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Found 15 papers, 2 papers with code
Domain-invariant Representation Learning via Segment Anything Model for Blood Cell Classification
To address this issue, we propose a novel framework of domain-invariant representation learning (DoRL) via segment anything model (SAM) for blood cell classification.
Towards Cross-Domain Single Blood Cell Image Classification via Large-Scale LoRA-based Segment Anything Model
Accurate classification of blood cells plays a vital role in hematological analysis as it aids physicians in diagnosing various medical conditions.
Opportunities and challenges in the application of large artificial intelligence models in radiology
Influenced by ChatGPT, artificial intelligence (AI) large models have witnessed a global upsurge in large model research and development.
Developing A Fair Individualized Polysocial Risk Score (iPsRS) for Identifying Increased Social Risk of Hospitalizations in Patients with Type 2 Diabetes (T2D)
Background: Racial and ethnic minority groups and individuals facing social disadvantages, which often stem from their social determinants of health (SDoH), bear a disproportionate burden of type 2 diabetes (T2D) and its complications.
Persistent Ballistic Entanglement Spreading with Optimal Control in Quantum Spin Chains
Entanglement propagation provides a key routine to understand quantum many-body dynamics in and out of equilibrium.
Quantum compiling with a variational instruction set for accurate and fast quantum computing
The quantum instruction set (QIS) is defined as the quantum gates that are physically realizable by controlling the qubits in quantum hardware.
Embedding Node Structural Role Identity Using Stress Majorization
In this paper, we present a novel and flexible framework using stress majorization, to transform the high-dimensional role identities in networks directly (without approximation or indirect modeling) to a low-dimensional embedding space.
Preparation of Many-body Ground States by Time Evolution with Variational Microscopic Magnetic Fields and Incomplete Interactions
State preparation is of fundamental importance in quantum physics, which can be realized by constructing the quantum circuit as a unitary that transforms the initial state to the target, or implementing a quantum control protocol to evolve to the target state with a designed Hamiltonian.
Embedding Node Structural Role Identity into Hyperbolic Space
However, the work on network embedding in hyperbolic space has been focused on microscopic node embedding.
D$^2$-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios
Driving datasets accelerate the development of intelligent driving and related computer vision technologies, while substantial and detailed annotations serve as fuels and powers to boost the efficacy of such datasets to improve learning-based models.
Discriminative Label Consistent Domain Adaptation
Domain adaptation (DA) is transfer learning which aims to learn an effective predictor on target data from source data despite data distribution mismatch between source and target.
Brenier approach for optimal transportation between a quasi-discrete measure and a discrete measure
Correctly estimating the discrepancy between two data distributions has always been an important task in Machine Learning.
Discriminative and Geometry Aware Unsupervised Domain Adaptation
Domain adaptation (DA) aims to generalize a learning model across training and testing data despite the mismatch of their data distributions.
Optimal Transport for Deep Joint Transfer Learning
By adding an Optimal Transport loss (OT loss) between source and target classifier predictions as a constraint on the source classifier, the proposed Joint Transfer Learning Network (JTLN) can effectively learn useful knowledge for target classification from source data.
