Search results
Results from the WOW.Com Content Network
A residual block in a deep residual network. Here, the residual connection skips two layers. A residual neural network (also referred to as a residual network or ResNet) [1] is a deep learning architecture in which the layers learn residual functions with reference to the layer inputs.
Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. [1] The GRU is like a long short-term memory (LSTM) with a gating mechanism to input or forget certain features, [2] but lacks a context vector or output gate, resulting in fewer parameters than LSTM. [3]
Block diagram for the full Transformer architecture. The stack on the right is a standard pre-LN Transformer decoder, which is essentially the same as the SpatialTransformer. Similar to the standard U-Net, the U-Net backbone used in the SD 1.5 is essentially composed of down-scaling layers followed by up-scaling layers. However, the UNet ...
The initial version of LSTM block included cells, input and output gates. [20] (Felix Gers, Jürgen Schmidhuber, and Fred Cummins, 1999) [67] introduced the forget gate (also called "keep gate") into the LSTM architecture in 1999, enabling the LSTM to reset its own state. [20] This is the most commonly used version of LSTM nowadays.
One encoder-decoder block A Transformer is composed of stacked encoder layers and decoder layers. Like earlier seq2seq models, the original transformer model used an encoder-decoder architecture. The encoder consists of encoding layers that process all the input tokens together one layer after another, while the decoder consists of decoding ...
Mamba [a] is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University to address some limitations of transformer models, especially in processing long sequences. It is based on the Structured State Space sequence (S4) model.
The DeepSeek MoE architecture. Also shown is MLA, a variant of attention mechanism in Transformer. [23]: Figure 2 Researchers at DeepSeek designed a variant of MoE, with "shared experts" that are always queried, and "routed experts" that might not be. They found that standard load balancing encourages the experts to be equally consulted, but ...
Residual connections, or skip connections, refers to the architectural motif of +, where is an arbitrary neural network module. This gives the gradient of ∇ f + I {\displaystyle \nabla f+I} , where the identity matrix do not suffer from the vanishing or exploding gradient.