Search Results for author:
Found 9 papers, 3 papers with code
Cache-Aware Reinforcement Learning in Large-Scale Recommender Systems
The recommendation with a cache is a solution to this problem, where a user-wise result cache is used to provide recommendations when the recommender system cannot afford a real-time computation.
Multi-Level Aggregation and Recursive Alignment Architecture for Efficient Parallel Inference Segmentation Network
Real-time semantic segmentation is a crucial research for real-world applications.
Privacy-Preserving Distributed Machine Learning Made Faster
So machine learning tasks need to be spread across multiple servers, turning the centralized machine learning into a distributed one.
Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services
Recently, industrial recommendation services have been boosted by the continual upgrade of deep learning methods.
Stock Market Trend Analysis Using Hidden Markov Model and Long Short Term Memory
This paper intends to apply the Hidden Markov Model into stock market and and make predictions.
Quantum walks on a programmable two-dimensional 62-qubit superconducting processor
Quantum walks are the quantum mechanical analogue of classical random walks and an extremely powerful tool in quantum simulations, quantum search algorithms, and even for universal quantum computing.
Quantum Physics
Experimental characterization of quantum many-body localization transition
Here, we experimentally implement a scalable protocol for detecting the many-body localization transition point, using the dynamics of a $N=12$ superconducting qubit array.
Quantum Physics Mesoscale and Nanoscale Physics Strongly Correlated Electrons
Experimental Quantum Generative Adversarial Networks for Image Generation
For the first time, we experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor.
RLCFR: Minimize Counterfactual Regret by Deep Reinforcement Learning
Extensive experimental results on various games have shown that the generalization ability of our method is significantly improved compared with existing state-of-the-art methods.
