arXiv:1906.06329
공식 코드 · Papers with Code
TensorNetwork for Machine Learning
원문 초록
We demonstrate the use of tensor networks for image classification with the TensorNetwork open source library. We explain in detail the encoding of image data into a matrix product state form, and describe how to contract the network in a way that is parallelizable and well-suited to automatic gradients for optimization. Applying the technique to the MNIST and Fashion-MNIST datasets we find out-of-the-box performance of 98% and 88% accuracy, respectively, using the same tensor network architecture. The TensorNetwork library allows us to seamlessly move from CPU to GPU hardware, and we see a factor of more than 10 improvement in computational speed using a GPU.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Performing Image Classification for 10 Different Monkey Species using CNN
FrImCla: A Framework for Image Classification Using Traditional and Transfer Learning Techniques
Enhanced network anomaly detection based on deep neural networks
MathOptAI.jl: Embed trained machine learning predictors into JuMP models
ConsumerBench: Benchmarking Generative AI Applications on End-User Devices
LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs
토론
이 논문과 연결된 포럼 글이 아직 없습니다.