering regression +1

An Information-theoretic Approach to Unsupervised Feature Selection for High-Dimensional Data

no code implementations8 Oct 2019 Shao-Lun Huang, Xiangxiang Xu, Lizhong Zheng

In this paper, we propose an information-theoretic approach to design the functional representations to extract the hidden common structure shared by a set of random variables.

feature selection

An Information Theoretic Interpretation to Deep Neural Networks

no code implementations16 May 2019 Shao-Lun Huang, Xiangxiang Xu, Lizhong Zheng, Gregory W. Wornell

It is commonly believed that the hidden layers of deep neural networks (DNNs) attempt to extract informative features for learning tasks.

feature selection

Probabilistic Clustering Using Maximal Matrix Norm Couplings

no code implementations10 Oct 2018 David Qiu, Anuran Makur, Lizhong Zheng

In this paper, we present a local information theoretic approach to explicitly learn probabilistic clustering of a discrete random variable.

Clustering Sentence +1

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