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During the deep learning era, attention mechanism was developed to solve similar problems in encoding-decoding. [1]In machine translation, the seq2seq model, as it was proposed in 2014, [24] would encode an input text into a fixed-length vector, which would then be decoded into an output text.
For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...
Multi-head attention enhances this process by introducing multiple parallel attention heads. Each attention head learns different linear projections of the Q, K, and V matrices. This allows the model to capture different aspects of the relationships between words in the sequence simultaneously, rather than focusing on a single aspect.
Developed multi-head latent attention (MLA). Also used mixture of experts (MoE). DeepSeek V3 Dec 2024 DeepSeek-V3-Base DeepSeek-V3 (a chat model) The architecture is essentially the same as V2. DeepSeek R1 20 Nov 2024 DeepSeek-R1-Lite-Preview Only accessed through API and a chat interface. 20 Jan 2025 DeepSeek-R1 DeepSeek-R1-Zero
DeepMind is known to have trained the program on over 170,000 proteins from the Protein Data Bank, a public repository of protein sequences and structures.The program uses a form of attention network, a deep learning technique that focuses on having the AI identify parts of a larger problem, then piece it together to obtain the overall solution. [2]
The safetensors format was developed around 2021 to solve problems with using Python's pickle format (that was then used in PyTorch). It was designed for saving and loading tensors. Compared to pickle format, it allows lazy loading, and avoids security problems. [21] After a security audit, it became the default format in 2023. [22]
If a multilayer perceptron has a linear activation function in all neurons, that is, a linear function that maps the weighted inputs to the output of each neuron, then linear algebra shows that any number of layers can be reduced to a two-layer input-output model.
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".