17 citations · 28 across the 6 of their papers we have counts for
6 papers
ALT: Boosting Deep Learning Performance by Breaking the Wall between Graph and Operator Level Optimizations
Zhiying Xu, Jiafan Xu, Hongding Peng +8
Deep learning models rely on highly optimized tensor libraries for efficient inference on heterogeneous hardware. Current deep compilers typically predetermine layouts of tensors a…
Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge
Kaixin Ma, Hao Cheng, Xiaodong Liu +2
We propose a novel open-domain question answering (ODQA) framework for answering single/multi-hop questions across heterogeneous knowledge sources. The key novelty of our method is…
Efficient Multi-Prize Lottery Tickets: Enhanced Accuracy, Training, and Inference Speed
Hao Cheng, Pu Zhao, Yize Li +4
Recently, Diffenderfer and Kailkhura proposed a new paradigm for learning compact yet highly accurate binary neural networks simply by pruning and quantizing randomly weighted full…
Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing
Robert Tinn, Hao Cheng, Yu Gu +5
Motivation: A perennial challenge for biomedical researchers and clinical practitioners is to stay abreast with the rapid growth of publications and medical notes. Natural language…
Knowledge-Rich Self-Supervision for Biomedical Entity Linking
Sheng Zhang, Hao Cheng, Shikhar Vashishth +6
Entity linking faces significant challenges such as prolific variations and prevalent ambiguities, especially in high-value domains with myriad entities. Standard classification ap…
Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention
Yichong Xu, Chenguang Zhu, Shuohang Wang +7
Most of today's AI systems focus on using self-attention mechanisms and transformer architectures on large amounts of diverse data to achieve impressive performance gains. In this…