for neural encoding and behavioral decoding, by recasting it as a structured masking problem. This approach allows us to sample from a distribution of interest, e.g., to visualize time series of various potentia
When the maximization is not going well, it is also possible to set the maximum number of iter- ations (see [R] Maximize) to the point where the optimizer appears to be stuck and to inspect the estimation results at that point. from(init specs) specifies the initial values of the ...
CVAE-5-NF: Same config as CVAE-3-YSKIP but with additional k∈{2,6,10} steps of Inverse Autoregressive Flow (IAF) transformations. All models for this section were implemented in Python 3.7 and PyTorch. For training, we used the Adam [43] optimizer at its default learning rate of 0.000...
In this work, the Adam optimizer was used to optimize the network and skip-layer connection used to accelerate the convergence speed of the CDCGAN. Figs. 7 and 8 show the training curves of the generator and discriminator with and without a skip-layer connection, respectively. The horizontal ...
Therefore, instead of gradient descent method for ∇Φ(𝒢)∇Φ(G) and ∇Φ(𝒟)∇Φ(D) in GAN, we implement the Adamax optimizer to train both 𝒢G and 𝒟D, which computes exponential moving averages of gradients {∇Φ(𝒢),∇Φ(𝒟)}{∇Φ(G),∇Φ(D)} and ...
4.3.2. Monitor the Control Flow Integrity Although SMU can detect tampering with instruction code or control flow in a timely manner by comparing dynamic and static labels, it is sometimes difficult to determine whether the jump directions of conditional branches have been tampered with. It is dif...