Search Results for author:
Found 4 papers, 2 papers with code
Reward Dropout Improves Control: Bi-objective Perspective on Reinforced LM
We study the theoretical aspects of Reinforced Language Models (RLMs) from a bi-objective optimization perspective.
OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
Large language models (LLMs) with hundreds of billions of parameters require powerful server-grade GPUs for inference, limiting their practical deployment.
INSTA-BNN: Binary Neural Network with INSTAnce-aware Threshold
Binary Neural Networks (BNNs) have emerged as a promising solution for reducing the memory footprint and compute costs of deep neural networks, but they suffer from quality degradation due to the lack of freedom as activations and weights are constrained to the binary values.
Improving Accuracy of Binary Neural Networks using Unbalanced Activation Distribution
We also show that adjusting the threshold values of binary activation functions results in the unbalanced distribution of the binary activation, which increases the accuracy of BNN models.
