Complexity science for understanding AI: from the dynamics of complex networks to collective effects of interacting neural networks

Semantic matching is of central significance to the answer selection task which aims to select correct answers for a given question from a candidate answer pool. A useful method is to employ neural networks with attention to generate sentences representations in a way that information from pair sentences can mutually influence the computation of representations. In this work, an effective architecture,multi-size neural network with attention mechanism (AM-MSNN),is introduced into the answer selection task

Abstract page for arXiv paper 2211.08486v4: Scalar Invariant Networks with Zero Bias

# StampCA: Growing Emoji with Conditional Neural Cellular Automata When a baby is born, it doesn’t just appear out of nowhere -- it starts as a single cell. This seed cell contains all the information needed to replicate and grow into a full adult. In biology, we call this process morphogenesis: the development of a seed into a structured design. Morphogenesis builds up an embyro. https://www.nature.com/articles/s41467-018-04155-2 Of course, if there’s a cool biological phenomenon, someone has tried to

New imaging techniques map the self-organizing patterns of neuronal networks, showing how brain cells form clusters to build functional circuits in the lab

Les réseaux de neurones et la ressemblance des possibles / Neural Networks and the Resemblance of Possibles 02/2017 Imagination artificielle Par-delà l’effet de mode provoqué par la mise à disposition du code source de plusieurs réseaux de neurones et la médiatisation orchestrée par certains acteurs du marché, j’aimerais formuler l’hypothèse de certaines implications conceptuelles de ces réseaux récursifs de neurones (RNN). Pour formuler celle-ci, je soutiendrais que les RNN intensifient

NeurIPS Proceedings Search Generalizing Tree Probability Estimation via Bayesian Networks Cheng Zhang, Frederick A Matsen IV Advances in Neural Information Processing Systems 31 (NeurIPS 2018) Abstract Probability estimation is one of the fundamental tasks in statistics and machine learning. However, standard methods for probability estimation on discrete objects do not handle object structure in a satisfactory manner. In this paper, we derive a general Bayesian network formulation for probability estimatio

Blog Topics Advertise Join Newsletter Can graph machine learning identify hate speech in online social networks? Online hate speech is a complex subject. Follow this demonstration using state-of-the-art graph neural network models to detect hateful users based on their activities on the Twitter social network. --> comments By Pantelis Elinas, Anna Leontjeva, and Yuriy Tyshetskiy. Over three decades, the Internet has grown from a small network of computers used by research scientists to communicate and excha

The text discusses a method of exchanging data between programs without using DLLs, utilizing TXT files via RAM-Disk for high-speed communication. It highlights the advantages of this approach over DLLs, emphasizing simplicity and flexibility. The author also explores the use of neural networks for determining buy/sell signals in trading models, mentioning challenges related to dataset length and model variability

田中専務 拓海先生、最近薦められた論文の題名が難しくて頭が痛いんです。『Neural McKean-Vlaso