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Found 14 papers, 9 papers with code
Optimal Transport for Brain-Image Alignment: Unveiling Redundancy and Synergy in Neural Information Processing
We believe our approach paves the way for a more precise understanding of brain signals in the future.
Towards Distributed Backdoor Attacks with Network Detection in Decentralized Federated Learning
In this paper, we experimentally demonstrate that, while directly applying DBA to decentralized FL, the attack success rate depends on the distribution of attackers in the network architecture.
Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspective
From the spatial data management perspective, we present a novel Transformer framework called PatchSTG to efficiently and dynamically model spatial dependencies for large-scale traffic forecasting with interpretability and fidelity.
Ranked #1 on
Traffic Prediction
on LargeST
Weak-to-Strong Generalization beyond Accuracy: a Pilot Study in Safety, Toxicity, and Legal Reasoning
We remark that existing works investigate the phenomenon of weak-to-strong generation in analogous setup (i. e., binary classification), rather than practical alignment-relevant tasks (e. g., safety).
PLeak: Prompt Leaking Attacks against Large Language Model Applications
As a result, a natural attack, called prompt leaking, is to steal the system prompt from an LLM application, which compromises the developer's intellectual property.
A Survey of Lottery Ticket Hypothesis
The Lottery Ticket Hypothesis (LTH) states that a dense neural network model contains a highly sparse subnetwork (i. e., winning tickets) that can achieve even better performance than the original model when trained in isolation.
Successfully Applying Lottery Ticket Hypothesis to Diffusion Model
Therefore, we propose to find the winning ticket with varying sparsity along different layers in the model.
SneakyPrompt: Jailbreaking Text-to-image Generative Models
Text-to-image generative models such as Stable Diffusion and DALL$\cdot$E raise many ethical concerns due to the generation of harmful images such as Not-Safe-for-Work (NSFW) ones.
Rethinking Graph Lottery Tickets: Graph Sparsity Matters
Therefore, we propose two techniques to improve GNN performance when the graph sparsity is high.
Addressing Heterogeneity in Federated Learning via Distributional Transformation
Federated learning (FL) allows multiple clients to collaboratively train a deep learning model.
RFID-Based Indoor Spatial Query Evaluation with Bayesian Filtering Techniques
Based on the inference method and tracking models, we develop innovative indoor range and k nearest neighbor (kNN) query algorithms.
CDGNet: A Cross-Time Dynamic Graph-based Deep Learning Model for Traffic Forecasting
Besides, we propose a novel encoder-decoder architecture to incorporate the cross-time dynamic graph-based GCN for multi-step traffic forecasting.
MGC: A Complex-Valued Graph Convolutional Network for Directed Graphs
We test our model on several graph datasets including directed homogeneous and heterogeneous graphs.
Practical Blind Membership Inference Attack via Differential Comparisons
The success of the former heavily depends on the quality of the shadow model, i. e., the transferability between the shadow and the target; the latter, given only blackbox probing access to the target model, cannot make an effective inference of unknowns, compared with MI attacks using shadow models, due to the insufficient number of qualified samples labeled with ground truth membership information.
