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Found 6 papers, 1 papers with code
Projecting Non-Fungible Token (NFT) Collections: A Contextual Generative Approach
In this paper, we want to obtain a generative model that, given the early transactions history (first quarter Q1) of a newly minted collection, generates subsequent transactions (quarters Q2, Q3, Q4), where the generative model is trained using the transaction history of a few mature collections.
Mitigating Adversarial Attacks by Distributing Different Copies to Different Users
We point out that by distributing different copies of the model to different buyers, we can mitigate the attack such that adversarial samples found on one copy would not work on another copy.
Filtering DDoS Attacks from Unlabeled Network Traffic Data Using Online Deep Learning
We also introduce a machine learning optimization problem that aims to sift out the attacks using ${\mathcal N}$ and ${\mathcal M}$.
SHADOWCAST: Controllable Graph Generation with Explainability
We introduce the problem of explaining graph generation, formulated as controlling the generative process to produce desired graphs with explainable structures.
SHADOWCAST: Controllable Graph Generation
Given an observed graph and some user-specified Markov model parameters, ${\rm S{\small HADOW}C{\small AST}}$ controls the conditions to generate desired graphs.
Towards Safer Smart Contracts: A Sequence Learning Approach to Detecting Vulnerabilities
In particular, we propose a novel approach of sequential learning of smart contract vulnerabilities using machine learning --- long-short term memory (LSTM) --- that perpetually learns from an increasing number of contracts handled over time, leading to safer smart contracts.
Cryptography and Security
