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Found 24 papers, 7 papers with code
GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization
The extraordinary ability of generative models emerges as a new trend in image editing and generating realistic images, posing a serious threat to the trustworthiness of multimedia data and driving the research of image manipulation detection and location(IMDL).
Module control of network analysis in psychopathology
The network approach to characterizing psychopathology departs from traditional latent categorical and dimensional approaches.
Transmission Interface Power Flow Adjustment: A Deep Reinforcement Learning Approach based on Multi-task Attribution Map
Transmission interface power flow adjustment is a critical measure to ensure the security and economy operation of power systems.
Human-Imperceptible Retrieval Poisoning Attacks in LLM-Powered Applications
Presently, with the assistance of advanced LLM application development frameworks, more and more LLM-powered applications can effortlessly augment the LLMs' knowledge with external content using the retrieval augmented generation (RAG) technique.
When Fuzzing Meets LLMs: Challenges and Opportunities
Fuzzing, a widely-used technique for bug detection, has seen advancements through Large Language Models (LLMs).
Distilling Semantic Priors from SAM to Efficient Image Restoration Models
SPD leverages a self-distillation manner to distill the fused semantic priors to boost the performance of original IR models.
View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network
Experiments on two datasets show that VDT is a feasible and effective solution for AGPReID, surpassing the previous method on mAP/Rank1 by up to 5. 0%/2. 7% on CARGO and 3. 7%/5. 2% on AG-ReID, keeping the same magnitude of computational complexity.
Ranked #1 on
Person Re-Identification
on AG-ReID
MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object Detection
Multi-modal 3D object detection models for automated driving have demonstrated exceptional performance on computer vision benchmarks like nuScenes.
Powerformer: A Section-adaptive Transformer for Power Flow Adjustment
In this paper, we present a novel transformer architecture tailored for learning robust power system state representations, which strives to optimize power dispatch for the power flow adjustment across different transmission sections.
HyperAttack: Multi-Gradient-Guided White-box Adversarial Structure Attack of Hypergraph Neural Networks
Hypergraph neural networks (HGNN) have shown superior performance in various deep learning tasks, leveraging the high-order representation ability to formulate complex correlations among data by connecting two or more nodes through hyperedge modeling.
Optimization for Amortized Inverse Problems
Incorporating a deep generative model as the prior distribution in inverse problems has established substantial success in reconstructing images from corrupted observations.
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System
As the topology of the power system is in the form of graph structure, graph neural network based representation learning is naturally suitable for learning the status of the power system.
Modeling 3D Layout for Group Re-Identification
To the best of our knowledge, 3DT is the first work to address GReID with 3D perspective, and the City1M is the currently largest dataset.
PANOM: Automatic Hyper-parameter Tuning for Inverse Problems
Automated hyper-parameter tuning for unsupervised learning, including inverse problems, remains a long-standing open problem due to the lack of validation data.
Efficient QAM Signal Detector for Massive MIMO Systems via PS-ADMM Approach
In this paper, we design an efficient quadrature amplitude modulation (QAM) signal detector for massive multiple-input multiple-output (MIMO) communication systems via the penalty-sharing alternating direction method of multipliers (PS-ADMM).
A Low-Complexity ADMM-based Massive MIMO Detectors via Deep Neural Networks
An alternate direction method of multipliers (ADMM)-based detectors can achieve good performance in both small and large-scale multiple-input multiple-output (MIMO) systems.
Agent-Based Campus Novel Coronavirus Infection and Control Simulation
Corona Virus Disease 2019 (COVID-19), due to its extremely high infectivity, has been spreading rapidly around the world and bringing huge influence to socioeconomic development as well as people's daily life.
Social and Information Networks Physics and Society Populations and Evolution
Designing Massive MIMO Detector via PS-ADMM approach
Then, the higher modulation signals are decomposed into a sum of multiple binary variables through their inherent structures, by exploiting introduced binary variables as penalty functions, the detection optimization model is equivalent to a nonconvex sharing minimization problem.
MCMC-Interactive Variational Inference
Leveraging well-established MCMC strategies, we propose MCMC-interactive variational inference (MIVI) to not only estimate the posterior in a time constrained manner, but also facilitate the design of MCMC transitions.
Quantum-Classical Machine learning by Hybrid Tensor Networks
In this work, we propose the quantum-classical hybrid tensor networks (HTN) which combine tensor networks with classical neural networks in a uniform deep learning framework to overcome the limitations of regular tensor networks in machine learning.
RNN-Test: Towards Adversarial Testing for Recurrent Neural Network Systems
Compared with the neuron coverage, the proposed state coverage metrics as guidance excel with 4. 17% to 97. 22% higher success (or generation) rate.
Weibull Racing Survival Analysis with Competing Events, Left Truncation, and Time-varying Covariates
Specifically, we study time to death of three types of lymphoma and show the potential of WDR in modeling nonlinear covariate effects and discovering new diseases.
Survival Analysis
Methodology
Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks
We propose Lomax delegate racing (LDR) to explicitly model the mechanism of survival under competing risks and to interpret how the covariates accelerate or decelerate the time to event.
Permuted and Augmented Stick-Breaking Bayesian Multinomial Regression
To model categorical response variables given their covariates, we propose a permuted and augmented stick-breaking (paSB) construction that one-to-one maps the observed categories to randomly permuted latent sticks.
