HAVOC
发表于 2025-3-28 15:53:23
,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
发表于 2025-3-28 20:35:07
http://reply.papertrans.cn/63/6206/620558/620558_42.png
斜谷
发表于 2025-3-29 01:22:23
http://reply.papertrans.cn/63/6206/620558/620558_43.png
Myelin
发表于 2025-3-29 06:51:15
http://reply.papertrans.cn/63/6206/620558/620558_44.png
熄灭
发表于 2025-3-29 07:34:01
,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
冷淡周边
发表于 2025-3-29 15:01:57
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
弯曲的人
发表于 2025-3-29 17:11:05
http://reply.papertrans.cn/63/6206/620558/620558_47.png
Bph773
发表于 2025-3-29 21:54:30
http://reply.papertrans.cn/63/6206/620558/620558_48.png
Galactogogue
发表于 2025-3-30 02:26:40
http://reply.papertrans.cn/63/6206/620558/620558_49.png
骄傲
发表于 2025-3-30 04:44:55
,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