Revisiting Data Analysis with Pre-trained Foundation Models | Paper Citation Record

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This paper cites Generalization on the unseen, logic reasoning and degree curriculum.

Revisiting Data Analysis with Pre-trained Foundation Models Generalization on the unseen, logic reasoning and degree curriculum

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Revisiting Data Analysis with Pre-trained Foundation Models Interventional causal repre- sentation learning

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Revisiting Data Analysis with Pre-trained Foundation Models Physics of Language Models: Part 1, Learning Hierarchical Language Structures

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Revisiting Data Analysis with Pre-trained Foundation Models Automated unit test improvement using large language models at meta

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Revisiting Data Analysis with Pre-trained Foundation Models A survey of cross-validation procedures for model selection

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Revisiting Data Analysis with Pre-trained Foundation Models Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Ab- stract Geometric Reasoning

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Revisiting Data Analysis with Pre-trained Foundation Models Transformers as statisticians: Prov- able in-context learning with in-context algorithm selection

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Revisiting Data Analysis with Pre-trained Foundation Models Grounded Copilot: How Program- mers Interact with Code-Generating Models

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Revisiting Data Analysis with Pre-trained Foundation Models Methodologies for data qual- ity assessment and improvement

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Revisiting Data Analysis with Pre-trained Foundation Models Neurosymbolic repair for low-code formula languages

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Revisiting Data Analysis with Pre-trained Foundation Models On the dangers of stochas- tic parrots: Can language models be too big?

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This paper cites Graph2Tac: Online Representation Learning of Formal Math Con- cepts.

Revisiting Data Analysis with Pre-trained Foundation Models Graph2Tac: Online Representation Learning of Formal Math Con- cepts

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This paper cites A parameterized theory of PAC learn- Revisiting Data Analysis with Pre-trained Foundation Models 23 ing.

Revisiting Data Analysis with Pre-trained Foundation Models A parameterized theory of PAC learn- Revisiting Data Analysis with Pre-trained Foundation Models 23 ing

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Revisiting Data Analysis with Pre-trained Foundation Models Statistical and computa- tional methods in data analysis

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This paper cites Practical Reliability Data Analy- sis for Non-Reliability Engineers.

Revisiting Data Analysis with Pre-trained Foundation Models Practical Reliability Data Analy- sis for Non-Reliability Engineers

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Revisiting Data Analysis with Pre-trained Foundation Models Language models are few- shot learners

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Revisiting Data Analysis with Pre-trained Foundation Models Practical statistics for data scientists: 50+ es- sential concepts using R and Python

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Revisiting Data Analysis with Pre-trained Foundation Models Ro- bustness of Nonlinear Representation Learning

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Revisiting Data Analysis with Pre-trained Foundation Models CoqPyt: Proof Navigation in Python in the Era of LLMs

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This paper cites Why data sci- entists prefer glassbox machine learning: Algo- rithms, differential privacy, editing and bias mit- igation.

Revisiting Data Analysis with Pre-trained Foundation Models Why data sci- entists prefer glassbox machine learning: Algo- rithms, differential privacy, editing and bias mit- igation

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Revisiting Data Analysis with Pre-trained Foundation Models SPIREX: Improving LLM-based relation extraction from RNA-focused scientific literature using graph machine learn- ing

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Revisiting Data Analysis with Pre-trained Foundation Models Binding Language Mod- els in Symbolic Languages

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Revisiting Data Analysis with Pre-trained Foundation Models Performance Optimiza- tion in the LLM World 2024

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Revisiting Data Analysis with Pre-trained Foundation Models Relational database: A practi- cal foundation for productivity

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Revisiting Data Analysis with Pre-trained Foundation Models Large Language Models for Compiler Optimization

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Revisiting Data Analysis with Pre-trained Foundation Models Meta Large Language Model Compiler: Foundation Models of Compiler Optimization

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Revisiting Data Analysis with Pre-trained Foundation Models Good semi-supervised learn- ing that requires a bad gan

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This paper cites Compet- ing on analytics: Updated, with a new introduc- tion: The new science of winning.

Revisiting Data Analysis with Pre-trained Foundation Models Compet- ing on analytics: Updated, with a new introduc- tion: The new science of winning

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Revisiting Data Analysis with Pre-trained Foundation Models Language Modeling Is Compression

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Revisiting Data Analysis with Pre-trained Foundation Models TURL: Table Understand- ing through Representation Learning

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Revisiting Data Analysis with Pre-trained Foundation Models LIDA: A Tool for Automatic Gen- eration of Grammar-Agnostic Visualizations and Infographics using Large Language Models

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Revisiting Data Analysis with Pre-trained Foundation Models Overfitting and undercomput- ing in machine learning

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Revisiting Data Analysis with Pre-trained Foundation Models Large language models of code fail at completing code with potential bugs

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Revisiting Data Analysis with Pre-trained Foundation Models DeepJoin: Joinable Table Discovery with Pre-trained Language Models

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Revisiting Data Analysis with Pre-trained Foundation Models Position: Compositional Generative Modeling: A Single Model is Not All You Need

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Revisiting Data Analysis with Pre-trained Foundation Models Enhancing job recommen- dation through llm-based generative adversarial networks

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Revisiting Data Analysis with Pre-trained Foundation Models On-device query intent prediction with lightweight LLMs to support ubiq- uitous conversations

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Revisiting Data Analysis with Pre-trained Foundation Models DreamCoder: growing gener- alizable, interpretable knowledge with wake–sleep Bayesian program learning

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Revisiting Data Analysis with Pre-trained Foundation Models Semantics-aware Dataset Dis- covery from Data Lakes with Contextualized Column- based Representation Learning

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Revisiting Data Analysis with Pre-trained Foundation Models Combining Small Language Mod- els and Large Language Models for Zero-Shot NL2SQL

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Revisiting Data Analysis with Pre-trained Foundation Models How large lan- guage models will disrupt data management

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Revisiting Data Analysis with Pre-trained Foundation Models Position: Relational Deep Learning-Graph Representation Learning on Re- lational Databases

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Revisiting Data Analysis with Pre-trained Foundation Models CatSQL: Towards Real World Natural Language to SQL Applications

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Revisiting Data Analysis with Pre-trained Foundation Models Text-to-SQL Empowered by Large Language Models: A Benchmark Evalua- tion

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Revisiting Data Analysis with Pre-trained Foundation Models Retrieval-Augmented Generation for Large Language Models: A Survey

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Revisiting Data Analysis with Pre-trained Foundation Models Using experts to develop a supply chain matu- rity model in Mexico

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Revisiting Data Analysis with Pre-trained Foundation Models LLM-PBC: Logic Learn- ing Machine-based explainable rules accurately stratify the genetic risk of Primary Biliary Cholan- gitis

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Revisiting Data Analysis with Pre-trained Foundation Models LILO: Learning Interpretable Libraries by Compressing and Documenting Code

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Revisiting Data Analysis with Pre-trained Foundation Models Why do tree-based models still outper- form deep learning on typical tabular data?

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Revisiting Data Analysis with Pre-trained Foundation Models Few-shot text-to-sql translation using structure and content prompt learning

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Revisiting Data Analysis with Pre-trained Foundation Models An intro- duction to variable and feature selection

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Revisiting Data Analysis with Pre-trained Foundation Models Interpreting Equivariant Repre- sentations

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Revisiting Data Analysis with Pre-trained Foundation Models The elements of statistical learning: data mining, inference, and prediction

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Revisiting Data Analysis with Pre-trained Foundation Models Optimizing Video Selection LIMIT Queries With Commonsense Knowledge

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Revisiting Data Analysis with Pre-trained Foundation Models Declarative language design for interactive visualization

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Revisiting Data Analysis with Pre-trained Foundation Models Large Language Models for Automated Data Science: Introducing CAAFE for Context- Aware Automated Feature Engineering

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Revisiting Data Analysis with Pre-trained Foundation Models What’s Left? Concept Ground- ing with Logic-Enhanced Foundation Models

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Revisiting Data Analysis with Pre-trained Foundation Models Amortizing intractable in- ference in large language models

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Revisiting Data Analysis with Pre-trained Foundation Models A survey of knowledge en- hanced pre-trained language models

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Revisiting Data Analysis with Pre-trained Foundation Models InfiAgent-DABench: Evaluat- ing Agents on Data Analysis Tasks

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Revisiting Data Analysis with Pre-trained Foundation Models Language Models as Controlled Natural Language Semantic Parsers for Knowledge Graph Question Answering

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Revisiting Data Analysis with Pre-trained Foundation Models Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

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Revisiting Data Analysis with Pre-trained Foundation Models Automatic programming: Large language models and beyond

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