LSTM is a popular RNN architecture, which was introduced by Sepp Hochreiter and Juergen Schmidhuber as a solution to the vanishing gradient problem. This work addressed the problem of long-term dependencies. That is, if the previous state that is influencing the current prediction is not in the...
Autoencoders have two main parts: an encoder and a decoder. The encoder maps the input into code and the decoder maps the code to a reconstruction of the input. The code is sometimes considered a third part as “the original data goes into a coded result, and the subsequent layers of ...
Theencoderanddecoderblocks in a transformer model include multiple layers that form the neural network for the model. We don't need to go into the details of all these layers, but it's useful to consider one of the types of layers that is used in both blocks:attentionlayers. Attention is...
NLP is especially useful in fully or partiallyautomating taskslike customer support, data entry and document handling. For example, NLP-powered chatbots can handle routine customer queries, freeing up human agents for more complex issues. Indocument processing, NLP tools can automatically classify, ex...
In this post, we introduce the encoder decoder structure in some cases known as Sequence to Sequence (Seq2Seq) model. For a better understanding of the structure of this model, previous knowledge on…
They consist of encoder and decoder networks, each of which may use a different underlying architecture, such as RNN, CNN, or transformer. The encoder learns the important features and characteristics of an image, compresses that information, and stores it as a representation in memory. The ...
The combination of these modules forms a decoder laye. The final output from the decoder is the context-aware representations of each token in the sequence. Self-Supervised Pre-Training The original purpose of transformers was for sequence-to-sequence tasks. However, their wide application and popu...
特别是之前的序列模型,如encoder-decoder模型,在捕捉长期依赖关系和并行计算方面能力有限。在2017年《Transformer》论文发表之前,大多数NLP任务都是通过使用带有注意力机制的RNN来获得的,所以Attention在Transformer之前就存在了。通过单独引入多头注意力机制,并放弃RNN部分,transformer架构通过允许多个独立的注意力机制,来从不...
In some LLMs, decoder layers come into play. Though not essential in every model, they bring the advantage of autoregressive generation, where the output is informed by previously processed tokens. This capability makes text generation smoother and more contextually relevant. Multi-head attentio...
The model consists of two parts: the encoder and the decoder. The encoder is a feedforward, fully connected neural network that transforms the input vector, containing the interactions for a specific user, into an n-dimensional variational distribution. This variational distribution is used to obtai...