Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness | Paper Citation Record

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This paper cites Pattern recognition and machine learning.

Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Pattern recognition and machine learning

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Understanding and Overcoming the Challenges of Efficient Transformer Quantization

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This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, march 2023.

Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, march 2023

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This paper cites A comparative survey of instance selection methods applied to non-neural and transformer-based text classification.

Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness A comparative survey of instance selection methods applied to non-neural and transformer-based text classification

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This paper cites Latent-Variable Generative Models for Data-Efficient Text Classification.

Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Latent-Variable Generative Models for Data-Efficient Text Classification

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Matrix completion via memoryless scalar quantization

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Learned Step Size Quantization

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Xilinx/brevitas

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness The elements of statistical learning: data mining, inference, and prediction

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Long short-term memory

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness spacy: Industrial-strength natural language processing in python

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Fastai: A layered api for deep learning

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Empirical Evaluation of Post-Training Quantization Methods for Language Tasks

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This paper cites Accurate post training quantization with small calibration sets, in: International Conference on Machine Learning, PMLR.

Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Accurate post training quantization with small calibration sets, in: International Conference on Machine Learning, PMLR

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Speech and Language Processing

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Sulla determinazione empirica di una legge di distribuzione

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This paper cites Generative models improve fairness of medical classifiers under distribution shifts.

Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Generative models improve fairness of medical classifiers under distribution shifts

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This paper cites Precision and recall metrics for generative models, in: Advances in Neural Information Processing Systems.

Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Precision and recall metrics for generative models, in: Advances in Neural Information Processing Systems

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This paper cites Robust inference via generative classifiers for handling noisy labels, in: Proceedings of the 36th International Conference on Machine Learning (ICML).

Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Robust inference via generative classifiers for handling noisy labels, in: Proceedings of the 36th International Conference on Machine Learning (ICML)

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness FP8-BERT: Post-Training Quantization for Transformer

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This paper cites On the Impact of Calibration Data in Post-training Quantization and Pruning.

Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness On the Impact of Calibration Data in Post-training Quantization and Pruning

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Are Generative Classifiers More Robust to Adversarial Attacks?

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness SpinQuant: LLM quantization with learned rotations

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness A Greedy Algorithm for Quantizing Neural Networks

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Efficient Estimation of Word Representations in Vector Space

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Up or Down? Adaptive Rounding for Post-Training Quantization

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This paper cites Data-free quantization through weight equalization and bias correction, in: Proceedings of the IEEE/CVF international conference on computer vision, pp.

Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Data-free quantization through weight equalization and bias correction, in: Proceedings of the IEEE/CVF international conference on computer vision, pp

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness A White Paper on Neural Network Quantization

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness On discriminative vs

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Deep neural networks are easily fooled: High confidence predictions for unrecognizable images, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness On estimation of a probability density function and mode

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness PyTorch: An Imperative Style, High-Performance Deep Learning Library

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness GloVe: Global vectors for word representation, in: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Remarks on some nonparametric estimates of a density function

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Generative and Discriminative Deep Belief Network Classifiers: Comparisons Under an Approximate Computing Framework

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Table for estimating the goodness of fit of empirical distributions

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This paper cites How to fine-tune bert for text classification?, in: China national conference on Chinese computational linguistics, Springer.

Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness How to fine-tune bert for text classification?, in: China national conference on Chinese computational linguistics, Springer

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness LLaMA: Open and Efficient Foundation Language Models

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Attention is all you need

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness HAQ: Hardware-Aware Automated Quantization with Mixed Precision

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness The effect of class imbalance on precision-recall curves

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Easyquant: Post-training quantization via scale optimization, in: CVPR

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Zeroquant: Efficient and affordable post-training quantization for large-scale transformers

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Generative and Discriminative Text Classification with Recurrent Neural Networks

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Q8BERT: Quantized 8Bit BERT

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness OPT: Open Pre-trained Transformer Language Models

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Qronos: Correcting the past by shaping the future

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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Learning low-precision structured subnetworks using joint layerwise channel pruning and uniform quantization

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This paper cites Character-level Convolutional Networks for Text Classification.

Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Character-level Convolutional Networks for Text Classification

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This paper cites A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification.

Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification

Reference 50

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This paper cites Selectq: Calibration data selection for post-training quantization.

Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Selectq: Calibration data selection for post-training quantization

Reference 51

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