Towards Formal Verification of Neural Networks: A Temporal Logic Based FrameworkNeural networkFormal verificationMSVLPPTLDue to extensive applications of deep learning and neural networks, their security has attracted more and more attentions from academic and industrial circles. Under the guidance of the...
Figure2b displays the mean binding energies of water molecules in the selected MOFs with open-metal centers. The agreement between our predictions and those found in the literature is quite impressive. The largest deviation is in the case of Mg, where the error is more than 10%, whereas all ...
Just as the OGB-LSC competition and Open Catalyst challenge played a major role for the GNN community, it is now time for a new series of competitions 🥇. We even got the TGB (Temporal graph benchmark) recently. If you were at NeurIPS’23, then you probably heard of P...
🪧 How to commit your own algorithm to TSB-AD: you can send us the Run_Custom_Detector.py (replace Custom_Detector with the model name) to us via (i)emailor (ii) open a pull request and add the file tobenchmark_expfolder inTSB-AD-algobranch. We will test and evaluate the algori...
One of those traits is unique toTemporal Fusion Transformer. We will cover this in the next section. The Extended Time-Series Data Format Among notable DL time-series models (e.g.,DeepAR[4]), TFT stands out because it supports various types of features. These are: ...
Our convolutional neural network (CNN) is extremely memory efficient (below 620 kilobytes) and runs at 60 hertz for a resolution of 1,920 × 1,080 pixels on a single consumer-grade graphics processing unit. Leveraging low-power on-device artificial intelligence acceleration chips, our CNN also ...
GRAPH Neural Networks EPANET 1. Introduction 1.1. Metamodels of water distribution systems Hydraulic models are essential for the design, management, and control of water distribution systems (WDS). These physics-based models, such as EPANET (Rossman, 2022), usually solve the mass and energy cons...
an inference is drawn that the likelihood function of an observation given a reference can closely approximated by a measure of the correlation between empirical and true temporal higher-order moment functions. On the assumption that all signal parameters as well as the noise power, independent data...
et al. Temporal order of signal propagation within and across intrinsic brain networks. Proc. Natl Acad. Sci. USA 118, e2105031118 (2021). Article CAS PubMed PubMed Central Google Scholar Seguin, C., Razi, A. & Zalesky, A. Inferring neural signalling directionality from undirected ...
detecting outlier data [12]. It recently demonstrated exceptional abilities in learning expressive models of complex big data such as graph data, trajectories data, high-dimensional streaming data and temporal-spatial data. Fig. 2 Artificial Intelligence vs. Machine Learning vs. Deep Learning...