{"total":12,"rows":[{"id":"2205.07877","title":"A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification","created":"2022-05-14","cited_by_count":65,"area":null},{"id":"2503.19595","title":"Optimizing Language Models for Inference Time Objectives using Reinforcement Learning","created":"2025-03-25","cited_by_count":53,"area":null},{"id":"2211.04104","title":"Selective compression learning of latent representations for variable-rate image compression","created":"2022-11-08","cited_by_count":17,"area":null},{"id":"2107.11353","title":"Modelling Latent Translations for Cross-Lingual Transfer","created":"2021-07-23","cited_by_count":16,"area":null},{"id":"2106.10064","title":"Fitting summary statistics of neural data with a differentiable spiking network simulator","created":"2021-06-18","cited_by_count":5,"area":null},{"id":"2110.03549","title":"Bias-Variance Tradeoffs in Single-Sample Binary Gradient Estimators","created":"2021-10-07","cited_by_count":4,"area":null},{"id":"2103.11284","title":"Learning Optimal Fronthauling and Decentralized Edge Computation in Fog Radio Access Networks","created":"2021-03-21","cited_by_count":3,"area":null},{"id":"2503.15482","title":"Natural Quantization of Neural Networks","created":"2025-03-19","cited_by_count":3,"area":null},{"id":"2211.03741","title":"AskewSGD : An Annealed interval-constrained Optimisation method to train Quantized Neural Networks","created":"2022-11-07","cited_by_count":2,"area":null},{"id":"2105.11681","title":"Deep Neural Networks and End-to-End Learning for Audio Compression","created":"2021-05-25","cited_by_count":1,"area":null},{"id":"2202.08465","title":"End-to-End Training for Back-Translation with Categorical Reparameterization Trick","created":"2022-02-17","cited_by_count":0,"area":null},{"id":"2207.00289","title":"Learning to segment from object sizes","created":"2022-07-01","cited_by_count":0,"area":null}],"next_cursor":null}