low-precision training

ELMO : Efficiency via Low-precision and Peak Memory Optimization in Large Output Spaces ICML 2025 QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models EMNLP 2025 Training LLMs with MXFP4 AISTATS 2025 u-μP: The Unit-Scaled Maximal Update Parametrization ICLR 2025 BOLD: Boolean Logic Deep Learning NeurIPS 2024 Learning Discretized Neural Networks under Ricci Flow JMLR 2024 Low Precision Local Training is Enough for Federated Learning NeurIPS 2024 Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction AAAI 2023 Dynamic Stashing Quantization for Efficient Transformer Training EMNLP 2023 Revisiting the Parameter Efficiency of Adapters from the Perspective of Precision Redundancy ICCV 2023 Stable and low-precision training for large-scale vision-language models NeurIPS 2023 Training Transformers with 4-bit Integers NeurIPS 2023 Unit Scaling: Out-of-the-Box Low-Precision Training ICML 2023 How Low Can We Go: Trading Memory for Error in Low-Precision Training ICLR 2022 Low-Precision Stochastic Gradient Langevin Dynamics ICML 2022 Quantized Training of Gradient Boosting Decision Trees NeurIPS 2022 Toward Efficient Low-Precision Training: Data Format Optimization and Hysteresis Quantization ICLR 2022 A Block Minifloat Representation for Training Deep Neural Networks ICLR 2021 CPT: Efficient Deep Neural Network Training via Cyclic Precision ICLR 2021 Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision ICML 2021 FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training NeurIPS 2020 Shifted and Squeezed 8-bit Floating Point format for Low-Precision Training of Deep Neural Networks ICLR 2020 Training Quantized Neural Networks With a Full-Precision Auxiliary Module CVPR 2020 Ultra-Low Precision 4-bit Training of Deep Neural Networks NeurIPS 2020 Backprop with Approximate Activations for Memory-efficient Network Training NeurIPS 2019