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A basic block is the simplest building block studied in the original ResNet. [1] This block consists of two sequential 3x3 convolutional layers and a residual connection. The input and output dimensions of both layers are equal. Block diagram of ResNet (2015). It shows a ResNet block with and without the 1x1 convolution.
Modern activation functions include the smooth version of the ReLU, the GELU, which was used in the 2018 BERT model, [2] the logistic function used in the 2012 speech recognition model developed by Hinton et al, [3] the ReLU used in the 2012 AlexNet computer vision model [4] [5] and in the 2015 ResNet model.
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]
Physics-informed neural networks for solving Navier–Stokes equations. Physics-informed neural networks (PINNs), [1] also referred to as Theory-Trained Neural Networks (TTNs), [2] are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations (PDEs).
As an example, a single 5×5 convolution can be factored into 3×3 stacked on top of another 3×3. Both has a receptive field of size 5×5. The 5×5 convolution kernel has 25 parameters, compared to just 18 in the factorized version. Thus, the 5×5 convolution is strictly more powerful than the factorized version.
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A decade ago, the SEC was a football-first conference. Today, it is unquestionably the strongest basketball conference in the country.
He is an associate professor at Massachusetts Institute of Technology and is known as one of the creators of residual neural network (ResNet). [1] [3]