Search Results for author:
Found 15 papers, 3 papers with code
Sparse Shrunk Additive Models
Meanwhile, in these feature selection models, the interactions between features are often ignored or just discussed under prior structure information.
Boosting Meta-Training with Base Class Information for Few-Shot Learning
However, as a non-end-to-end training method, indicating the meta-training stage can only begin after the completion of pre-training, Meta-Baseline suffers from higher training cost and suboptimal performance due to the inherent conflicts of the two training stages.
Your Vision-Language Model Itself Is a Strong Filter: Towards High-Quality Instruction Tuning with Data Selection
In the first stage, we devise a scoring network to evaluate the difficulty of training instructions, which is co-trained with the VLM.
GPT-4 Vision on Medical Image Classification -- A Case Study on COVID-19 Dataset
This technical report delves into the application of GPT-4 Vision (GPT-4V) in the nuanced realm of COVID-19 image classification, leveraging the transformative potential of in-context learning to enhance diagnostic processes.
Rethinking Class Activation Maps for Segmentation: Revealing Semantic Information in Shallow Layers by Reducing Noise
A major limitation to the performance of the class activation maps is the small spatial resolution of the feature maps in the last layer of the convolutional neural network.
CG-fusion CAM: Online segmentation of laser-induced damage on large-aperture optics
Online segmentation of laser-induced damage on large-aperture optics in high-power laser facilities is challenged by complicated damage morphology, uneven illumination and stray light interference.
Effective Medical Code Prediction via Label Internal Alignment
The clinical notes are usually typed into the system by physicians.
SuperScaler: Supporting Flexible DNN Parallelization via a Unified Abstraction
SuperScaler is a system that facilitates the design and generation of highly flexible parallelization plans.
Generating Pseudo-labels Adaptively for Few-shot Model-Agnostic Meta-Learning
Model-Agnostic Meta-Learning (MAML) is a famous few-shot learning method that has inspired many follow-up efforts, such as ANIL and BOIL.
Training Neural Networks for Solving 1-D Optimal Piecewise Linear Approximation
Our main contributions are that we propose the theorems to characterize the optimal solution of the PWLA problem and present the LNN method for solving it.
How to Explain Neural Networks: an Approximation Perspective
The lack of interpretability has hindered the large-scale adoption of AI technologies.
Breaking the Computation and Communication Abstraction Barrier in Distributed Machine Learning Workloads
Therefore, we present CoCoNeT, with a DSL to express a program with both computation and communication.
Optimizing Large-Scale Hyperparameters via Automated Learning Algorithm
Modern machine learning algorithms usually involve tuning multiple (from one to thousands) hyperparameters which play a pivotal role in terms of model generalizability.
Origin of the Electronic Structure in Single-Layer FeSe/SrTiO3 Films
These observations not only show the first direct evidence that the electronic structure of single-layer FeSe/SrTiO3 films originates from bulk FeSe through a combined effect of an electronic phase transition and an interfacial charge transfer, but also provide a quantitative basis for theoretical models in describing the electronic structure and understanding the superconducting mechanism in single-layer FeSe/SrTiO3 films.
Band Gap
Superconductivity
Strongly Correlated Electrons
Approximating Trajectory Constraints with Machine Learning -- Microgrid Islanding with Frequency Constraints
In this paper, we introduce a deep learning aided constraint encoding method to tackle the frequency-constraint microgrid scheduling problem.
