Papers tabular-classification
“tabular-classification” 태그가 달린 논문 48편 · 필터 해제
Learning Interpretable Differentiable Logic Networks for Tabular Regression
Neural networks (NNs) achieve outstanding performance in many domains; however, their decision processes are often opaque and their inference can be computationally expensive in resource-constrained environments. We rece…
Computational Efficiencyregressiontabular-classificationtabular-regressionTest-Time Training Provably Improves Transformers as In-context Learners
Test-time training (TTT) methods explicitly update the weights of a model to adapt to the specific test instance, and they have found success in a variety of settings, including most recently language modeling and reason…
In-Context LearningLanguage ModelingLanguage Modellingtabular-classificationTabNSA: Native Sparse Attention for Efficient Tabular Data Learning
Tabular data poses unique challenges for deep learning due to its heterogeneous features and lack of inherent spatial structure. This paper introduces TabNSA, a novel deep learning architecture leveraging Native Sparse A…
Deep Learningfeature selectiontabular-classificationPrior-Fitted Networks Scale to Larger Datasets When Treated as Weak Learners
Prior-Fitted Networks (PFNs) have recently been proposed to efficiently perform tabular classification tasks. Although they achieve good performance on small datasets, they encounter limitations with larger datasets. The…
AutoMLtabular-classificationTabMixer: advancing tabular data analysis with an enhanced MLP-mixer approach
Tabular data, prevalent in relational databases and spreadsheets, is fundamental across fields like healthcare, engineering, and finance. Despite significant advances in tabular data learning, critical challenges remain:…
Computational EfficiencyDeep LearningIncremental LearningMissing Values+2JoLT: Joint Probabilistic Predictions on Tabular Data Using LLMs
We introduce a simple method for probabilistic predictions on tabular data based on Large Language Models (LLMs) called JoLT (Joint LLM Process for Tabular data). JoLT uses the in-context learning capabilities of LLMs to…
ImputationIn-Context Learningtabular-classificationWhat exactly has TabPFN learned to do?
TabPFN [Hollmann et al., 2023], a Transformer model pretrained to perform in-context learning on fresh tabular classification problems, was presented at the last ICLR conference. To better understand its behavior, we tre…
In-Context Learningtabular-classificationTabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problems
TabPFN has emerged as a promising in-context learning model for tabular data, capable of directly predicting the labels of test samples given labeled training examples. It has demonstrated competitive performance, partic…
Computational EfficiencyIn-Context Learningtabular-classificationEfficiency Bottlenecks of Convolutional Kolmogorov-Arnold Networks: A Comprehensive Scrutiny with ImageNet, AlexNet, LeNet and Tabular Classification
Algorithmic level developments like Convolutional Neural Networks, transformers, attention mechanism, Retrieval Augmented Generation and so on have changed Artificial Intelligence. Recent such development was observed by…
Kolmogorov-Arnold NetworksRetrieval-augmented Generationtabular-classificationHow good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives
A rapidly developing application of LLMs in XAI is to convert quantitative explanations such as SHAP into user-friendly narratives to explain the decisions made by smaller prediction models. Evaluating the narratives wit…
tabular-classificationBayesian Concept Bottleneck Models with LLM Priors
Concept Bottleneck Models (CBMs) have been proposed as a compromise between white-box and black-box models, aiming to achieve interpretability without sacrificing accuracy. The standard training procedure for CBMs is to …
Image Classificationtabular-classificationtext-classificationUncertainty QuantificationNeural Reasoning Networks: Efficient Interpretable Neural Networks With Automatic Textual Explanations
Recent advances in machine learning have led to a surge in adoption of neural networks for various tasks, but lack of interpretability remains an issue for many others in which an understanding of the features influencin…
FairnessFeature ImportanceGPUtabular-classificationSTAND: Data-Efficient and Self-Aware Precondition Induction for Interactive Task Learning
STAND is a data-efficient and computationally efficient machine learning approach that produces better classification accuracy than popular approaches like XGBoost on small-data tabular classification problems like learn…
Active LearningHoldout Settabular-classificationPMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applications
In practice, we are often faced with small-sized tabular data. However, current tabular benchmarks are not geared towards data-scarce applications, making it very difficult to derive meaningful conclusions from empirical…
AutoMLBinary ClassificationDeep Learningtabular-classificationImproving GBDT Performance on Imbalanced Datasets: An Empirical Study of Class-Balanced Loss Functions
Class imbalance remains a significant challenge in machine learning, particularly for tabular data classification tasks. While Gradient Boosting Decision Trees (GBDT) models have proven highly effective for such tasks, t…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONtabular-classificationTransformers with Stochastic Competition for Tabular Data Modelling
Despite the prevalence and significance of tabular data across numerous industries and fields, it has been relatively underexplored in the realm of deep learning. Even today, neural networks are often overshadowed by tec…
Deep Learningtabular-classificationtabular-regressionQuantifying Prediction Consistency Under Fine-Tuning Multiplicity in Tabular LLMs
Fine-tuning LLMs on tabular classification tasks can lead to the phenomenon of fine-tuning multiplicity where equally well-performing models make conflicting predictions on the same input. Fine-tuning multiplicity can ar…
Decision Makingtabular-classificationPairwise Difference Learning for Classification
Pairwise difference learning (PDL) has recently been introduced as a new meta-learning technique for regression. Instead of learning a mapping from instances to outcomes in the standard way, the key idea is to learn a fu…
Binary ClassificationClassificationMeta-Learningtabular-classificationALPBench: A Benchmark for Active Learning Pipelines on Tabular Data
In settings where only a budgeted amount of labeled data can be afforded, active learning seeks to devise query strategies for selecting the most informative data points to be labeled, aiming to enhance learning algorith…
Active Learningtabular-classificationMSBoost: Using Model Selection with Multiple Base Estimators for Gradient Boosting
Gradient boosting is a widely used machine learning algorithm for tabular regression, classification and ranking. Although, most of the open source implementations of gradient boosting such as XGBoost, LightGBM and other…
ClassificationModel Selectionregressiontabular-classification+1