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Found 13 papers, 5 papers with code
Symmetrical Joint Learning Support-query Prototypes for Few-shot Segmentation
We propose Sym-Net, a novel framework for Few-Shot Segmentation (FSS) that addresses the critical issue of intra-class variation by jointly learning both query and support prototypes in a symmetrical manner.
Investigating the Impact of Quantization on Adversarial Robustness
Quantization is a promising technique for reducing the bit-width of deep models to improve their runtime performance and storage efficiency, and thus becomes a fundamental step for deployment.
Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives
Foundation models have indeed made a profound impact on various fields, emerging as pivotal components that significantly shape the capabilities of intelligent systems.
Preconditioned Federated Learning
Federated Learning (FL) is a distributed machine learning approach that enables model training in communication efficient and privacy-preserving manner.
MORE: Measurement and Correlation Based Variational Quantum Circuit for Multi-classification
MORE adopts the same variational ansatz as binary classifiers while performing multi-classification by fully utilizing the quantum information of a single readout qubit.
Vertical Federated Learning: Taxonomies, Threats, and Prospects
In a number of practical scenarios, VFL is more relevant than HFL as different companies (e. g., bank and retailer) hold different features (e. g., credit history and shopping history) for the same set of customers.
Scalable Quantum Neural Networks for Classification
The quantum feature extractors in the SQNN system are independent of each other, so one can flexibly use quantum devices of varying sizes, with larger quantum devices extracting more local features.
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.
Dite-HRNet: Dynamic Lightweight High-Resolution Network for Human Pose Estimation
A high-resolution network exhibits remarkable capability in extracting multi-scale features for human pose estimation, but fails to capture long-range interactions between joints and has high computational complexity.
Ranked #30 on
Pose Estimation
on COCO test-dev
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.
Spatio-Temporal Hierarchical Adaptive Dispatching for Ridesharing Systems
Thus, in this paper, we exploit adaptive dispatching intervals to boost the platform's profit under a guarantee of the maximum passenger waiting time.
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.
Compressive Sensing of Sparse Tensors
We then compare the performance of the proposed method with Kronecker compressive sensing (KCS) and multi way compressive sensing (MWCS).
