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Found 16 papers, 5 papers with code
Bayes-Optimal Unsupervised Learning for Channel Estimation in Near-Field Holographic MIMO
In this paper, we address the fundamental challenge of designing a low-complexity Bayes-optimal channel estimator in near-field HMIMO systems operating in unknown EM environments.
Learning Bayes-Optimal Channel Estimation for Holographic MIMO in Unknown EM Environments
Holographic MIMO (HMIMO) has recently been recognized as a promising enabler for future 6G systems through the use of an ultra-massive number of antennas in a compact space to exploit the propagation characteristics of the electromagnetic (EM) channel.
Atom-Motif Contrastive Transformer for Molecular Property Prediction
As motifs in a molecule are significant patterns that are of great importance for determining molecular properties (e. g., toxicity and solubility), overlooking motif interactions inevitably hinders the effectiveness of MPP.
AI-Native Transceiver Design for Near-Field Ultra-Massive MIMO: Principles and Techniques
Ultra-massive multiple-input multiple-output (UMMIMO) is a cutting-edge technology that promises to revolutionize wireless networks by providing an unprecedentedly high spectral and energy efficiency.
Task-Oriented Communication with Out-of-Distribution Detection: An Information Bottleneck Framework
Task-oriented communication is an emerging paradigm for next-generation communication networks, which extracts and transmits task-relevant information, instead of raw data, for downstream applications.
Online SOC Estimation of Lithium-ion Battery Based on Improved Adaptive H Infinity Extended Kalman Filter
Considering that the lithium-ion battery is a time-varying nonlinear system, which needs real-time State of Charge estimation, a joint algorithm of forgetting factor recursive least squares and improved adaptive H Infinity Extended Kalman Filter is proposed for online estimation of model parameters and state of charge.
An Adaptive and Robust Deep Learning Framework for THz Ultra-Massive MIMO Channel Estimation
For practical usage, the proposed framework is further extended to wideband THz UM-MIMO systems with beam squint effect.
Lightweight and Flexible Deep Equilibrium Learning for CSI Feedback in FDD Massive MIMO
In this paper, we propose a lightweight and flexible deep learning-based CSI feedback approach by capitalizing on deep equilibrium models.
Blind Performance Prediction for Deep Learning Based Ultra-Massive MIMO Channel Estimation
Reliability is of paramount importance for the physical layer of wireless systems due to its decisive impact on end-to-end performance.
Hyperspectral Image Classification With Contrastive Graph Convolutional Network
To enhance the feature representation ability, in this paper, a GCN model with contrastive learning is proposed to explore the supervision signals contained in both spectral information and spatial relations, which is termed Contrastive Graph Convolutional Network (ConGCN), for HSI classification.
Hybrid Far- and Near-Field Channel Estimation for THz Ultra-Massive MIMO via Fixed Point Networks
We draw inspirations from fixed point theory to develop an efficient deep learning based channel estimator with adaptive complexity and linear convergence guarantee.
RubCSG at SemEval-2022 Task 5: Ensemble learning for identifying misogynous MEMEs
This work presents an ensemble system based on various uni-modal and bi-modal model architectures developed for the SemEval 2022 Task 5: MAMI-Multimedia Automatic Misogyny Identification.
Federated Learning in ASR: Not as Easy as You Think
We discuss the outcomes of these systems, which both show great similarities and only small improvements, pointing to a need for a deeper understanding of federated learning for speech recognition.
Large-vocabulary Audio-visual Speech Recognition in Noisy Environments
To address the inherent difficulties, we propose a new fusion strategy: a recurrent integration network is trained to fuse the state posteriors of multiple single-modality models, guided by a set of model-based and signal-based stream reliability measures.
Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration
In this paper, we focus on the multi-object tracking (MOT) problem of automatic driving and robot navigation.
Fusing information streams in end-to-end audio-visual speech recognition
While audio-visual speech recognition can significantly improve the recognition rate of end-to-end models in such poor conditions, it is not obvious how to best utilize any available information on acoustic and visual signal quality and reliability in these models.
