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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.
Shannon's diagram of a general communications system, showing the process by which a message sent becomes the message received (possibly corrupted by noise). seq2seq is an approach to machine translation (or more generally, sequence transduction) with roots in information theory, where communication is understood as an encode-transmit-decode process, and machine translation can be studied as a ...
Inception [1] is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed Inception v1).). The series was historically important as an early CNN that separates the stem (data ingest), body (data processing), and head (prediction), an architectural design that persists in all modern
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]
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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 ] Early life and education
The model was trained with back-propagation. The training algorithm was further improved in 1991 [54] to improve its generalization ability. The model architecture was modified by removing the last fully connected layer and applied for medical image segmentation (1991) [50] and automatic detection of breast cancer in mammograms (1994). [51]
Since the model relies on Query (Q), Key (K) and Value (V) matrices that come from the same source itself (i.e. the input sequence / context window), this eliminates the need for RNNs completely ensuring parallelizability for the architecture. This differs from the original form of the Attention mechanism introduced in 2014.