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Found 34 papers, 6 papers with code
Adaptive Intra-Class Variation Contrastive Learning for Unsupervised Person Re-Identification
The memory dictionary-based contrastive learning method has achieved remarkable results in the field of unsupervised person Re-ID.
Document Set Expansion with Positive-Unlabelled Learning Using Intractable Density Estimation
The Document Set Expansion (DSE) task involves identifying relevant documents from large collections based on a limited set of example documents.
Hierarchical Query Classification in E-commerce Search
E-commerce platforms typically store and structure product information and search data in a hierarchy.
Document Set Expansion with Positive-Unlabeled Learning: A Density Estimation-based Approach
Previous work shows that PU learning is a promising method for this task.
A Novel Field-Free SOT Magnetic Tunnel Junction With Local VCMA-Induced Switching
By integrating the local voltage-controlled magnetic anisotropy (VCMA) effect, Dzyaloshinskii-Moriya interaction (DMI) effect, and spin-orbit torque (SOT) effect, we propose a novel device structure for field-free magnetic tunnel junction (MTJ).
Holographic Imaging with XL-MIMO and RIS: Illumination and Reflection Design
This paper addresses a near-field imaging problem utilizing extremely large-scale multiple-input multiple-output (XL-MIMO) antennas and reconfigurable intelligent surfaces (RISs) already in place for wireless communications.
Near-Field Wideband Secure Communications: An Analog Beamfocusing Approach
In the rapidly advancing landscape of 6G, characterized by ultra-high-speed wideband transmission in millimeter-wave and terahertz bands, our paper addresses the pivotal task of enhancing physical layer security (PLS) within near-field wideband communications.
Label-free Node Classification on Graphs with Large Language Models (LLMS)
In light of these observations, this work introduces a label-free node classification on graphs with LLMs pipeline, LLM-GNN.
End-to-End Beam Retrieval for Multi-Hop Question Answering
This approach models the multi-hop retrieval process in an end-to-end manner by jointly optimizing an encoder and two classification heads across all hops.
Ranked #1 on
Question Answering
on HotpotQA
A Robust Integrated Multi-Strategy Bus Control System via Deep Reinforcement Learning
An efficient urban bus control system has the potential to significantly reduce travel delays and streamline the allocation of transportation resources, thereby offering enhanced and user-friendly transit services to passengers.
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation.
Prompt Engineering for Healthcare: Methodologies and Applications
Prompt engineering is a critical technique in the field of natural language processing that involves designing and optimizing the prompts used to input information into models, aiming to enhance their performance on specific tasks.
Hybrid RIS-Assisted MIMO Dual-Function Radar-Communication System
Dual-function radar-communication (DFRC) technology is emerging in next-generation wireless systems.
Hardware Implementation of Task-based Quantization in Multi-user Signal Recovery
In this work, we apply task-based quantization to multi-user signal recovery and present a hardware prototype implementation.
Near-Field Sparse Channel Representation and Estimation in 6G Wireless Communications
In this case, the spherical wave assumption which takes into account both the user angle and distance is more accurate than the conventional planar one that is only related to the user angle.
Near-Field Channel Estimation for Extremely Large-Scale Array Communications: A model-based deep learning approach
In particular, we first formulate the XL-MIMO near-field channel estimation task as a compressed sensing problem using the spatial gridding-based sparsifying dictionary, and then solve the resulting problem by applying the Learning Iterative Shrinkage and Thresholding Algorithm (LISTA).
Near-field Localization with Dynamic Metasurface Antennas
We use a direct positioning estimation method based on curvature-of-arrival of the impinging wavefront to obtain the user location, and characterize the effects of DMA tuning on the estimation accuracy.
Beamforming Design for Integrated Sensing and Wireless Power Transfer Systems
This letter proposes a new concept of integrated sensing and wireless power transfer (ISWPT), where radar sensing and wireless power transfer functions are integrated into one hardware platform.
Joint Microstrip Selection and Beamforming Design for MmWave Systems with Dynamic Metasurface Antennas
Dynamic metasurface antennas (DMAs) provide a new paradigm to realize large-scale antenna arrays for future wireless systems.
Physics Embedded Machine Learning for Electromagnetic Data Imaging
To benefit from prior knowledge in big data and the theoretical constraint of physical laws, physics embedded ML methods for EM imaging have become the focus of a large body of recent work.
BiTAT: Neural Network Binarization with Task-dependent Aggregated Transformation
Neural network quantization aims to transform high-precision weights and activations of a given neural network into low-precision weights/activations for reduced memory usage and computation, while preserving the performance of the original model.
6G Wireless Communications: From Far-field Beam Steering to Near-field Beam Focusing
As a consequence, it is expected that some portion of future 6G wireless communications may take place in the radiating near-field (Fresnel) region, in addition to the far-field operation as in current wireless technologies.
Jointly Learned Symbol Detection and Signal Reflection in RIS-Aided Multi-user MIMO Systems
Reconfigurable Intelligent Surfaces (RISs) are regarded as a key technology for future wireless communications, enabling programmable radio propagation environments.
Channel Estimation with Simultaneous Reflecting and Sensing Reconfigurable Intelligent Metasurfaces
As an indicative application of HRISs, we formulate and solve the individual channels identification problem for the uplink of multi-user HRIS-empowered systems.
Task-Based Graph Signal Compression
The common framework for graph signal compression is based on sampling, resulting in a set of continuous-amplitude samples, which in turn have to be quantized into a finite bit representation.
Near-Field Wireless Power Transfer with Dynamic Metasurface Antennas
Radio frequency wireless power transfer (WPT) enables charging low-power mobile devices without relying on wired infrastructure.
Near-field Wireless Power Transfer for 6G Internet-of-Everything Mobile Networks: Opportunities and Challenges
Radiating wireless power transfer (WPT) brings forth the possibility to cost-efficiently charge wireless devices without requiring a wiring infrastructure.
Learning to Sample: Data-Driven Sampling and Reconstruction of FRI Signals
Typically, Fourier samples of the FRI signals are used for reconstruction.
DualGraph: A Graph-Based Method for Reasoning About Label Noise
Unreliable labels derived from large-scale dataset prevent neural networks from fully exploring the data.
Beam Focusing for Near-Field Multi-User MIMO Communications
As the ability to achieve beam focusing is dictated by the transmit antenna, we study near-field signaling considering different antenna structures, including fully-digital architectures, hybrid phase shifter-based precoders, and the emerging dynamic metasurface antenna (DMA) architecture for massive MIMO arrays.
UserReg: A Simple but Strong Model for Rating Prediction
We compare the effectiveness of UserReg with three linear CF models that are widely-used as baselines, and with a set of recently proposed complex models that are based on deep learning or graph techniques.
cross-modal knowledge enhancement mechanism for few-shot learning
After that, with the message-passing mechanism, CKEM selects and transfers relevant knowledge from external semantic knowledge bank to original visual-based class representations in Knowledge Fusion Model(KFM).
Robustness and Reliability of Gender Bias Assessment in Word Embeddings: The Role of Base Pairs
It has been shown that word embeddings can exhibit gender bias, and various methods have been proposed to quantify this.
On Data Augmentation for Extreme Multi-label Classification
Our contributions are two-factored: (1) we introduce a new state-of-the-art classifier that uses label attention with RoBERTa and combine it with our augmentation framework for further improvement; (2) we present a broad study on how effective are different augmentation methods in the XMC task.
