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Found 5 papers, 1 papers with code
Practice with Graph-based ANN Algorithms on Sparse Data: Chi-square Two-tower model, HNSW, Sign Cauchy Projections
In this paper, we report our exploration of efficient search in sparse data with graph-based ANN algorithms (e. g., HNSW, or SONG which is the GPU version of HNSW), which are popular in industrial practice, e. g., search and ads (advertising).
LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks
LAWS is a combinatorial optimization strategy taking advantage of model parameter initialization and fast convergence of QNG.
QuantumFed: A Federated Learning Framework for Collaborative Quantum Training
With the fast development of quantum computing and deep learning, quantum neural networks have attracted great attention recently.
Object-Based Image Coding: A Learning-Driven Revisit
The Object-Based Image Coding (OBIC) that was extensively studied about two decades ago, promised a vast application perspective for both ultra-low bitrate communication and high-level semantical content understanding, but it had rarely been used due to the inefficient compact representation of object with arbitrary shape.
A new perspective in understanding of Adam-Type algorithms and beyond
Moreover, we provide new variant of Adam-Type algorithm, namely AdamAL which can naturally mitigate the non-convergence issue of Adam and improve its performance.
