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Found 5 papers, 1 papers with code
SkipViT: Speeding Up Vision Transformers with a Token-Level Skip Connection
Our method does not add any parameters to the ViT model and aims to find the best trade-off between training throughput and achieving a 0% loss in the Top-1 accuracy of the final model.
SwiftLearn: A Data-Efficient Training Method of Deep Learning Models using Importance Sampling
In this paper, we present SwiftLearn, a data-efficient approach to accelerate training of deep learning models using a subset of data samples selected during the warm-up stages of training.
GQKVA: Efficient Pre-training of Transformers by Grouping Queries, Keys, and Values
We tested our method on ViT, which achieved an approximate 0. 3% increase in accuracy while reducing the model size by about 4% in the task of image classification.
Gray-box Adversarial Attack of Deep Reinforcement Learning-based Trading Agents
In this research, we demonstrate that a "gray-box" approach for attacking a Deep RL-based trading agent is possible by trading in the same stock market, with no extra access to the trading agent.
Deep State Inference: Toward Behavioral Model Inference of Black-box Software Systems
Many software engineering tasks, such as testing, and anomaly detection can benefit from the ability to infer a behavioral model of the software. Most existing inference approaches assume access to code to collect execution sequences.
