Breaking the Curse of Dimensionality: A Game-Changer for Large-Scale Multi-Task Learning
The Transformer architecture has revolutionized the field of natural language processing (NLP) and beyond, achieving state-of-the-art results in a wide range of tasks. However, its reliance on self-attention mechanisms comes with a significant cost: memory requirements that skyrocket with the size of the input. This limitation has made it challenging to apply Transformer-based models to large-scale multi-task learning, where the model needs to process vast amounts of data and multiple tasks simultaneously.
The Curse of Dimensionality
The curse of dimensionality refers to the phenomenon where the number of data points required to accurately mo...
Breaking the Curse of Dimensionality: A Game-Changer for Large-Scale Multi-Task Learning
The Transformer architecture has revolutionized the field of natural language processing (NLP) and beyond, achieving state-of-the-art results in a wide range of tasks. However, its reliance on self-attention mechanisms comes with a significant cost: memory requirements that skyrocket with the size of the input. This limitation has made it challenging to apply Transformer-based models to large-scale multi-task learning, where the model needs to process vast amounts of data and multiple tasks simultaneously.
The Curse of Dimensionality
The curse of dimensionality refers to the phenomenon where the number of data points required to accurately model a high-dimensional space grows exponentially with the dimensionality. In the context of Transformer-based models, this means that the number of attention weights, model parameters, and memory requirements increase exponentially with the input ...
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