71 citations · 104 across the 11 of their papers we have counts for
11 papers
Distilled Rapid Embedding Transfer (DRET): Parameter-Efficient Biomedical Domain Adaptation via Priority-Based Embedding Transfer
Girish Sundaram, Daniel Berleant
Large domain-specific language models such as BioBERT and ClinicalBERT achieve strong performance on biomedical NLP tasks, but their computational demands make them impractical for…
Quantitative Technology Forecasting: a Review of Trend Extrapolation Methods
Peng-Hung Tsai, Daniel Berleant, Richard S. Segall +4
Quantitative technology forecasting uses quantitative methods to understand and project technological changes. It is a broad field encompassing many different techniques and has be…
Visual Question Answering (VQA) on Images with Superimposed Text
Venkat Kodali, Daniel Berleant
Superimposed text annotations have been under-investigated, yet are ubiquitous, useful and important, especially in medical images. Medical images also highlight the challenges pos…
Automating Systematic Literature Reviews with Natural Language Processing and Text Mining: a Systematic Literature Review
Girish Sundaram, Daniel Berleant
Objectives: An SLR is presented focusing on text mining based automation of SLR creation. The present review identifies the objectives of the automation studies and the aspects of…
Discrete-Event Simulation in Healthcare Settings: a Review
John J. Forbus, Daniel Berleant
We review and define the current state of the art as relating to discrete event simulation in healthcare-related systems. A review of published literature over the past five years…
Discovering Limitations of Image Quality Assessments with Noised Deep Learning Image Sets
Wei Dai, Daniel Berleant
Image quality is important, and can affect overall performance in image processing and computer vision as well as for numerous other reasons. Image quality assessment (IQA) is cons…
Benchmarking Robustness of Deep Learning Classifiers Using Two-Factor Perturbation
Wei Dai, Daniel Berleant
Accuracies of deep learning (DL) classifiers are often unstable in that they may change significantly when retested on adversarial images, imperfect images, or perturbed images. Th…
Recent, rapid advancement in visual question answering architecture: a review
Venkat Kodali, Daniel Berleant
Understanding visual question answering is going to be crucial for numerous human activities. However, it presents major challenges at the heart of the artificial intelligence ende…
Moore's law, Wright's law and the Countdown to Exponential Space
Daniel Berleant, Venkat Kodali, Richard Segall +2
Technologies have often been observed to improve exponentially over time. In practice this often means identifying a constant known as the doubling time, describing the time period…
Benchmarking Robustness of Deep Learning Classifiers Using Two-Factor Perturbation
Wei Dai, Daniel Berleant
This paper adds to the fundamental body of work on benchmarking the robustness of deep learning (DL) classifiers. We innovate a new benchmarking methodology to evaluate robustness…
Direct pivotal predictive inference
George Kahrimanis, Daniel Berleant
Without assuming any pdf for some measured parameter, we derive a predictive pdf for the outcome of a second measurement, given the outcome of the first measurement and two common…