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Found 16 papers, 1 papers with code
Principle Driven Parameterized Fiber Model based on GPT-PINN Neural Network
Therefore, the model can greatly alleviate the heavy burden of re-training since only the linear combination coefficients need to be found when changing the transmission condition.
Fiber Transmission Model with Parameterized Inputs based on GPT-PINN Neural Network
By taking into the account of the previously proposed principle driven fiber model, the reduced basis expansion method and transforming the parameterized inputs into parameterized coefficients of the Nonlinear Schrodinger Equations, universal solutions with respect to inputs corresponding to different bit rates can all be obtained without the need of re-training the whole model.
Fiber neural networks for the intelligent optical fiber communications
Though the potential ability of optical fiber was demonstrated via the establishing of fiber neural networks, it will be of great significance of combining both fiber transmission and computing functions so as to cater the needs of future beyond 5G intelligent communication signal processing.
Data-free Multi-label Image Recognition via LLM-powered Prompt Tuning
This paper proposes a novel framework for multi-label image recognition without any training data, called data-free framework, which uses knowledge of pre-trained Large Language Model (LLM) to learn prompts to adapt pretrained Vision-Language Model (VLM) like CLIP to multilabel classification.
REPOFUSE: Repository-Level Code Completion with Fused Dual Context
The success of language models in code assistance has spurred the proposal of repository-level code completion as a means to enhance prediction accuracy, utilizing the context from the entire codebase.
Code-Based English Models Surprising Performance on Chinese QA Pair Extraction Task
In previous studies, code-based models have consistently outperformed text-based models in reasoning-intensive scenarios.
CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model
It is specifically designed for code-related tasks with both English and Chinese prompts and supports over 40 programming languages.
Capturing dynamical correlations using implicit neural representations
The observation and description of collective excitations in solids is a fundamental issue when seeking to understand the physics of a many-body system.
Sophisticated deep learning with on-chip optical diffractive tensor processing
With OCU as the fundamental unit, we build an optical convolutional neural network (oCNN) to implement two popular deep learning tasks: classification and regression.
Passive Non-line-of-sight Imaging for Moving Targets with an Event Camera
Non-line-of-sight (NLOS) imaging is an emerging technique for detecting objects behind obstacles or around corners.
Key frames assisted hybrid encoding for photorealistic compressive video sensing
Snapshot compressive imaging (SCI) encodes high-speed scene video into a snapshot measurement and then computationally makes reconstructions, allowing for efficient high-dimensional data acquisition.
Principle-driven Fiber Transmission Model based on PINN Neural Network
Instead of adopting input signals and output signals which are calculated by SSFM algorithm in advance before training, this principle-driven PINN based fiber model adopts frames of time and distance as its inputs and the corresponding real and imaginary parts of NLSE solutions as its outputs.
Smoothness Sensor: Adaptive Smoothness-Transition Graph Convolutions for Attributed Graph Clustering
Clustering the nodes of an attributed graph, in which each node is associated with a set of feature attributes, has attracted significant attention.
Electro-optical Neural Networks based on Time-stretch Method
In this paper, a novel architecture of electro-optical neural networks based on the time-stretch method is proposed and numerically simulated.
High-fidelity adiabatic quantum computation using the intrinsic Hamiltonian of a spin system: Application to the experimental factorization of 291311
In previous implementations of adiabatic quantum algorithms using spin systems, the average Hamiltonian method with Trotter's formula was conventionally adopted to generate an effective instantaneous Hamiltonian that simulates an adiabatic passage.
Quantum Physics
High-speed real-time single-pixel microscopy based on Fourier sampling
However, according to CS theory, image reconstruction is an iterative process that consumes enormous amounts of computational time and cannot be performed in real time.
