The proposed cross-frame multi-object tracking transformer (CFTforrmer ... This approach allows the encoder-decoder to track the queries more efficiently across the frames. For this model, scalable ...

However, conventional deep learning-based multi-input ... Lightweight Transformer encoders are deployed at resource-constrained BSs to compress received signals, which are then forwarded to a central ...

Finally understand how encoder blocks work in transformers, with a step-by-step guide that makes it all click. #AI ...

Welcome to Learn with Jay – your go-to channel for mastering new skills and boosting your knowledge! Whether it’s personal ...

In this article, we demonstrate how to do this by creating a verifiable and replicable method of prompt science that aids in prompt engineering. Specifically, we take inspiration from a rich ...

Mixture-of-Experts (MoE) models are revolutionizing the way we scale AI. By activating only a subset of a model’s components ...

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We introduce Mixture-of-Transformers (MoT), a sparse architecture with modality-aware sparsity for every non-embedding transformer parameter (e.g., feed-forward networks, attention matrices, and layer ...