Search Results for author:
Found 8 papers, 2 papers with code
7T MRI Synthesization from 3T Acquisitions
We demonstrate that the V-Net based model has superior performance in enhancing both single-site and multi-site MRI datasets compared to the existing benchmark model.
Zero-shot sampling of adversarial entities in biomedical question answering
Our investigations illustrate the brittleness of domain knowledge in LLMs and reveal a shortcoming of standard evaluations for high-capacity models.
Machine Learning for Uncovering Biological Insights in Spatial Transcriptomics Data
Development and homeostasis in multicellular systems both require exquisite control over spatial molecular pattern formation and maintenance.
Multi-Modal Prototype Learning for Interpretable Multivariable Time Series Classification
We validate our framework on a simulated dataset with embedded patterns, as well as a real human activity recognition problem.
Interpretable machine learning: definitions, methods, and applications
Official code for using / reproducing ACD (ICLR 2019) from the paper "Hierarchical interpretations for neural network predictions" https://arxiv. org/abs/1806. 05337
Robust registration of medical images in the presence of spatially-varying noise
Bias field noise is one example of such a distortion that is often present in the magnetic resonance (MR) images or other modalities such as retina images.
Interpreting Convolutional Neural Networks Through Compression
In our compression, the filter importance index is defined as the classification accuracy reduction (CAR) of the network after pruning that filter.
Structural Compression of Convolutional Neural Networks
Deep convolutional neural networks (CNNs) have been successful in many tasks in machine vision, however, millions of weights in the form of thousands of convolutional filters in CNNs makes them difficult for human intepretation or understanding in science.
