Deep Learning
New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
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Adversarial Training from Mean Field Perspective
Neural Information Processing Systems
We also show that the network width alleviates these issues.
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Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing
Neural Information Processing Systems
Due to their size, the capability of these networks has increased tremendously, but this has come at the cost of a significant increase in necessary compute.
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Mixture of Tokens: Continuous MoE through Cross-Example Aggregation
Neural Information Processing Systems
Most widely adopted MoE models are discontinuous with respect to their parameters - often referred to as sparse .
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