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Found 37 papers, 5 papers with code
SFR-GNN: Simple and Fast Robust GNNs against Structural Attacks
Existing efforts are dedicated to purifying the maliciously modified structure or applying adaptive aggregation, thereby enhancing the robustness against adversarial structural attacks.
Band-Attention Modulated RetNet for Face Forgery Detection
The transformer networks are extensively utilized in face forgery detection due to their scalability across large datasets. Despite their success, transformers face challenges in balancing the capture of global context, which is crucial for unveiling forgery clues, with computational complexity. To mitigate this issue, we introduce Band-Attention modulated RetNet (BAR-Net), a lightweight network designed to efficiently process extensive visual contexts while avoiding catastrophic forgetting. Our approach empowers the target token to perceive global information by assigning differential attention levels to tokens at varying distances.
Spikewhisper: Temporal Spike Backdoor Attacks on Federated Neuromorphic Learning over Low-power Devices
Federated neuromorphic learning (FedNL) leverages event-driven spiking neural networks and federated learning frameworks to effectively execute intelligent analysis tasks over amounts of distributed low-power devices but also perform vulnerability to poisoning attacks.
Collective Certified Robustness against Graph Injection Attacks
We investigate certified robustness for GNNs under graph injection attacks.
Adversarially Robust Signed Graph Contrastive Learning from Balance Augmentation
Similar to how structure learning can restore unsigned graphs, balance learning can be applied to signed graphs by improving the balance degree of the poisoned graph.
Universally Robust Graph Neural Networks by Preserving Neighbor Similarity
This theoretical proof explains the empirical observations that the graph attacker tends to connect dissimilar node pairs based on the similarities of neighbor features instead of ego features both on homophilic and heterophilic graphs.
Cost Aware Untargeted Poisoning Attack against Graph Neural Networks,
Graph Neural Networks (GNNs) have become widely used in the field of graph mining.
Node-aware Bi-smoothing: Certified Robustness against Graph Injection Attacks
Furthermore, we extend two state-of-the-art certified robustness frameworks to address node injection attacks and compare our approach against them.
Optimal Power Flow in Highly Renewable Power System Based on Attention Neural Networks
The Optimal Power Flow (OPF) problem is pivotal for power system operations, guiding generator output and power distribution to meet demand at minimized costs, while adhering to physical and engineering constraints.
Generative Diffusion Models for Lattice Field Theory
This study delves into the connection between machine learning and lattice field theory by linking generative diffusion models (DMs) with stochastic quantization, from a stochastic differential equation perspective.
Diffusion Models as Stochastic Quantization in Lattice Field Theory
In this work, we establish a direct connection between generative diffusion models (DMs) and stochastic quantization (SQ).
Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach
Graph anomaly detection (GAD) has achieved success and has been widely applied in various domains, such as fraud detection, cybersecurity, finance security, and biochemistry.
Coupled-Space Attacks against Random-Walk-based Anomaly Detection
In addition, we conduct transfer attack experiments in a black-box setting, which show that our feature attack significantly decreases the anomaly scores of target nodes.
Homophily-Driven Sanitation View for Robust Graph Contrastive Learning
We conduct extensive experiments to evaluate the performance of our proposed model, GCHS (Graph Contrastive Learning with Homophily-driven Sanitation View), against two state of the art structural attacks on GCL.
Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks
A physics-informed neural network (PINN) embedded with the susceptible-infected-removed (SIR) model is devised to understand the temporal evolution dynamics of infectious diseases.
Simple yet Effective Gradient-Free Graph Convolutional Networks
Linearized Graph Neural Networks (GNNs) have attracted great attention in recent years for graph representation learning.
Towards Adversarially Robust Recommendation from Adaptive Fraudster Detection
The robustness of recommender systems under node injection attacks has garnered significant attention.
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification
Graph Neural Networks (GNNs) are vulnerable to data poisoning attacks, which will generate a poisoned graph as the input to the GNN models.
A Multi-user Oriented Live Free-viewpoint Video Streaming System Based On View Interpolation
As an important application form of immersive multimedia services, free-viewpoint video(FVV) enables users with great immersive experience by strong interaction.
Automatic differentiation approach for reconstructing spectral functions with neural networks
Reconstructing spectral functions from Euclidean Green's functions is an important inverse problem in physics.
Reconstructing spectral functions via automatic differentiation
Exploiting the neural networks' regularization as a non-local smoothness regulator of the spectral function, we represent spectral functions by neural networks and use the propagator's reconstruction error to optimize the network parameters unsupervisedly.
Probabilistic Bearing Fault Diagnosis Using Gaussian Process with Tailored Feature Extraction
However, current deep learning methods perform the bearing fault diagnosis in the form of deterministic classification, which overlook the uncertainties that inevitably exist in actual practice.
Shared Data and Algorithms for Deep Learning in Fundamental Physics
We introduce a Python package that provides simply and unified access to a collection of datasets from fundamental physics research - including particle physics, astroparticle physics, and hadron- and nuclear physics - for supervised machine learning studies.
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection
Graph-based Anomaly Detection (GAD) is becoming prevalent due to the powerful representation abilities of graphs as well as recent advances in graph mining techniques.
The Incompressible Navier-Stokes-Fourier Limit from Boltzmann-Fermi-Dirac Equation
We study Boltzmann-Fermi-Dirac equation when quantum effects are taken into account in dilute gas dynamics.
Analysis of PDEs
Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk
As the COVID-19 pandemic continues to ravage the world, it is of critical significance to provide a timely risk prediction of the COVID-19 in multi-level.
Robust Collective Classification against Structural Attacks
Finally, we apply our approach in a transductive learning setting, and show that robust AMN is much more robust than state-of-the-art deep learning methods, while sacrificing little in accuracy on non-adversarial data.
Efficient Characterization of Dynamic Response Variation Using Multi-Fidelity Data Fusion through Composite Neural Network
Uncertainties in a structure is inevitable, which generally lead to variation in dynamic response predictions.
ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard
21 teams submit results for Task 1, 23 teams submit results for Task 2, 24 teams submit results for Task 3, and 13 teams submit results for Task 4.
Computing Equilibria in Binary Networked Public Goods Games
Public goods games study the incentives of individuals to contribute to a public good and their behaviors in equilibria.
Computer Science and Game Theory
Adversarial Robustness of Similarity-Based Link Prediction
We propose a novel approach for increasing robustness of similarity-based link prediction by endowing the analyst with a restricted set of reliable queries which accurately measure the existence of queried links.
Multi-layer Attention Mechanism for Speech Keyword Recognition
The key idea is that, in addition to the conventional attention mechanism, information of layers prior to feature extraction and LSTM are introduced into attention weights calculations.
Ranked #4 on
Keyword Spotting
on Google Speech Commands
(Google Speech Commands V2 20 metric)
Attacking Similarity-Based Link Prediction in Social Networks
We offer a comprehensive algorithmic investigation of the problem of attacking similarity-based link prediction through link deletion, focusing on two broad classes of such approaches, one which uses only local information about target links, and another which uses global network information.
Social and Information Networks Cryptography and Security
Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network
Link prediction is one of the fundamental research problems in network analysis.
Social and Information Networks Cryptography and Security 91D30 (Primary) 68T20 (Secondary) G.2.2; J.4
Scalar Quantization as Sparse Least Square Optimization
Furthermore, to compute quantization results with a given amount of values/clusters, this paper designed an iterative method and a clustering-based method, and both of them are built on sparse least square.
An equation-of-state-meter of quantum chromodynamics transition from deep learning
A primordial state of matter consisting of free quarks and gluons that existed in the early universe a few microseconds after the Big Bang is also expected to form in high-energy heavy-ion collisions.
An equation-of-state-meter of QCD transition from deep learning
Supervised learning with a deep convolutional neural network is used to identify the QCD equation of state (EoS) employed in relativistic hydrodynamic simulations of heavy-ion collisions from the simulated final-state particle spectra $\rho(p_T,\Phi)$.
