Multi-class Classification
234 papers with code • 4 benchmarks • 11 datasets
Benchmarks
These leaderboards are used to track progress in Multi-class Classification
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Libraries
Use these libraries to find Multi-class Classification models and implementationsDatasets
SMM4H
CVE
SF-MASK
DeepParliament
TII-SSRC-23
Latest papers with no code
FingerNet: EEG Decoding of A Fine Motor Imagery with Finger-tapping Task Based on A Deep Neural Network
We believe that effective execution of motor imagery can be achieved not only for fine MI, but also for local muscle MI
Neural Network Learning and Quantum Gravity
The landscape of low-energy effective field theories stemming from string theory is too vast for a systematic exploration.
A Tutorial on the Pretrain-Finetune Paradigm for Natural Language Processing
Our tutorial offers a comprehensive introduction to the pretrain-finetune paradigm.
Multi-class Temporal Logic Neural Networks
Time-series data can represent the behaviors of autonomous systems, such as drones and self-driving cars.
Understanding Self-Distillation and Partial Label Learning in Multi-Class Classification with Label Noise
By deriving a closed-form solution for the student model's outputs, we discover that SD essentially functions as label averaging among instances with high feature correlations.
A Convergence Analysis of Approximate Message Passing with Non-Separable Functions and Applications to Multi-Class Classification
Motivated by the recent application of approximate message passing (AMP) to the analysis of convex optimizations in multi-class classifications [Loureiro, et.
BAdaCost: Multi-class Boosting with Costs
We present BAdaCost, a multi-class cost-sensitive classification algorithm.
PowerGraph: A power grid benchmark dataset for graph neural networks
To this aim, we develop a graph dataset for cascading failure events, which are the major cause of blackouts in electric power grids.
Leveraging Human-Machine Interactions for Computer Vision Dataset Quality Enhancement
In this paper, we introduce a lightweight, user-friendly, and scalable framework that synergizes human and machine intelligence for efficient dataset validation and quality enhancement.
Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning
In the ambitious realm of space AI, the integration of federated learning (FL) with low Earth orbit (LEO) satellite constellations holds immense promise.
