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Found 28 papers, 8 papers with code
Hybrid Beamforming for RIS-Assisted Multiuser Fluid Antenna Systems
Recent advances in reconfigurable antennas have led to the new concept of the fluid antenna system (FAS) for shape and position flexibility, as another degree of freedom for wireless communication enhancement.
Beyond Unimodal Boundaries: Generative Recommendation with Multimodal Semantics
Generative recommendation (GR) has become a powerful paradigm in recommendation systems that implicitly links modality and semantics to item representation, in contrast to previous methods that relied on non-semantic item identifiers in autoregressive models.
Amplitude-Domain Reflection Modulation for Active RIS-Assisted Wireless Communications
In this paper, we propose a novel active reconfigurable intelligent surface (RIS)-assisted amplitude-domain reflection modulation (ADRM) transmission scheme, termed as ARIS-ADRM.
Joint Sparse Graph for Enhanced MIMO-AFDM Receiver Design
This paper is devoted to enhanced receiver design for multiple input and multiple output AFDM (MIMO-AFDM) systems.
Single Sparse Graph Enhanced Expectation Propagation Algorithm Design for Uplink MIMO-SCMA
Sparse code multiple access (SCMA) and multiple input multiple output (MIMO) are considered as two efficient techniques to provide both massive connectivity and high spectrum efficiency for future machine-type wireless networks.
Advanced Nonlinear SCMA Codebook Design Based on Lattice Constellations
This mapping is referred to as nonlinear mapping (codebook) in this paper.
NetworkGym: Reinforcement Learning Environments for Multi-Access Traffic Management in Network Simulation
Mobile devices such as smartphones, laptops, and tablets can often connect to multiple access networks (e. g., Wi-Fi, LTE, and 5G) simultaneously.
On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks
Heterophily, or the tendency of connected nodes in networks to have different class labels or dissimilar features, has been identified as challenging for many Graph Neural Network (GNN) models.
Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning
Graph machine learning has made significant strides in recent years, yet the integration of visual information with graph structure and its potential for improving performance in downstream tasks remains an underexplored area.
Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation
Large language models (LLMs) have significantly advanced various natural language processing tasks, but deploying them remains computationally expensive.
LinkGPT: Teaching Large Language Models To Predict Missing Links
To address the efficiency challenges at inference time, we introduce a retrieval-reranking scheme.
BClean: A Bayesian Data Cleaning System
By evaluating on both real-world and synthetic datasets, we demonstrate that BClean is capable of achieving an F-measure of up to 0. 9 in data cleaning, outperforming existing Bayesian methods by 2% and other data cleaning methods by 15%.
Spatial Attention-based Distribution Integration Network for Human Pose Estimation
The RFM incorporates a dilated residual block and attention mechanism to expand receptive fields while enhancing sensitivity to spatial information.
TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning
How can we enhance the node features acquired from Pretrained Models (PMs) to better suit downstream graph learning tasks?
Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks
The proposed algorithm achieved excellent generalization results against an external dataset with sensitivity of 77% at a false positive rate of 7. 6.
Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices
While Graph Neural Networks (GNNs) are remarkably successful in a variety of high-impact applications, we demonstrate that, in link prediction, the common practices of including the edges being predicted in the graph at training and/or test have outsized impact on the performance of low-degree nodes.
Touch and Go: Learning from Human-Collected Vision and Touch
The ability to associate touch with sight is essential for tasks that require physically interacting with objects in the world.
Weight-based Channel-model Matrix Framework provides a reasonable solution for EEG-based cross-dataset emotion recognition
Cross-dataset emotion recognition as an extremely challenging task in the field of EEG-based affective computing is influenced by many factors, which makes the universal models yield unsatisfactory results.
CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment
Network alignment, or the task of finding corresponding nodes in different networks, is an important problem formulation in many application domains.
Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection
In stripping chest X-ray images of known confounding variables by lung field segmentation, along with suppression of signal noise from the bone structure we can train a highly accurate deep learning lung nodule detection algorithm with outstanding generalization accuracy of 89% to nodule samples in unseen data.
ContinuityLearner: Geometric Continuity Feature Learning for Lane Segmentation
Lane segmentation is a challenging issue in autonomous driving system designing because lane marks show weak textural consistency due to occlusion or extreme illumination but strong geometric continuity in traffic images, from which general convolution neural networks (CNNs) are not capable of learning semantic objects.
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding
While most network embedding techniques model the relative positions of nodes in a network, recently there has been significant interest in structural embeddings that model node role equivalences, irrespective of their distances to any specific nodes.
NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge Bases
Codifying commonsense knowledge in machines is a longstanding goal of artificial intelligence.
Modeling Long-Term and Short-Term Interests with Parallel Attentions for Session-based Recommendation
First, most of the attention-based methods only simply utilize the last clicked item to represent the user's short-term interest ignoring the temporal information and behavior context, which may fail to capture the recent preference of users comprehensively.
MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis
The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications.
Self-Supervised Learning of Depth and Ego-motion with Differentiable Bundle Adjustment
Learning to predict scene depth and camera motion from RGB inputs only is a challenging task.
Structure-Attentioned Memory Network for Monocular Depth Estimation
To this end, we introduce a new Structure-Oriented Memory (SOM) module to learn and memorize the structure-specific information between RGB image domain and the depth domain.
Ranked #57 on
Monocular Depth Estimation
on KITTI Eigen split
Learning Object-specific Distance from a Monocular Image
Environment perception, including object detection and distance estimation, is one of the most crucial tasks for autonomous driving.
