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Mikołaj Piórczyński

2 accepted papers

2026

Universal Properties of Activation Sparsity in Modern Large Language Models

ICLR 2026poster

Activation sparsity is an intriguing property of deep neural networks that has been extensively studied in ReLU-based models, due to its advantages for efficiency, robustness, and interpretability. However, methods relying on exact zero activations do not directly apply to modern Large Language Mod…

Cited by 0SourceScholar
2024

Exploiting Activation Sparsity with Dense to Dynamic-k Mixture-of-Experts Conversion

NeurIPS 2024poster

Transformer models can face practical limitations due to their high computational requirements. At the same time, such models exhibit significant activation sparsity, which can be leveraged to reduce the inference cost by converting parts of the network into equivalent Mixture-of-Experts (MoE) layer…

Cited by 3SourcePDFScholar