22 Papers
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Power and Model Efficiency

Efficient LLM Inference using Dynamic Input Pruning and Cache-Aware Masking
Davide Belli, Marco Federici Mart van Baalen, et al. | MLSys, 2025

Power and Model Efficiency

Pruning vs Quantization: Which is Better?
Andrey Kuzmin, Markus Nagel, et al. | NeurIPS, 2023

Power and Model Efficiency

Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing
Yelysei Bondarenko, Markus Nagel, et al. | NeurIPS, 2023

Power and Model Efficiency

Overcoming Oscillations in Quantization-Aware Training
Markus Nagel, Marios Fournarakis, et al. | ICML, 2022

Power and Model Efficiency

FP8 Quantization: The Power of the Exponent
Andrey Kuzmin, Mart Van Baalen, et al. | NeurIPS, 2022

Power and Model Efficiency

Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces
Bert Moons,  Parham Noorzad, et al. | ICCV, 2021

Power and Model Efficiency

Understanding and Overcoming the Challenges of Efficient Transformer Quantization
Yelysei Bondarenko, Markus Nagel, et al. | EMNLP, 2021

Power and Model Efficiency

A Data and Compute Efficient Design for Limited-Resources Deep Learning
Mirgahney Mohamed,  Gabriele Cesa, et al. | ICLR, 2020

Power and Model Efficiency

Bayesian Bits: Unifying Quantization and Pruning
Mart van Baalen,  Christos Louizos, et al. | NeurIPS, 2020

Power and Model Efficiency

Batch-Shaping for Learning Conditional Channel Gated Networks
Babak Ehteshami Bejnordi, Tijmen Blankevoort, et al. | ICLR, 2020

Power and Model Efficiency

Up or Down? Adaptive Rounding for Post-Training Quantization
ICML, 2020

Power and Model Efficiency

Gradient l1 Regularization for Quantization Robustness
Milad Alizadeh, Arash Behboodi, et al. | ICLR, 2020

Power and Model Efficiency

Structured Convolutions for Efficient Neural Network Design
Yash Bhalgat,  Yizhe Zhang, et al. | NeurIPS, 2020

Power and Model Efficiency

Conditional Channel Gated Networks for Task-Aware Continual Learning
Davide Abati,  Jakub Tomczak, et al. | CVPR, 2020

Power and Model Efficiency

LSQ+: Improving low-bit quantization through learnable offsets and better initialization
Yash Bhalgat, Jinwon Lee, et al. | CVPR, 2020

Power and Model Efficiency

Differentiable Joint Pruning and Quantization for Hardware Efficiency
Ying Wang,  Yadong Lu, et al. | ECCV, 2020

Power and Model Efficiency

Blended Coarse Gradient Descent for Full Quantization of Deep Neural Networks
Penghang Yin, Shuai Zhang, et al. | RMS, 2019

Power and Model Efficiency

DAC: Data-free Automatic Acceleration of Convolutional Networks
Xin Li, Shuai Zhang, et al. | WACV, 2019

Power and Model Efficiency

Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets
Penghang Yin, Jiancheng Lyu, et al. | ICLR, 2019

Power and Model Efficiency

Data-Free Quantization through Weight Equalization and Bias Correction
Markus Nagel,  Mart van Baalen, et al. | ICCV, 2019

Power and Model Efficiency

Relaxed Quantization for Discretized Neural Networks
Christos Louizos, Matthias Reisser, et al. | NeurIPS, 2018

Power and Model Efficiency

A Quantization-Friendly Separable Convolution for MobileNets 
Tao Sheng,  Chen Feng, et al. | EMC^, 2018