(one of) can't scale like that or use
Posted: Mon Dec 23, 2024 10:38 am
As a compression heuristic update rule, it is necessary to discover the underlying structure and relationship between thousands or even millions of k. The researchers first observed that self-supervised learning can compress a large training set into the weights of the model, which usually shows a deep understanding of the semantic connections between its training data, which is exactly what they need. . Inspired by this, the researchers designed a new class of sequence modeling layers in which the hidden state is the model update rule is a step in self-supervised learning. Since the process of updating the hidden state on the test sequence is equivalent to training the model at test time, this new layer is called a test-time training layer.
The researchers introduced two simple japan mobile number instances: - and - where the hidden states are linear models and two layers respectively. The layer can be integrated into any network architecture and optimized end-to-end similar to the layer and self-attention. . The actual running time layer is already very efficient in terms of . The researchers have taken it a step further by proposing two innovations to make it efficient in actual running time. First, similar to the gradient stepping of the - sequence in normal training to achieve better parallelism, they also use -'s k.
Second, the researchers developed a dual form for each operation in - to better utilize modern and . The output of this dual form is comparable to the original implementation but the training speed is more than times faster. As shown in the figure, - is faster than in the k context and comparable to . The killer - As shown in the figure, all sequence modeling layers can be viewed from the perspective of storing historical context in hidden states. For example, layers - such as, K, and layers - compress the context into a fixed-size state that changes over time.
The researchers introduced two simple japan mobile number instances: - and - where the hidden states are linear models and two layers respectively. The layer can be integrated into any network architecture and optimized end-to-end similar to the layer and self-attention. . The actual running time layer is already very efficient in terms of . The researchers have taken it a step further by proposing two innovations to make it efficient in actual running time. First, similar to the gradient stepping of the - sequence in normal training to achieve better parallelism, they also use -'s k.
Second, the researchers developed a dual form for each operation in - to better utilize modern and . The output of this dual form is comparable to the original implementation but the training speed is more than times faster. As shown in the figure, - is faster than in the k context and comparable to . The killer - As shown in the figure, all sequence modeling layers can be viewed from the perspective of storing historical context in hidden states. For example, layers - such as, K, and layers - compress the context into a fixed-size state that changes over time.