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Random Search Hyper-Parameter Tuning: Expected Improvement Estimation and the Corresponding Lower Bound
However, the expected accuracy improvement from every additional search iteration, is still unknown.
Transformer Vs. MLP-Mixer: Exponential Expressive Gap For NLP Problems
Vision-Transformers are widely used in various vision tasks.
The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design
We highlight a bias introduced by this common practice: we prove that the pretrained NLM can model much stronger dependencies between text segments that appeared in the same training example, than it can between text segments that appeared in different training examples.
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