We introduce TQCompressor, a novel method for neural network model compression with improved tensor decompositions. We explore the challenges posed by the computational and storage demands of pre-trained language models in NLP tasks and propose a permutation-based enhancement to Kronecker decomposition. This enhancement makes it possible to reduce loss in model expressivity which is usually associated with factorization. We demonstrate this method applied to the GPT-2$_{small}$. The result of the compression is TQCompressedGPT-2 model, featuring 81 mln. parameters compared to 124 mln. in the GPT-2$_{small}$. We make TQCompressedGPT-2 publicly available. We further enhance the performance of the TQCompressedGPT-2 through a training strategy involving multi-step knowledge distillation, using only a 3.1% of the OpenWebText. TQCompressedGPT-2 surpasses DistilGPT-2 and KnGPT-2 in comparative evaluations, marking an advancement in the efficient and effective deployment of models in resource-constrained environments.
McGill University（麦吉尔大学）；Mila Quebec AI Institute（米拉魁北克人工智能研究所）；RWTH Aachen University（亚琛工业大学）；Max Planck Institute for Software Systems（马克斯·普朗克软件系统研究所）
Hypothesis Testing and Machine Learning: Interpreting Variable Effects in Deep Artificial Neural Networks using Cohen's f2
University of South Carolina（南卡罗来纳大学）；University of Kassel（卡塞尔大学）；University of Wales（威尔士大学）；Technical University Munich（慕尼黑工业大学）；University of Newcastle upon Tyne（纽卡斯尔大学）；Università per Stranieri di Perugia（佩鲁贾外国人大学）
Improving the Accuracy and Interpretability of Neural Networks for Wind Power Forecasting
Ecole Polytechnique Federale de Lausanne (EPFL)（洛桑联邦理工学院）；Aalborg University（奥尔堡大学）；The Hong Kong Polytechnic University（香港理工大学）；The University of Hong Kong（香港大学）
