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20202026
most citedUnder the Hood of Neural Networks: Characterizing Learned Representations by Functional Neuron Populations and Network Ablations

13 citations · 15 across the 5 of their papers we have counts for

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cs.LG2026

Efficiently Approximating Attention Is Hard

Lukas Haverbeck, Carmen Amo Alonso, Andres Felipe Posada-Moreno +2

Softmax attention is ubiquitous in modern machine learning, but its quadratic scaling with sequence length makes it costly. To reduce this cost, attention is often approximated wit…

cs.LG2026

The risk of KV cache compression

Lukas Haverbeck, Carmen Amo Alonso, Andres Felipe Posada-Moreno +2

Transformer inference on long sequences is expensive because softmax attention repeatedly reads from a large KV cache. The prevalent approach to this bottleneck is KV cache compres…