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The idea of using the attention mechanism for self-attention, instead of in an encoder-decoder (cross-attention), was also proposed during this period, such as in differentiable neural computers [29] and neural Turing machines. [30] It was termed intra-attention [31] where an LSTM is augmented with a memory network as it encodes an input sequence.
Each encoder layer consists of two major components: a self-attention mechanism and a feed-forward layer. It takes an input as a sequence of input vectors, applies the self-attention mechanism, to produce an intermediate sequence of vectors, then applies the feed-forward layer for each vector individually.
Scaled dot-product attention & self-attention. The use of the scaled dot-product attention and self-attention mechanism instead of a Recurrent neural network or Long short-term memory (which rely on recurrence instead) allow for better performance as described in the following paragraph. The paper described the scaled-dot production as follows:
Multihead attention pooling (MAP) applies a multiheaded attention block to pooling. Specifically, it takes as input a list of vectors x 1 , x 2 , … , x n {\displaystyle x_{1},x_{2},\dots ,x_{n}} , which might be thought of as the output vectors of a layer of a ViT.
Multihead attention pooling (MAP) applies a multiheaded attention block to pooling. Specifically, it takes as input a list of vectors x 1 , x 2 , … , x n {\displaystyle x_{1},x_{2},\dots ,x_{n}} , which might be thought of as the output vectors of a layer of a ViT.
The impacted companies are sure to evolve in the weeks ahead as Musk and the DOGE team turn their attention to other areas, especially the $850 billion Department of Defense. Both DOGE and Defense ...
The tactic is called "sticky eyes" and Chelsea Anderson, a TikToker and self-described "professional life hacker," breaks it down in a video with over 6.7 million views and 800,000 likes.
Synthesize 200K non-reasoning data (writing, factual QA, self-cognition, translation) using DeepSeek-V3. SFT DeepSeek-V3-Base on the 800K synthetic data for 2 epochs. Apply the same GRPO RL process as R1-Zero with rule-based reward (for reasoning tasks), but also model-based reward (for non-reasoning tasks, helpfulness, and harmlessness).