8 citations · 14 across the 4 of their papers we have counts for
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cs.LG2023★ 2 cited
SparQ Attention: Bandwidth-Efficient LLM Inference
Luka Ribar, Ivan Chelombiev, Luke Hudlass-Galley +3
The computational difficulties of large language model (LLM) inference remain a significant obstacle to their widespread deployment. The need for many applications to support long…
cs.LG2019★ 8 cited
Adaptive Estimators Show Information Compression in Deep Neural Networks
Ivan Chelombiev, Conor Houghton, Cian O'Donnell
To improve how neural networks function it is crucial to understand their learning process. The information bottleneck theory of deep learning proposes that neural networks achieve…