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Found 9 papers, 2 papers with code
Learning quantum properties from short-range correlations using multi-task networks
Through numerical experiments, we show that multi-task learning can be applied to sufficiently regular states to predict global properties, like string order parameters, from the observation of short-range correlations, and to distinguish between quantum phases that cannot be distinguished by single-task networks.
How To Prevent the Continuous Damage of Noises To Model Training?
To address this problem, the proposed GSS alleviates the damage by switching the current gradient direction of each sample to a new direction selected from a gradient direction pool, which contains all-class gradient directions with different probabilities.
Quantum Similarity Testing with Convolutional Neural Networks
The task of testing whether two uncharacterized quantum devices behave in the same way is crucial for benchmarking near-term quantum computers and quantum simulators, but has so far remained open for continuous-variable quantum systems.
LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data
In this work, we propose a new approach, LSTMSPLIT, that uses SL architecture with an LSTM network to classify time-series data with multiple clients.
Flexible learning of quantum states with generative query neural networks
Existing networks are typically trained with experimental data gathered from the specific quantum state that needs to be characterized.
One model Packs Thousands of Items with Recurrent Conditional Query Learning
In this paper, we propose a Recurrent Conditional Query Learning (RCQL) method to solve both 2D and 3D packing problems.
Edge-competing Pathological Liver Vessel Segmentation with Limited Labels
The diagnosis of MVI needs discovering the vessels that contain hepatocellular carcinoma cells and counting their number in each vessel, which depends heavily on experiences of the doctor, is largely subjective and time-consuming.
Mutual-Complementing Framework for Nuclei Detection and Segmentation in Pathology Image
The existing two unsupervised methods are prone to failure on degenerated samples.
Solving Packing Problems by Conditional Query Learning
Previous studies have shown that NCO outperforms heuristic algorithms in many combinatorial optimization problems such as the routing problems.
