Titlebook: Machine Learning and Knowledge Extraction; 7th IFIP TC 5, TC 12 Andreas Holzinger,Peter Kieseberg,Edgar Weippl Conference proceedings 2023

HAVOC

,Human-in-the-Loop Integration with?Domain-Knowledge Graphs for?Explainable Federated Deep Learning,(GNNs). Specifically, a protein-protein interaction (PPI) network is masked over a deep neural network for classification, with patient-specific multi-modal genomic features enriched into the PPI graph’s nodes. Subnetworks that are relevant to the classification (referred to as “disease subnetworks”

micronized

斜谷

Myelin

熄滅

,Reinforcement Learning with?Temporal-Logic-Based Causal Diagrams,common approach is to represent the tasks as deterministic finite automata (DFA) and integrate them into the state-space for RL algorithms. However, while these machines model the reward function, they often overlook the causal knowledge about the environment. To address this limitation, we propose

冷淡周邊

Using Machine Learning to Generate a Dictionary for Environmental Issues,g a bag of words approach, where ESG stands for “environment, social and governance.” Specifically, the paper reviews some experiments performed to develop a dictionary for information about the environment, for “carbon footprint”. We investigate using Word2Vec based on Form 10K text and from Earnin

彎曲的人

Bph773

Galactogogue

驕傲

,The Split Matters: Flat Minima Methods for?Improving the?Performance of?GNNs,. At the same absolute value, a flat minimum in the loss landscape is presumed to generalize better than a sharp minimum. Methods for determining flat minima have been mostly researched for independent and identically distributed (i.i.d.) data such as images. Graphs are inherently non-i.i.d. since t