Entdecken Sie, wie neuronale Netzwerke nicht nur Daten, sondern auch Vorurteile verarbeiten. Tauchen Sie ein in die Welt des Neural Network Bias
Empirical applications of the free-energy principle are not straightforward because they entail a commitment to a particular process theory, especially at the cellular and synaptic levels. Using a recently established reverse engineering technique, we confirm the quantitative predictions of the free-energy principle using in vitro networks of rat cortical neurons that perform causal inference. Upon receiving electrical stimuli—generated by mixing two hidden sources—neurons self-organised to selectively
Presentation of Deep Learning Library (DLL) 1.0, a very fast neural network library
Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of their successive outputs is highly correlated with the accuracy of the value to which they converge. Thus, we can use the convergence rate as a useful proxy for uncertainty. This results in an approach to uncertainty estimation that provides state-of-the-art
Keeping up with the latest advancements in neural architecture search is vital for staying competitive
One of the most common problems in machine learning is overfitting. This can occur for a variety of reasons [1]. To address this problem...
Although deep learning models perform remarkably well across a range of tasks such as language translation and object recognition, it remains unclear what high-level logic, if any, they follow....
田中専務 拓海先生、お忙しいところ失礼します。最近部下に『シンボリックな手法でニューラルを強化する論文』を薦め…
# Model Prediction with Convolutional Neural Network (CNN) ASCMO-DYNAMIC offers the possibility to use Convolutional Neural Networks (CNNs) for transient modeling. As for RNNs, the open source machine learning platform Tensorflow is the underlying basis. CNNs have long been the state-of-the-art approach for solving image-based tasks. Recently, CNNs have become increasingly popular for time series modeling tasks. Instead of using 2D convolutions as in the image use case, 1D convolutions are used in the tim
Convolutional Neural Networks (CNNs), a deep learning application, are powerful tools particularly suited for image processing and classification applications. Pooling is a major component of CNNs and significantly influences learning. In this step, data is reduced