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cs.CL2025
Softplus Attention with Re-weighting Boosts Length Extrapolation in Large Language Models
Bo Gao, Michael W. Spratling, Letizia Gionfrida
Large language models have achieved remarkable success in recent years, primarily due to self-attention. However, traditional Softmax attention suffers from numerical instability a…
cs.LG2025
HEM: a margin-based loss for visual categorisation tasks
Michael W. Spratling, Heiko H. Schütt
Training deep neural networks (DNNs) on classification tasks can be performed with a number of different losses, but cross-entropy (CE) loss is the de-facto standard. Here, we prop…