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pro vyhledávání: '"Agostinelli, Victor"'
In scientific fields such as quantum computing, physics, chemistry, and machine learning, high dimensional data are typically represented using sparse tensors. Tensor contraction is a popular operation on tensors to exploit meaning or alter the input
Externí odkaz:
http://arxiv.org/abs/2410.10094
A promising approach to preserving model performance in linearized transformers is to employ position-based re-weighting functions. However, state-of-the-art re-weighting functions rely heavily on target sequence lengths, making it difficult or impos
Externí odkaz:
http://arxiv.org/abs/2405.13046
Large language models (LLMs) have achieved state-of-the-art performance in various language processing tasks, motivating their adoption in simultaneous translation. Current fine-tuning methods to adapt LLMs for simultaneous translation focus on promp
Externí odkaz:
http://arxiv.org/abs/2405.10443
Large language models (LLMs) with billions of parameters and pretrained on massive amounts of data are now capable of near or better than state-of-the-art performance in a variety of downstream natural language processing tasks. Neural machine transl
Externí odkaz:
http://arxiv.org/abs/2312.04691
Compactness in deep learning can be critical to a model's viability in low-resource applications, and a common approach to extreme model compression is quantization. We consider Iterative Product Quantization (iPQ) with Quant-Noise to be state-of-the
Externí odkaz:
http://arxiv.org/abs/2306.14031
Autor:
Agostinelli, Victor, Chen, Lizhong
Various natural language processing (NLP) tasks necessitate models that are efficient and small based on their ultimate application at the edge or in other resource-constrained environments. While prior research has reduced the size of these models,
Externí odkaz:
http://arxiv.org/abs/2304.08453