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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 ...
The models and the code were released under Apache 2.0 license on GitHub. [4] An individual Inception module. On the left is a standard module, and on the right is a dimension-reduced module. A single Inception dimension-reduced module. The Inception v1 architecture is a deep CNN composed of 22 layers. Most of these layers were "Inception modules".
DeepDream is a computer vision program created by Google engineer Alexander Mordvintsev that uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia, thus creating a dream-like appearance reminiscent of a psychedelic experience in the deliberately overprocessed images.
Example of a system context diagram. [1] A system context diagram in engineering is a diagram that defines the boundary between the system, or part of a system, and its environment, showing the entities that interact with it. [2] This diagram is a high level view of a system. It is similar to a block diagram.
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]
Other than language models, Vision MoE [36] is a Transformer model with MoE layers. They demonstrated it by training a model with 15 billion parameters. MoE Transformer has also been applied for diffusion models. [37] A series of large language models from Google used MoE. GShard [38] uses MoE with up to top-2 experts per layer. Specifically ...
This produces a rather pathological loss landscape: as approach from above, the loss approaches zero, but as soon as crosses , the attractor basin changes, and loss jumps to 0.50. [ note 4 ] Consequently, attempting to train b {\displaystyle b} by gradient descent would "hit a wall in the loss landscape", and cause exploding gradient.