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Found 10 papers, 2 papers with code
Quantitative Technology Forecasting: a Review of Trend Extrapolation Methods
A widely used approach in this field is trend extrapolation.
ASI: Accuracy-Stability Index for Evaluating Deep Learning Models
In the context of deep learning research, where model introductions continually occur, the need for effective and efficient evaluation remains paramount.
Visual Question Answering (VQA) on Images with Superimposed Text
Superimposed text annotations have been under-investigated, yet are ubiquitous, useful and important, especially in medical images.
Automating Systematic Literature Reviews with Natural Language Processing and Text Mining: a Systematic Literature Review
Results: This review identifies the objectives of the automation studies, steps within the study selection, study quality assessment, data extraction and data synthesis portions that were automated, the various ML techniques used, challenges, limitations and scope of further research.
Discovering Limitations of Image Quality Assessments with Noised Deep Learning Image Sets
After quantitatively analyzing experimental results, we report the limitations of the two IQAs with these noised CIFAR-10 and MNIST image sets.
Benchmarking Robustness of Deep Learning Classifiers Using Two-Factor Perturbation
We created comprehensive 69 benchmarking image sets, including a clean set, sets with single factor perturbations, and sets with two-factor perturbation conditions.
Recent, rapid advancement in visual question answering architecture: a review
Understanding visual question answering is going to be crucial for numerous human activities.
Moore's law, Wright's law and the Countdown to Exponential Space
A frequently noted competitor to Moore's law is known as Wright's law, which has aeronautical roots.
Benchmarking Robustness of Deep Learning Classifiers Using Two-Factor Perturbation
Also, we introduce a new four-quadrant statistical visualization tool, including minimum accuracy, maximum accuracy, mean accuracy, and coefficient of variation, for benchmarking robustness of DL classifiers.
Benchmarking Contemporary Deep Learning Hardware and Frameworks:A Survey of Qualitative Metrics
This paper surveys benchmarking principles, machine learning devices including GPUs, FPGAs, and ASICs, and deep learning software frameworks.
