Search Results for author:
Found 35 papers, 11 papers with code
A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide
Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and applications, and thus investigation of deep learning for HOIs has become a valuable agenda for the data mining and machine learning communities.
HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs
Based on the generative SSL task, we propose a hypergraph SSL method, HypeBoy.
FlowerFormer: Empowering Neural Architecture Encoding using a Flow-aware Graph Transformer
The success of a specific neural network architecture is closely tied to the dataset and task it tackles; there is no one-size-fits-all solution.
SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning
In this paper, we propose SLADE (Self-supervised Learning for Anomaly Detection in Edge Streams) for rapid detection of dynamic anomalies in edge streams, without relying on labels.
Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective
Second, how does A-X dependence affect GNNs?
Channel Estimation for RIS-Aided mmWave MU-MIMO Systems with Hybrid Beamforming Structures
Channel estimation is one of the key challenges for the deployment of reconfigurable intelligence surface (RIS)-aided communication systems.
Learning-based Ecological Adaptive Cruise Control of Autonomous Electric Vehicles: A Comparison of ADP, DQN and DDPG Approaches
For performance demonstrations, the HWFET, US06, and WLTP Class 3b driving cycles were used to simulate the front vehicle, and the energy consumptions of the host vehicle and front vehicle were compared.
Classification of Edge-dependent Labels of Nodes in Hypergraphs
Interestingly, many real-world systems modeled as hypergraphs contain edge-dependent node labels, i. e., node labels that vary depending on hyperedges.
User-friendly Image Editing with Minimal Text Input: Leveraging Captioning and Injection Techniques
From the users' standpoint, prompt engineering is a labor-intensive process, and users prefer to provide a target word for editing instead of a full sentence.
Diffusion Model for Dense Matching
The objective for establishing dense correspondence between paired images consists of two terms: a data term and a prior term.
Semantic-Preserving Augmentation for Robust Image-Text Retrieval
Image text retrieval is a task to search for the proper textual descriptions of the visual world and vice versa.
RIS-Enabled and Access-Point-Free Simultaneous Radio Localization and Mapping
In the upcoming sixth generation (6G) of wireless communication systems, reconfigurable intelligent surfaces~(RISs) are regarded as one of the promising technological enablers, which can provide programmable signal propagation.
MmWave Mapping and SLAM for 5G and Beyond
We distinguish the different types of sensing problems and then focus on mapping and SLAM as running examples.
Controllable Style Transfer via Test-time Training of Implicit Neural Representation
We propose a controllable style transfer framework based on Implicit Neural Representation that pixel-wisely controls the stylized output via test-time training.
LANIT: Language-Driven Image-to-Image Translation for Unlabeled Data
Existing techniques for image-to-image translation commonly have suffered from two critical problems: heavy reliance on per-sample domain annotation and/or inability of handling multiple attributes per image.
REVECA -- Rich Encoder-decoder framework for Video Event CAptioner
We designed a Rich Encoder-decoder framework for Video Event CAptioner (REVECA) that utilizes spatial and temporal information from the video to generate a caption for the corresponding the event boundary.
PMBM-based SLAM Filters in 5G mmWave Vehicular Networks
Secondly, the Poisson multi-Bernoulli (PMB) SLAM filter is based on the standard reduction from PMBM to PMB, but involves a novel interpretation based on auxiliary variables and a relation to Bethe free energy.
Deep Translation Prior: Test-time Training for Photorealistic Style Transfer
Recent techniques to solve photorealistic style transfer within deep convolutional neural networks (CNNs) generally require intensive training from large-scale datasets, thus having limited applicability and poor generalization ability to unseen images or styles.
BLOOM-Net: Blockwise Optimization for Masking Networks Toward Scalable and Efficient Speech Enhancement
In this paper, we present a blockwise optimization method for masking-based networks (BLOOM-Net) for training scalable speech enhancement networks.
A Computationally Efficient EK-PMBM Filter for Bistatic mmWave Radio SLAM
Millimeter wave (mmWave) signals are useful for simultaneous localization and mapping (SLAM), due to their inherent geometric connection to the propagation environment and the propagation channel.
Atomic Norm Minimization-based Low-Overhead Channel Estimation for RIS-aided MIMO Systems
Pilot signals received during beam training are compiled into one matrix to define the atomic norm of the channel for RIS-aided MIMO systems.
Location-aware Channel Estimation for RIS-aided mmWave MIMO Systems via Atomic Norm Minimization
The beam training overhead at the base station (BS) is reduced by the direct beam steering towards the RIS with the location of the BS and the RIS.
Personalized Federated Learning over non-IID Data for Indoor Localization
Localization and tracking of objects using data-driven methods is a popular topic due to the complexity in characterizing the physics of wireless channel propagation models.
Dirichlet process approach for radio-based simultaneous localization and mapping
By comparing the results with the SLAM based on the Rao-Blackwellized probability hypothesis density filter, we confirm a slight drop in SLAM performance, but as a result, we validate that it has a significant gain in computational complexity.
Cooperative mmWave PHD-SLAM with Moving Scatterers
Using the multiple-model (MM) probability hypothesis density (PHD) filter, millimeter wave (mmWave) radio simultaneous localization and mapping (SLAM) in vehicular scenarios is susceptible to movements of objects, in particular vehicles driving in parallel with the ego vehicle.
Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation
In addition, since the compact personalized models can outperform larger general-purpose models, we claim that the proposed method performs model compression with no loss of denoising performance.
Personalized Speech Enhancement through Self-Supervised Data Augmentation and Purification
Training personalized speech enhancement models is innately a no-shot learning problem due to privacy constraints and limited access to noise-free speech from the target user.
Deep Learning-based Beam Tracking for Millimeter-wave Communications under Mobility
In this paper, we propose a deep learning-based beam tracking method for millimeter-wave (mmWave)communications.
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community.
Online Exemplar Fine-Tuning for Image-to-Image Translation
Existing techniques to solve exemplar-based image-to-image translation within deep convolutional neural networks (CNNs) generally require a training phase to optimize the network parameters on domain-specific and task-specific benchmarks, thus having limited applicability and generalization ability.
Exploiting Diffuse Multipath in 5G SLAM
5G millimeter wave (mmWave) signals can be used to jointly localize the receiver and map the propagation environment in vehicular networks, which is a typical simultaneous localization and mapping (SLAM) problem.
Boosted Locality Sensitive Hashing: Discriminative Binary Codes for Source Separation
Speech enhancement tasks have seen significant improvements with the advance of deep learning technology, but with the cost of increased computational complexity.
Nearest Neighbor Search-Based Bitwise Source Separation Using Discriminant Winner-Take-All Hashing
We propose an iteration-free source separation algorithm based on Winner-Take-All (WTA) hash codes, which is a faster, yet accurate alternative to a complex machine learning model for single-channel source separation in a resource-constrained environment.
5G mmWave Cooperative Positioning and Mapping using Multi-Model PHD Filter and Map Fusion
5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station (BS) and vehicles are equipped with large antenna arrays.
Incremental Binarization On Recurrent Neural Networks For Single-Channel Source Separation
Our experiments show that the proposed BGRU method produces source separation results greater than that of a real-valued fully connected network, with 11-12 dB mean Signal-to-Distortion Ratio (SDR).
