Marktechpost

SEPTEMBER 26, 2023

The post ETH Zurich Researchers Introduce the Fast Feedforward (FFF) Architecture: A Peer of the Feedforward (FF) Architecture that Accesses Blocks of its Neurons in Logarithmic Time appeared first on MarkTechPost. If you like our work, you will love our newsletter.

Neural Network 116

Neural Network Large Language Models Deep Learning Artificial Intelligence 116

Marktechpost

APRIL 18, 2024

The Forward-Forward (FF) technique, which Hinton developed, offers a fresh method for training neural networks, in addition to the studies above focused on distributed backpropagation implementations. This contrasts with backpropagation, primarily focused on solving problems without distribution.

Neural Network 91

Neural Network Algorithm Machine Learning Deep Learning 91

Marktechpost

AUGUST 15, 2023

The study analyzes three HPE methods and conducts both horizontal and vertical analyses on the popular FF++ deepfake dataset. This research team evaluated three HPE methods using the FF++ deepfake dataset and conducted experiments involving KNN with Dynamic Time Warping (DTW) and deep learning models.

Convolutional Neural Networks 52

Convolutional Neural Networks Deep Learning Neural Network Machine Learning 52

Marktechpost

JULY 24, 2023

Experiments to evaluate the effectiveness of their TALL-Swin method for detecting deepfake videos: Intra-dataset evaluations: The authors compared TALL-Swin with several advanced methods using the FF++ dataset under both Low Quality (LQ) and High Quality (HQ) videos.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence AI AI 52

Unite.AI

DECEMBER 8, 2023

At its core, the UltraFastBERT framework is a variant of the BERT framework, builds on this concept, and replaces feedforward layers with faster feedforward networks in its architecture that ultimately results in the UltraFastBERT framework utilizing only 0.3%

BERT 311

BERT Neural Network Large Language Models Algorithm 311

Sebastian Ruder

FEBRUARY 23, 2023

(a) Sequential Bottleneck Adapter : The first adapter architecture proposed for Transformers consisting of two bottleneck layers placed after the multi-head attention (MHA) and feed-forward (FF) layers. (b) c) (IA)$^3$ : Rescaling operations performed within the MHA and FF layers. Module parameter generation.  Instead

Deep Learning 52

Deep Learning NLP Computer Vision Metadata 52