Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search
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In this thesis, we propose new algorithms to solve inverse problems in the context of biomedical images. Due to ill-posedness, solving these problems require some prior knowledge of the statistics of the underlying images. The traditional algorithms, in th ...
Optical tomography has been widely investigated for biomedical imaging applications. In recent years, it has been combined with digital holography and has been employed to produce high quality images of phase objects such as cells. In this Thesis, we look ...
Thanks to the digital preservation of cultural heritage materials, multimedia tools (e.g., based on automatic visual processing) considerably ease the work of scholars in the humanities and help them to perform quantitative analysis of their data. In this ...
We consider the transfer learning scenario, where the learner does not have access to the source domain directly, but rather operates on the basis of hypotheses induced from it - the Hypothesis Transfer Learning (HTL) problem. Particularly, we conduct a th ...
A recent line of work focused on making adversarial training computationally efficient for deep learning models. In particular, Wong et al. (2020) showed that ℓ∞-adversarial training with fast gradient sign method (FGSM) can fail due to a phenomenon called ...
The design and analysis of machine learning algorithms typically considers the problem of learning on a single task, and the nature of learning in such scenario is well explored. On the other hand, very often tasks faced by machine learning systems arrive ...
Rapid advances in data collection and processing capabilities have allowed for the use of increasingly complex models that give rise to nonconvex optimization problems. These formulations, however, can be arbitrarily difficult to solve in general, in the s ...
This project presents a pre-State Estimation method for the detection of Bad Data (BD) in Phasor Measurement Units (PMUs) using correlation analysis and a Neural Network Classifier. Presented in this paper is the algorithm design, the steps for generating ...
Neural Architecture Search (NAS) aims to facilitate the design of deep networks fornew tasks. Existing techniques rely on two stages: searching over the architecture space and validating the best architecture. NAS algorithms are currently compared solely b ...
This tutorial covers biomedical image reconstruction, from the foundational concepts of system modeling and direct reconstruction to modern sparsity and learning-based approaches. Imaging is a critical tool in biological research and medicine, and most ima ...