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Found 10 papers, 5 papers with code
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems
Based on this, we propose a comprehensive taxonomy, which systematically analyzes potential risks associated with each module of an LLM system and discusses the corresponding mitigation strategies.
Two Sides of The Same Coin: Bridging Deep Equilibrium Models and Neural ODEs via Homotopy Continuation
Deep Equilibrium Models (DEQs) and Neural Ordinary Differential Equations (Neural ODEs) are two branches of implicit models that have achieved remarkable success owing to their superior performance and low memory consumption.
Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach
We present a simple approach to incorporate prior knowledge in BNNs based on external summary information about the predicted classification probabilities for a given dataset.
6GAN: IPv6 Multi-Pattern Target Generation via Generative Adversarial Nets with Reinforcement Learning
6GAN forces multiple generators to train with a multi-class discriminator and an alias detector to generate non-aliased active targets with different addressing pattern types.
6GCVAE: Gated Convolutional Variational Autoencoder for IPv6 Target Generation
IPv6 scanning has always been a challenge for researchers in the field of network measurement.
SiamHAN: IPv6 Address Correlation Attacks on TLS Encrypted Traffic via Siamese Heterogeneous Graph Attention Network
To do this, we propose an IPv6 address correlation model - SiamHAN.
Deconfounded Representation Similarity for Comparison of Neural Networks
Similarity metrics such as representational similarity analysis (RSA) and centered kernel alignment (CKA) have been used to compare layer-wise representations between neural networks.
6VecLM: Language Modeling in Vector Space for IPv6 Target Generation
Fast IPv6 scanning is challenging in the field of network measurement as it requires exploring the whole IPv6 address space but limited by current computational power.
Informative Bayesian Neural Network Priors for Weak Signals
Encoding domain knowledge into the prior over the high-dimensional weight space of a neural network is challenging but essential in applications with limited data and weak signals.
Learning Global Pairwise Interactions with Bayesian Neural Networks
Estimating global pairwise interaction effects, i. e., the difference between the joint effect and the sum of marginal effects of two input features, with uncertainty properly quantified, is centrally important in science applications.
