Search Results for author:
Found 7 papers, 3 papers with code
Atom: Low-bit Quantization for Efficient and Accurate LLM Serving
To maximize LLMs' serving throughput, we introduce Atom, a low-bit quantization method that achieves high throughput improvements with negligible accuracy loss.
SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic Networks
In this paper, we perform an empirical evaluation on methods for sharing parameters in isotropic networks (SPIN).
Encode Once and Decode in Parallel: Efficient Transformer Decoding
Transformer-based NLP models are powerful but have high computational costs that limit deployment scenarios.
Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search
This is the goal of our work, where we introduce stratified organ at risk segmentation (SOARS), an approach that stratifies OARs into anchor, mid-level, and small & hard (S&H) categories.
Accelerating SpMM Kernel with Cache-First Edge Sampling for Graph Neural Networks
To evaluate ES-SpMM's performance, we integrated it with a popular GNN framework, DGL, and tested it using representative GNN models and datasets.
Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study
Accurate organ at risk (OAR) segmentation is critical to reduce the radiotherapy post-treatment complications.
FastSR-NeRF: Improving NeRF Efficiency on Consumer Devices with A Simple Super-Resolution Pipeline
Compared to existing NeRF+SR methods, our pipeline mitigates the SR computing overhead and can be trained up to 23x faster, making it feasible to run on consumer devices such as the Apple MacBook.
