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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. It was developed in 2015 for image recognition , and won the ImageNet Large Scale Visual Recognition Challenge ( ILSVRC ) of that year.
The paper was accompanied by a software package written in TensorFlow release on GitHub. [10] It was reimplemented in PyTorch by lucidrains. [11] [12] On December 20, 2021, the LDM paper was published on arXiv, [13] and both Stable Diffusion [14] and LDM [15] repositories were published on GitHub. However, they remained roughly the same.
Block locally optimal multi-step steepest descent for eigenvalue problems was described in. [3] Local minimization of the Rayleigh quotient on the subspace spanned by the current approximation, the current residual and the previous approximation, as well as its block version, appeared in. [4] The preconditioned version was analyzed in [5] and. [6]
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 ...
The building block of RNNs is the recurrent unit. This unit maintains a hidden state, essentially a form of memory, which is updated at each time step based on the current input and the previous hidden state. This feedback loop allows the network to learn from past inputs, and incorporate that knowledge into its current processing.
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.
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]
Countermeasures such as skip connections [10] [38] (as in residual neural networks), gated update rules [39] and jumping knowledge [40] can mitigate oversmoothing. Modifying the final layer to be a fully-adjacent layer, i.e., by considering the graph as a complete graph , can mitigate oversquashing in problems where long-range dependencies are ...