We summarize two fundamental weaknesses of attackers, namely “Device aggregation” and “Activity aggregation”, and naturally present a neural network approach based on heterogeneous account-device graphs
Facilitated by the automatic differentiation technique widely used in deep learning, we propose a uniform framework of differentiable Tensor renormalization group that can be applied to improve various Tensor renormalization group methods, in an automatic fashion
All networks are trained on strictly binary silhouettes, but we overlay a checkerboard texture on the source shape to clearly visualize the smoothness of the estimated mappings and display the texture transfer abilities facilitated by our system
We propose iBTune to adjust DBMS buffer pool sizes by using a large deviation analysis for least recently used caching models and leveraging the similar instances based on performance metrics to find tolerable miss ratios
Assuming the Exponential Time Hypothesis, there is an > 0 such that any strong simulation that can determine if 0|C|0| = 0 of a polynomial-sized quantum circuit C formed from the Clifford+T gate set with N T -gates takes time at least 2 N
To compare our Quantum Approximate Optimization Algorithm simulator with existing quantum simulation package equipped with QAOA functionalities, we choose MAX-CUT problems on a random regular graph and compare the time spent for a single energy function query
Beyond surveying the advances of each aspect of Convolutional Neural Network, we have introduced the application of Convolutional Neural Network on many tasks, including image classification, object detection, object tracking, pose estimation, text detection, visual saliency dete...