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[G Certification Prep] Part 12: Casting the Magic of "Non-linearity" on Neural Networks! The Role and Evolution of Activation Functions

Hello! The theme for the 12th session of the G Certification Prep Course is **"Activation Functions."** Activation functions play a subtle yet crucial role within neural networks. In this session, we ...
IEEE

Multi Pattern Features-Based Spoofing Detection Mechanism Using One Class Learning

Abstract: Automatic Speaker Verification systems are prone to various voice spoofing attacks such as replays, voice conversion (VC) and speech synthesis. Malicious users can perform specific tasks ...
eLife

An interpretable neural network unveils higher-order epistasis in large protein sequence-function relationships

In this important work, the authors present a new transformer-based neural network designed to isolate and quantify higher-order epistasis in protein sequences. They provide solid evidence that higher ...
The Lancet

Automated interpretation of systolic and diastolic function on the echocardiogram: a multicohort study

dBioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore eGenome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore fInstitute of ...
Hosted on MSN

Activation functions in neural networks: Types and examples

In this video, we will see What is Activation Function in Neural network, types of Activation function in Neural Network, why to use an Activation Function and which Activation function to use. The ...
GitHub

A Hierarchical Softmax Framework for PyTorch.

Documentation available here: https://rbturnbull.github.io/hierarchicalsoftmax/. The SoftmaxNode class inherits from the anytree Node class which means that you can ...
GitHub

Flash Attention with Sink — GPT-OSS 20B Attention Implementation

flash-attention-with-sink implements an attention variant used in GPT-OSS 20B that integrates a "sink" step into FlashAttention. This repo focuses on the forward path and provides an experimental ...
IEEE

Algorithm-Hardware Co-Design of a Unified Accelerator for Non-Linear Functions in Transformers

Abstract: Nonlinear functions (NFs) in Transformers require high-precision computation consuming significant time and energy, despite the aggressive quantization schemes for other components.