Local feature attribution methods are increasingly used to explain complex machine learning models. However, current methods are limited because they are extremely expensive to compute or are not capable of explaining a distributed series of models where
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Representation of Functions by Trigonometric SeriesKonyagin sergei Vladimirovich
4. Find a power series representation for 1/(1−x)31/(1−x)3. What is the radius of convergence?Solution:According to the above exercise, we have 1(1−x)3=∞∑n=2n(n−1)2xn−2=∞∑n=0(n+1)(n+2)2xn1(1−x)3=∑n=2∞n(n−1)2xn−2=∑n=0∞(n+1)(n+...
In mathematics, a Taylor series is a representation of a function as an infinite sum of terms that are calculated from the values of the function's derivatives at a single point: [5.11]f(xo+Δx)=f(xo)+Δx1!f′(xo)+Δx22!f″(xo)+Δx33!f‴(xo)+⋯ where, f′=∂f∂xf...
While the switch will be automatically tagged with "Monitor Only" in the dashboard to distinguish from fully managed Meraki switches, "Monitor Only" Catalyst 9500 switch appears and functions very similarly to Meraki MS switches in the dashboard, including a visual representation of c...
2001. Analysis and representation of regional sea-level variability from altimetry and atmospheric-oceanic data. Geophysical Journal International, 145(1): 1–18. doi: 10.1046/j.1365-246x.2001.00284.x [9] Frihy O E. 1992. Sea-level rise and shoreline retreat of the Nile Delta promontories,...
Fig. 2. The NOFRF based representation for the output frequency response of nonlinear systems. According to the definition of NOFRF, it can be seen that the NOFRF is not only related to the nonlinear characteristics of the system, but also related to the system input. It reflects a combine...
Second, the differences between these tasks are a kind of inductive bias, which can help improve generalization capacities. Motivated by the success of MTL, we propose a deep multi-task representation learning method (MTRL) to tackle time series classification and retrieval jointly. Specifically, ...
large data sets for conceptually similar tasks is desirable. This leveraging of existing neural networks is called transfer learning. In this example we adapt two deep CNNs, GoogLeNet and SqueezeNet, pretrained for image recognition to classify ECG waveforms based on a time-freq...