135 citations · 167 across the 6 of their papers we have counts for
6 papers
RAF: Holistic Compilation for Deep Learning Model Training
Cody Hao Yu, Haozheng Fan, Guangtai Huang +9
As deep learning is pervasive in modern applications, many deep learning frameworks are presented for deep learning practitioners to develop and train DNN models rapidly. Meanwhile…
Making Distributed Mobile Applications SAFE: Enforcing User Privacy Policies on Untrusted Applications with Secure Application Flow Enforcement
Adriana Szekeres, Irene Zhang, Katelin Bailey +6
Today's mobile devices sense, collect, and store huge amounts of personal information, which users share with family and friends through a wide range of applications. Once users gi…
Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference
Haichen Shen, Jared Roesch, Zhi Chen +6
Modern deep neural networks increasingly make use of features such as dynamic control flow, data structures and dynamic tensor shapes. Existing deep learning systems focus on optim…
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
Tianqi Chen, Thierry Moreau, Ziheng Jiang +9
There is an increasing need to bring machine learning to a wide diversity of hardware devices. Current frameworks rely on vendor-specific operator libraries and optimize for a narr…
Fast Video Classification via Adaptive Cascading of Deep Models
Haichen Shen, Seungyeop Han, Matthai Philipose +1
Recent advances have enabled "oracle" classifiers that can classify across many classes and input distributions with high accuracy without retraining. However, these classifiers ar…
Visualizing NLP annotations for Crowdsourcing
Hanchuan Li, Haichen Shen, Shengliang Xu +1
Visualizing NLP annotation is useful for the collection of training data for the statistical NLP approaches. Existing toolkits either provide limited visual aid, or introduce compr…