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Found 8 papers, 0 papers with code
ILMPQ : An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA
Our proposed ILMPQ DNN quantization framework achieves 70. 73 Top1 accuracy in ResNet-18 on the ImageNet dataset.
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions
Specifically, this is the first effort to assign mixed quantization schemes and multiple precisions within layers -- among rows of the DNN weight matrix, for simplified operations in hardware inference, while preserving accuracy.
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm
Conventional wisdom in pruning Transformer-based language models is that pruning reduces the model expressiveness and thus is more likely to underfit rather than overfit.
Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework
Unlike existing methods that use the same quantization scheme for all weights, we propose the first solution that applies different quantization schemes for different rows of the weight matrix.
MSP: An FPGA-Specific Mixed-Scheme, Multi-Precision Deep Neural Network Quantization Framework
To tackle the limited computing and storage resources in edge devices, model compression techniques have been widely used to trim deep neural network (DNN) models for on-device inference execution.
MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization
We consider a generalization of mixed regression where the response is an additive combination of several mixture components.
Efficient Tensor Decomposition with Boolean Factors
Tensor decomposition has been extensively used as a tool for exploratory analysis.
Latent Feature Lasso
The latent feature model (LFM), proposed in (Griffiths \& Ghahramani, 2005), but possibly with earlier origins, is a generalization of a mixture model, where each instance is generated not from a single latent class but from a combination of latent features.
