Search Results for author:
Found 64 papers, 27 papers with code
An unsupervised method for MRI recovery: Deep image prior with structured sparsity
Objective: To propose and validate an unsupervised MRI reconstruction method that does not require fully sampled k-space data.
Training-free Heterogeneous Graph Condensation via Data Selection
The second is low efficiency, HGCond follows the existing GC methods designed for homogeneous graphs and leverages the sophisticated optimization paradigm, resulting in a time-consuming condensing procedure.
Large Language Model Enhanced Recommender Systems: Taxonomy, Trend, Application and Future
Large Language Model (LLM) has transformative potential in various domains, including recommender systems (RS).
Motion-Guided Deep Image Prior for Cardiac MRI
Cardiovascular magnetic resonance imaging is a powerful diagnostic tool for assessing cardiac structure and function.
Explainable CTR Prediction via LLM Reasoning
Recommendation Systems have become integral to modern user experiences, but lack transparency in their decision-making processes.
Can LLMs be Good Graph Judger for Knowledge Graph Construction?
We seek to utilize the capacity of LLMs to function as a graph judger, a capability superior to their role only as a predictor for KG construction problems.
A Symmetric Dynamic Learning Framework for Diffeomorphic Medical Image Registration
Diffeomorphic image registration is crucial for various medical imaging applications because it can preserve the topology of the transformation.
Image Registration with Averaging Network and Edge-Based Loss for Low-SNR Cardiac MRI
Methods: To address low SNR encountered in single-shot imaging, especially at low field strengths, we propose a fast deep learning (DL)-based image registration method, called Averaging Morph with Edge Detection (AiM-ED).
Towards Graph Prompt Learning: A Survey and Beyond
Large-scale "pre-train and prompt learning" paradigms have demonstrated remarkable adaptability, enabling broad applications across diverse domains such as question answering, image recognition, and multimodal retrieval.
Mamba Retriever: Utilizing Mamba for Effective and Efficient Dense Retrieval
Experimental results show that (1) on the MS MARCO passage ranking dataset and BEIR, the Mamba Retriever achieves comparable or better effectiveness compared to Transformer-based retrieval models, and the effectiveness grows with the size of the Mamba model; (2) on the long-text LoCoV0 dataset, the Mamba Retriever can extend to longer text length than its pre-trained length after fine-tuning on retrieval task, and it has comparable or better effectiveness compared to other long-text retrieval models; (3) the Mamba Retriever has superior inference speed for long-text retrieval.
SynthVLM: High-Efficiency and High-Quality Synthetic Data for Vision Language Models
Crucially, our method's reliance on purely generated data ensures the preservation of privacy, achieving SoTA performance with just 100k data points (only 18% of the official dataset size).
DisenSemi: Semi-supervised Graph Classification via Disentangled Representation Learning
To ensure the meaningful transfer of knowledge from the unsupervised encoder to the supervised one, we further define an MI-based disentangled consistency regularization between two models and identify the corresponding rationale that aligns well with the current graph classification task.
KeyVideoLLM: Towards Large-scale Video Keyframe Selection
Recently, with the rise of web videos, managing and understanding large-scale video datasets has become increasingly important.
Decoding Matters: Addressing Amplification Bias and Homogeneity Issue for LLM-based Recommendation
However, we find these methods encounter significant challenges: 1) amplification bias -- where standard length normalization inflates scores for items containing tokens with generation probabilities close to 1 (termed ghost tokens), and 2) homogeneity issue -- generating multiple similar or repetitive items for a user.
Composited-Nested-Learning with Data Augmentation for Nested Named Entity Recognition
Compared to Flat Named Entity Recognition (FNER), annotated resources are scarce in the corpus for NNER.
Coil Reweighting to Suppress Motion Artifacts in Real-Time Exercise Cine Imaging
Results: For healthy volunteers, applying CR to RT-Cine images collected at rest did not significantly change the image quality score (p=1).
DEMO: A Statistical Perspective for Efficient Image-Text Matching
Image-text matching has been a long-standing problem, which seeks to connect vision and language through semantic understanding.
BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models
However, general LLMs, which are developed on open-domain data, may lack the domain-specific knowledge essential for tasks in vertical domains, such as legal, medical, etc.
DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment
Most of the existing works focus on improving the representation ability for the contextualized embedding of the [CLS] token and calculate relevance using textual semantic similarity.
Leveraging Large Language Models for Relevance Judgments in Legal Case Retrieval
Nonetheless, the method of employing a general large language model for reliable relevance judgments in legal case retrieval is yet to be thoroughly explored.
Accelerated Real-time Cine and Flow under In-magnet Staged Exercise
Conclusions: This study demonstrated the feasibility of RT ExCMR with inmagnet exercise in healthy subjects and patients.
Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equations
To tackle the above issues, we propose a neural Spatial-Temporal Delay Differential Equation model, namely STDDE.
Fine-grained Prototypical Voting with Heterogeneous Mixup for Semi-supervised 2D-3D Cross-modal Retrieval
In this paper we propose a semi-supervised approach named Fine-grained Prototypcical Voting with Heterogeneous Mixup (FIVE) which maps both 2D and 3D data into a common embedding space for cross-modal retrieval.
Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems
Instead of dismissing the role of incremental learning, we attribute the lack of anticipated performance enhancement to a mismatch between the LLM4Rec architecture and incremental learning: LLM4Rec employs a single adaptation module for learning recommendations, limiting its ability to simultaneously capture long-term and short-term user preferences in the incremental learning context.
E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation
Furthermore, practical ID-based recommendation strategies, reliant on a huge number of unique identities (IDs) to represent users and items, have gained prominence in real-world recommender systems due to their effectiveness and efficiency.
Deep Image prior with StruCtUred Sparsity (DISCUS) for dynamic MRI reconstruction
In the second study, we use data from a realistic late gadolinium enhancement (LGE) phantom to compare DISCUS with compressed sensing (CS) and DIP, and to demonstrate the positive impact of group sparsity.
Surface Coil Intensity Correction for MRI
Modern MRI scanners utilize one or more arrays of small receive-only coils to collect k-space data.
Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting
This paper studies the problem of traffic flow forecasting, which aims to predict future traffic conditions on the basis of road networks and traffic conditions in the past.
A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems
As the focus on Large Language Models (LLMs) in the field of recommendation intensifies, the optimization of LLMs for recommendation purposes (referred to as LLM4Rec) assumes a crucial role in augmenting their effectiveness in providing recommendations.
Motion-robust free-running volumetric cardiovascular MRI
For evaluation, CORe is first compared to traditional compressed sensing (CS), robust regression (RR), and an existing outlier rejection method using two simulation studies.
A Bi-variant Variational Model for Diffeomorphic Image Registration with Relaxed Jacobian Determinant Constraints
In this paper, we propose a new bi-variant diffeomorphic image registration model that introduces a soft constraint on the Jacobian equation $\det(\nabla\bm{\varphi}(\bm{x})) = f(\bm{x}) > 0$.
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification
Although graph neural networks (GNNs) have achieved impressive achievements in graph classification, they often need abundant task-specific labels, which could be extensively costly to acquire.
PastNet: Introducing Physical Inductive Biases for Spatio-temporal Video Prediction
In this paper, we investigate the challenge of spatio-temporal video prediction task, which involves generating future video frames based on historical spatio-temporal observation streams.
Solution existence, uniqueness, and stability of discrete basis sinograms in multispectral CT
In this work, we consider a {\it discrete} form of the nonlinear system in step (1), and then carry out theoretical and numerical analyses of conditions on the existence, uniqueness, and stability of a solution to the discrete nonlinear system for accurately estimating the discrete basis sinograms, leading to quantitative reconstruction of VMIs in MSCT.
TGNN: A Joint Semi-supervised Framework for Graph-level Classification
The two twin modules collaborate with each other by exchanging instance similarity knowledge to fully explore the structure information of both labeled and unlabeled data.
SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval
Moreover, in contrast to the general retrieval, the relevance in the legal domain is sensitive to key legal elements.
Multi-metrics adaptively identifies backdoors in Federated learning
To evaluate the effectiveness of our approach, we conduct comprehensive experiments on different datasets under various attack settings, where our method achieves the best defensive performance.
Prototypical Mixing and Retrieval-Based Refinement for Label Noise-Resistant Image Retrieval
This paper investigates a realistic but understudied problem of image retrieval under label noise, which could lead to severe overfitting or memorization of noisy samples during optimization.
Try to Avoid Attacks: A Federated Data Sanitization Defense for Healthcare IoMT Systems
This paper introduces a Federated Data Sanitization Defense, a novel approach to protect the system from data poisoning attacks.
On Mitigating Hard Clusters for Face Clustering
Face clustering is a promising way to scale up face recognition systems using large-scale unlabeled face images.
Towards Representation Alignment and Uniformity in Collaborative Filtering
Then, we empirically analyze the learning dynamics of typical CF methods in terms of quantified alignment and uniformity, which shows that better alignment or uniformity both contribute to higher recommendation performance.
Technical Report (v1.0)--Pseudo-random Cartesian Sampling for Dynamic MRI
For an effective application of compressed sensing (CS), which exploits the underlying compressibility of an image, one of the requirements is that the undersampling artifact be incoherent (noise-like) in the sparsifying transform domain.
A Survey on Dropout Methods and Experimental Verification in Recommendation
Among various methods of coping with overfitting, dropout is one of the representative ways.
Cardiac and respiratory motion extraction for MRI using Pilot Tone-a patient study
Purpose: We seek to evaluate the accuracy and reliability of the cardiac and respiratory signals extracted from PT in patients clinically referred for cardiovascular MRI with the image-derived signals as the reference.
Recommendation Unlearning
From the perspective of utility, if a system's utility is damaged by some bad data, the system needs to forget these data to regain utility.
Density-Based Clustering with Kernel Diffusion
Finding a suitable density function is essential for density-based clustering algorithms such as DBSCAN and DPC.
ARGO: Modeling Heterogeneity in E-commerce Recommendation
Nowadays, E-commerce is increasingly integrated into our daily lives.
Criterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit Recommendation
Besides, CHCF integrates criterion learning and user preference learning into a unified framework, which can be trained jointly for the interaction prediction of the target behavior.
Deep Unsupervised Hashing by Distilled Smooth Guidance
Hashing has been widely used in approximate nearest neighbor search for its storage and computational efficiency.
Discriminative-Generative Dual Memory Video Anomaly Detection
In this paper, we propose a DiscRiminative-gEnerative duAl Memory (DREAM) anomaly detection model to take advantage of a few anomalies and solve data imbalance.
Efficient Non-Sampling Knowledge Graph Embedding
Experiments on benchmark datasets show that our NS-KGE framework can achieve a better performance on efficiency and accuracy over traditional negative sampling based models, and that the framework is applicable to a large class of knowledge graph embedding models.
Graph Contrastive Clustering
On the other hand, a novel graph-based contrastive learning strategy is proposed to learn more compact clustering assignments.
Spatiotemporal Imaging with Diffeomorphic Optimal Transportation
The proposed model is a production of assembling the Wasserstein distance with the Benamou--Brenier formula in optimal transportation and the flow of diffeomorphisms involved in large deformation diffeomorphic metric mapping, which is suitable for the scenario of spatiotemporal imaging with large diffeomorphic and mass-preserving deformations.
CIMON: Towards High-quality Hash Codes
However, due to the inefficient representation ability of the pre-trained model, many false positives and negatives in local semantic similarity will be introduced and lead to error propagation during the hash code learning.
A Novel Neural Network Training Framework with Data Assimilation
In data assimilation algorithms, the error covariance between the forecasts and observations is used to optimize the parameters.
Classical-noise-free sensing based on quantum correlation measurement
An intriguing question is: Can the quantum nature (quantumness) of sensors and targets be exploited to enable schemes that are not possible for classical probes or classical targets?
Quantum Physics
Effects of local decoherence on quantum critical metrology
94, 047201 (2005)] on the critical behaviors of the noisy Ising model, which shows that the universality class of the quantum criticality is modified by the decoherence, we find that the standard quantum limit is recovered by the single-particle decoherence, which is equivalent to local quantum measurement conducted by the environment and destroys the many-body entanglement in the ground state at the quantum critical point.
Quantum Physics
Deep Robust Clustering by Contrastive Learning
Different from existing methods, DRC looks into deep clustering from two perspectives of both semantic clustering assignment and representation feature, which can increase inter-class diversities and decrease intra-class diversities simultaneously.
Ranked #3 on
Online Clustering
on cifar10
Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation
However, existing KG enhanced recommendation methods have largely focused on exploring advanced neural network architectures to better investigate the structural information of KG.
Eicient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation
Factorization Machines (FM) with negative sampling is a popular solution for context-aware recommendation.
A Survey on Deep Hashing Methods
Nearest neighbor search aims to obtain the samples in the database with the smallest distances from them to the queries, which is a basic task in a range of fields, including computer vision and data mining.
Automatic Extraction and Sign Determination of Respiratory Signal in Real-time Cardiac Magnetic Resonance imaging
A complete heartbeat from each slice is then used for cardiac function quantification.
A New Variational Model for Joint Image Reconstruction and Motion Estimation in Spatiotemporal Imaging
This model consists of two components, one for conducting modified static image reconstruction, and the other performs sequentially indirect image registration.
Indirect Image Registration with Large Diffeomorphic Deformations
The paper adapts the large deformation diffeomorphic metric mapping framework for image registration to the indirect setting where a template is registered against a target that is given through indirect noisy observations.
