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Found 8 papers, 0 papers with code
Koopman Invertible Autoencoder: Leveraging Forward and Backward Dynamics for Temporal Modeling
Accurate long-term predictions are the foundations for many machine learning applications and decision-making processes.
Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology
Furthermore, we show that KGSSL is relatively more robust to distortion than baseline methods, and outperforms the baseline model by 35\% when plugging in KGSSL inferred characteristics.
Phase Retrieval using Single-Instance Deep Generative Prior
Several deep learning methods for phase retrieval exist, but most of them fail on realistic data without precise support information.
Model-agnostic Methods for Text Classification with Inherent Noise
To our knowledge, this is the first time such a comprehensive study in text classification encircling popular models and model-agnostic loss methods has been conducted.
Regularized Graph Convolutional Networks for Short Text Classification
The lack of structure in short text sequences limits the success of popular NLP methods based on deep learning.
Deep Learning Initialized Phase Retrieval
Phase retrieval (PR) consists of estimating 2D or 3D objects from their Fourier magnitudes and takes a central place in scientific imaging.
Unlocking Inverse Problems Using Deep Learning: Breaking Symmetries in Phase Retrieval
In many physical systems, inputs related by intrinsic system symmetries generate the same output.
Inverse Problems, Deep Learning, and Symmetry Breaking
In many physical systems, inputs related by intrinsic system symmetries are mapped to the same output.
