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08/15/2023
Domain-Aware Fine-Tuning: Enhancing Neural Network Adaptability
Fine-tuning pre-trained neural network models has become a widely adopte...
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05/13/2023
Learning to Learn Unlearned Feature for Brain Tumor Segmentation
We propose a fine-tuning algorithm for brain tumor segmentation that nee...
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04/04/2023
Bridging the Gap between Model Explanations in Partially Annotated Multi-label Classification
Due to the expensive costs of collecting labels in multi-label classific...
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06/08/2022
Large Loss Matters in Weakly Supervised Multi-Label Classification
Weakly supervised multi-label classification (WSML) task, which is to le...
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08/17/2020
Information-Theoretic Privacy in Federated Submodel learning
We consider information-theoretic privacy in federated submodel learning...
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02/16/2020
REST: Performance Improvement of a Black Box Model via RL-based Spatial Transformation
In recent years, deep neural networks (DNN) have become a highly active ...
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02/01/2019
Private Secure Coded Computation
We introduce a variation of coded computation that ensures data security...
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12/08/2018
Sampling-based Bayesian Inference with gradient uncertainty
Deep neural networks(NNs) have achieved impressive performance, often ex...
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01/09/2018
GIFT: Guided and Interpretable Factorization for Tensors - An Application to Large-Scale Multi-platform Cancer Analysis
Given multi-platform genome data with prior knowledge of functional gene...
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10/09/2017
CTD: Fast, Accurate, and Interpretable Method for Static and Dynamic Tensor Decompositions
How can we find patterns and anomalies in a tensor, or multi-dimensional...
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05/09/2017
