Loss functions and constraints improve sea surface height prediction

In order to understand currents, tides and other ocean dynamics, scientists need to accurately capture sea surface height, or ...
IEEE

A Fully Probabilistic Model for Sigmoid Approximation and Its Hardware- Efficient Implementation

Abstract: The sigmoid function is a representative activation function in shallow neural networks. Its hardware realization is challenging due to the complex exponential and reciprocal operations.
GitHub

Create a base class for sigmoid functions

We might consider having a base function for all sigmoid function, and add a class member that allows to control the vertical offset. We could then implement more models in addition to the Erf (or ...
Hosted on MSN

20 Activation Functions in Python for Deep Neural Networks – ELU, ReLU, Leaky-ReLU, Sigmoid, Cosine

Explore 20 different activation functions for deep neural networks, with Python examples including ELU, ReLU, Leaky-ReLU, Sigmoid, and more. #ActivationFunctions #DeepLearning #Python Supreme Court ...
IEEE

Sigmoid Function–Based Discontinuous Feedforward Current Ripple Modeling for Stability Analysis of Cascaded Converter

Abstract: The cascaded converter, under the switching ripple interaction between source and load converters, can be described as a high-order system with multiple switching state sequences (SSSs).
C&EN

Transfer Learning with a Graph Attention Network and Weighted Loss Function for Screening of Persistent, Bioaccumulative, Mobile, and Toxic Chemicals

Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental ...
SiliconANGLE

Neo4j integrates dozens of graph analytics functions with data in Snowflake

Graph database vendor Neo4j Inc. is teaming up with Snowflake Inc. to make a library of Neo4j’s graph analytics functions available in the Snowflake cloud. The deal announced today allows users to ...