8 citations · 11 across the 10 of their papers we have counts for
5 papers · 1 filter
Test-Time Adaptation of Quantized ViTs via Single-Pass Quantizer-Aligned Recalibration
Hyeongheon Cha, Young D. Kwon, Sung-Ju Lee
Post-training quantization is a standard route to fitting vision transformers (ViTs) into edge compute and memory budgets, yet quantized models become especially brittle under dist…
Monte Carlo Estimation for KV Cache Eviction
Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer +5
Most KV-cache eviction methods ask, in effect, which memory appeared important while reading the prompt? We instead ask which memory will matter while answering? Since decoding que…
On the Interaction Between Model Compression and Test-Time Adaptation
Francesco Corti, Dong Wang, Young D. Kwon +2
Deep neural networks deployed in the wild must be both efficient and adaptable, requiring model compression and test-time adaptation (TTA). While both are well studied in isolation…
Speculative Decoding with a Speculative Vocabulary
Miles Williams, Young D. Kwon, Rui Li +2
Speculative decoding has rapidly emerged as a leading approach for accelerating language model (LM) inference, as it offers substantial speedups while yielding identical outputs. T…
Tempora: Characterising the Time-Contingent Utility of Online Test-Time Adaptation
Sudarshan Sreeram, Young D. Kwon, Cecilia Mascolo
Test-time adaptation (TTA) offers a compelling remedy for machine learning (ML) models that degrade under domain shifts, improving generalisation on-the-fly with only unlabelled sa…