How to best define, detect and characterize network memory, i.e. the dependence of a network’s structure on its past, is currently a matter of debate. Here we show that the memory of a temporal network is inherently multidimensional, and we introduce a
Human emotions fluctuate over time. However, it is unclear how these shifting emotional states influence the organization of episodic memory. Here, we examine how emotion dynamics transform experiences into memorable events. Using custom musical pieces a
Be sure to decide the size of TT carefully. The use of TT involves a lot of memory access, which may result in worse search speed especially when the search depth is less than 3-ply. In addition, it is possible to allow the search to use a deeper TT cache when available. This may...
In addition to the space-scale, a DT may contain temporal scales as well, given the different dynamic nature of the different components of a system. As an example, considering a production system in Fig. 5, a DT can be developed for the whole production plant, for the single machine in...
A recent “third wave” of neural network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning spee
Causality-Inspired Spatial-Temporal Explanations for Dynamic Graph Neural Networks Anomaly Detection Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection Boosting Graph Anomaly Detection with Adaptive Message Passing LLM Talk like a Graph: Encoding Graphs for Large Language Models Lab...
In this paper, the newly proposed CoCosNet v2 establishes full-resolution correspondence for cross-domain images. Researchers proposed two techniques to improve the memory efficiency of high-resolution correspondence. First, they adopted a coarse-to-fine strategy (...
In summary, the iTransformer is not a new architecture; it does not reinvent the Transformer. It simply applies it on the inverted dimensions of the input, which allows the model to learn multivariate correlations and capture temporal properties. ...
with far less attention given to how the structure of connections between events impacts memory. Here we conduct a functional magnetic resonance imaging study in which participants watch and recall a series of realistic audiovisual narratives. By transforming narratives into networks of events, we demo...
Neural generative models can be used to learn complex probability distributions from data, to sample from them, and to produce probability density estimates. We propose a computational framework for developing neural generative models inspired by the the