390 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of OxfordGB9 papers
- Rowan UniversityUS8 papers
- Harvard UniversityUS4 papers
- University of PennsylvaniaUS4 papers
- Chalmers University of TechnologySE3 papers
- Georgetown UniversityUS3 papers
- University of Alabama at BirminghamUS3 papers
- University of GothenburgSE3 papers
- Vanderbilt UniversityUS3 papers
- Baylor College of MedicineUS2 papers
- Brigham and Women's HospitalUS2 papers
- Carnegie Mellon UniversityUS2 papers
7 papers · 1 filter
Revisiting the Fragility of Influence Functions
Jacob R. Epifano, Ravi P. Ramachandran, Aaron J. Masino +1
In the last few years, many works have tried to explain the predictions of deep learning models. Few methods, however, have been proposed to verify the accuracy or faithfulness of…
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust Models
Ian E. Nielsen, Ravi P. Ramachandran, Nidhal Bouaynaya +2
The expansion of explainable artificial intelligence as a field of research has generated numerous methods of visualizing and understanding the black box of a machine learning mode…
Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review
Asim Waqas, Aakash Tripathi, Ravi P. Ramachandran +2
Cancer has relational information residing at varying scales, modalities, and resolutions of the acquired data, such as radiology, pathology, genomics, proteomics, and clinical rec…
Transformers in Time-series Analysis: A Tutorial
Sabeen Ahmed, Ian E. Nielsen, Aakash Tripathi +3
Transformer architecture has widespread applications, particularly in Natural Language Processing and computer vision. Recently Transformers have been employed in various aspects o…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks
Ian E. Nielsen, Dimah Dera, Ghulam Rasool +2
With the rise of deep neural networks, the challenge of explaining the predictions of these networks has become increasingly recognized. While many methods for explaining the decis…
Exploring Robust Architectures for Deep Artificial Neural Networks
Asim Waqas, Ghulam Rasool, Hamza Farooq +1
The architectures of deep artificial neural networks (DANNs) are routinely studied to improve their predictive performance. However, the relationship between the architecture of a…