11 citations · 21 across the 19 of their papers we have counts for
5 papers · 1 filter
OTOv3: Automatic Architecture-Agnostic Neural Network Training and Compression from Structured Pruning to Erasing Operators
Tianyi Chen, Tianyu Ding, Zhihui Zhu +4
Compressing a predefined deep neural network (DNN) into a compact sub-network with competitive performance is crucial in the efficient machine learning realm. This topic spans vari…
The Efficiency Spectrum of Large Language Models: An Algorithmic Survey
Tianyu Ding, Tianyi Chen, Haidong Zhu +7
The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains, reshaping the artificial general intelligence landscape. However, the inc…
DREAM: Diffusion Rectification and Estimation-Adaptive Models
Jinxin Zhou, Tianyu Ding, Tianyi Chen +4
We present DREAM, a novel training framework representing Diffusion Rectification and Estimation Adaptive Models, requiring minimal code changes (just three lines) yet significantl…
CaesarNeRF: Calibrated Semantic Representation for Few-shot Generalizable Neural Rendering
Haidong Zhu, Tianyu Ding, Tianyi Chen +3
Generalizability and few-shot learning are key challenges in Neural Radiance Fields (NeRF), often due to the lack of a holistic understanding in pixel-level rendering. We introduce…
LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery
Tianyi Chen, Tianyu Ding, Badal Yadav +2
Large Language Models (LLMs) have transformed the landscape of artificial intelligence, while their enormous size presents significant challenges in terms of computational costs. W…