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Found 8 papers, 0 papers with code
Interpreting Graph Neural Networks with In-Distributed Proxies
A popular paradigm for the explainability of GNNs is to identify explainable subgraphs by comparing their labels with the ones of original graphs.
Adversarial Collaborative Filtering for Free
In this paper, we present Sharpness-aware Collaborative Filtering (SharpCF), a simple yet effective method that conducts adversarial training without extra computational cost over the base optimizer.
Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute Privacy
To address this challenge, in this paper, we leverage the inherent vulnerability of machine learning to adversarial attacks, and design a novel text-space Adversarial attack for Social Good, called Adv4SG.
Denoising Self-attentive Sequential Recommendation
Transformer-based sequential recommenders are very powerful for capturing both short-term and long-term sequential item dependencies.
TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems
We found that our TinyKG with INT2 quantization aggressively reduces the memory footprint of activation maps with $7 \times$, only with $2\%$ loss in accuracy, allowing us to deploy KGNNs on memory-constrained devices.
SMARTQUERY: An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reduction
Graph neural networks have achieved significant success in representation learning.
Towards Generating Adversarial Examples on Mixed-type Data
The existence of adversarial attacks (or adversarial examples) brings huge concern about the machine learning (ML) model's safety issues.
LOCx2, a Low-latency, Low-overhead, 2 x 5.12-Gbps Transmitter ASIC for the ATLAS Liquid Argon Calorimeter Trigger Upgrade
In this paper, we present the design and test results of LOCx2, a transmitter ASIC for the ATLAS Liquid Argon Calorimeter trigger upgrade.
Instrumentation and Detectors
