75 citations · 133 across the 7 of their papers we have counts for
7 papers
Mortgage Language Model: Domain-Adaptive Pretraining with Residual Instruction, Alignment Tuning, and Task-Specific Routing
Manish Jain, Satheesh Kumar Ponnambalam, Salman Faroz +2
Large Language Models (LLMs) demonstrate exceptional capabilities across general domains, yet their application to specialized sectors such as mortgage finance requires domain-spec…
Introducing v0.5 of the AI Safety Benchmark from MLCommons
Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed +97
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safe…
Tuna: A Static Analysis Approach to Optimizing Deep Neural Networks
Yao Wang, Xingyu Zhou, Yanming Wang +3
We introduce Tuna, a static analysis approach to optimizing deep neural network programs. The optimization of tensor operations such as convolutions and matrix multiplications is t…
Bring Your Own Codegen to Deep Learning Compiler
Zhi Chen, Cody Hao Yu, Trevor Morris +6
Deep neural networks (DNNs) have been ubiquitously applied in many applications, and accelerators are emerged as an enabler to support the fast and efficient inference tasks of the…
Efficient Execution of Quantized Deep Learning Models: A Compiler Approach
Animesh Jain, Shoubhik Bhattacharya, Masahiro Masuda +2
A growing number of applications implement predictive functions using deep learning models, which require heavy use of compute and memory. One popular technique for increasing reso…
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…