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Papershelf

A guide to convolution arithmetic for deep learning
Dumoulin & Visin·2016
arXiv·Math·Computer Vision·Deep Learning
A White Paper on Neural Network Quantization
Nagel et al.·2021
arXiv·Model Optimization·Deep Learning
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
Lin et al.·2023
arXiv·Model Optimization·Deep Learning
Generating Sequences With Recurrent Neural Networks
Alex Graves·2013
arXiv·RNN·NLP·Deep Learning
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
Frantar et al.·2022
arXiv·Model Optimization·Deep Learning
Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning
Aghajanyan et al.·2020
arXiv·Math·Deep Learning
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Cho et al.·2014
arXiv·RNN·NLP·Deep Learning
LoRA: Low-Rank Adaptation of Large Language Models
Hu et al.·2021
arXiv·Fine Tuning·Deep Learning
Sequence to Sequence Learning with Neural Networks
Sutskever et al.·2014
arXiv·RNN·NLP·Deep Learning
The Matrix Calculus You Need For Deep Learning
Parr & Howard·2018
arXiv·Math·Deep Learning