Multi-class Classification
229 papers with code • 5 benchmarks • 12 datasets
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Latest papers
HemaGraph: Breaking Barriers in Hematologic Single Cell Classification with Graph Attention
To the best of our knowledge, this is the first effort to use GATs, and Graph Neural Networks (GNNs) in general, to classify cell populations from single-cell flow cytometry data.
Evaluating ML-Based Anomaly Detection Across Datasets of Varied Integrity: A Case Study
In this study, we address the prevalent issue of data integrity in network traffic datasets, which are instrumental in developing machine learning (ML) models for anomaly detection.
Enumerating the k-fold configurations in multi-class classification problems
K-fold cross-validation is a widely used tool for assessing classifier performance.
Safe reinforcement learning in uncertain contexts
In this work, we drop this assumption and show how we can perform safe learning when we cannot directly measure the context variables.
Distance Guided Generative Adversarial Network for Explainable Binary Classifications
Experimental results show that DisGAN consistently outperforms the GAN-based augmentation methods with explainable binary classification.
NearbyPatchCL: Leveraging Nearby Patches for Self-Supervised Patch-Level Multi-Class Classification in Whole-Slide Images
In addressing the demands of this critical task, self-supervised learning (SSL) methods have emerged as a valuable resource, leveraging their efficiency in circumventing the need for a large number of annotations, which can be both costly and time-consuming to deploy supervised methods.
Boosting Prompt-Based Self-Training With Mapping-Free Automatic Verbalizer for Multi-Class Classification
Recently, prompt-based fine-tuning has garnered considerable interest as a core technique for few-shot text classification task.
Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations
We introduce Llama Guard, an LLM-based input-output safeguard model geared towards Human-AI conversation use cases.
Improving Bias Mitigation through Bias Experts in Natural Language Understanding
To mitigate the detrimental effect of the bias on the networks, previous works have proposed debiasing methods that down-weight the biased examples identified by an auxiliary model, which is trained with explicit bias labels.
Exponentially Convergent Algorithms for Supervised Matrix Factorization
Supervised matrix factorization (SMF) is a classical machine learning method that simultaneously seeks feature extraction and classification tasks, which are not necessarily a priori aligned objectives.