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cs.LG2026
Gate the Filter, Not the Message: Node-Channel Mixtures for Pre-Propagation GNNs
Zichao Yue, Zhiru Zhang
Pre-propagation graph neural networks (PPGNNs) push all graph-dependent computation into a preprocessing step and train only on the resulting dense hop features, which makes them h…
cs.LG2024
Learning from Students: Applying t-Distributions to Explore Accurate and Efficient Formats for LLMs
Jordan Dotzel, Yuzong Chen, Bahaa Kotb +5
The increasing size of large language models (LLMs) traditionally requires low-precision integer formats to meet strict latency and power demands. Yet recently, alternative formats…
cs.LG2024
ShadowLLM: Predictor-based Contextual Sparsity for Large Language Models
Yash Akhauri, Ahmed F AbouElhamayed, Jordan Dotzel +4
The high power consumption and latency-sensitive deployments of large language models (LLMs) have motivated efficiency techniques like quantization and sparsity. Contextual sparsit…