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CLAIM uses LLMs to fill missing tabular data with contextual text, outperforming traditional methods and improving downstream task accuracy.

CLAIM: A Contextual Language Model for Accurate Imputation of Missing Tabular Data

MaCoDE turns masked language modeling into a powerful method for synthetic tabular data generation with high ML utility and privacy control.

MLM Meets Conditional Density: A New Way to Handle Tabular Data Synthesis

How Masked Language Modeling Can Be Used to Generate Synthetic Tabular Data

Why MaCoDE Outperforms GANs in Tabular Data Generation

Generating Private, High-Utility Tabular Data with Masked Language Models

Discover how advances in Novel Class Discovery (NCD) algorithms are unlocking new possibilities in solving NCD problems even in realistic scenarios. 

A Practical Approach to Novel Class Discovery in Tabular Data: Appendix 

Explore the complete training procedure for Novel Class Discovery (NCD), integrating models. 

A Practical Approach to Novel Class Discovery in Tabular Data: Full training procedure

Discover how to effectively tackle Novel Class Discovery (NCD) in tabular data without relying on prior knowledge of novel classes.

A Practical Approach to Novel Class Discovery in Tabular Data

4/28/2024: Trending stories on Hackernoon today!

The TechBeat: Building Effective Modern Data Architectures with Iceberg, Tabular and MinIO (4/28/2024)

Modern datalakes provide a central hub for all your data needs. However, building and managing an effective data lake can be complex.

Building Effective Modern Data Architectures with Iceberg, Tabular and MinIO

How to create simple bar charts in Python using tabular data.

Four Types of Bar Charts in Python - Based on Tabular Data

Discover how the Line Text Scanner app improves text processing for tabular data, especially on receipts and tables.

Using Google MLKit Text Recognition to Scan Tabular Data

Despite AI/ML research focusing on unstructured data, tabular data remains the primary area of time and financial investment in the Data Integration world.


What You Need to Know About Tabular Data as a Challenge

Transformer-based models are a game-changer when it comes to using unstructured text data. As of September 2020, the top-performing models in the General Language Understanding Evaluation (GLUE) benchmark are all BERT transformer-based models. At Georgian, we often encounter scenarios where we have supporting tabular feature information and unstructured text data. We found that by using the tabular data in these models, we could further improve performance, so we set out to build a toolkit that makes it easier for others to do the same.

A Beginner Guide to Incorporating Tabular Data via HuggingFace Transformers

Tree-based models like Random Forest and XGBoost have become very popular in solving tabular(structured) data problems and gained a lot of tractions in Kaggle competitions lately. It has its very deserving reasons. However, in this article, I want to introduce a different approach from fast.ai’s Tabular module leveraging.

Gain State-Of-The-Art Results on Tabular Data with Deep Learning & Embedding Layers [A How To Guide]

The PDF (<a href="https://en.wikipedia.org/wiki/PDF" target="_blank">Portable Document Format</a>) was born out of <a href="http://www.planetpdf.com/planetpdf/pdfs/warnock_camelot.pdf" target="_blank">The Camelot Project</a> to create “a universal way to communicate documents across a wide variety of machine configurations, operating systems and communication networks”. Basically, the goal was to make documents viewable on any display and printable on any modern printer. PDF was built on top of <a href="https://en.wikipedia.org/wiki/PostScript" target="_blank">PostScript</a> (a page description language), which had already solved this “view and print anywhere” problem. PDF encapsulates the components required to create a “view and print anywhere” document. These include characters, fonts, graphics and images.

Announcing Camelot, a Python Library to Extract Tabular Data from PDFs

This article will discuss compression in the Big Data context, covering the types and methods of compression

Compression in Big Data: Types and Techniques

Everyone is talking about ontologies. Why, what is an ontology actually, and how is it related to graphs?

Microsoft Fabric IQ Puts Ontology Back on the Map — and Back in the Confusion

Federated fine-tuning methods for secure, private and scalable tabular model training in regulated sectors.

The Pragmatic Guide to Federated AI: Building Compliant LLM/XGBoost Pipelines for Sensitive Data

Meet the expert faculty behind the HackerNoon Blogging Fellowship and learn how their mentorship helps writers grow, gain authority, and master modern content.

They Are Real, and They Are Here - Meet The HackerNoon Blogging Course Faculty

Is graph really the new star schema? What do graphs like to non-insiders, and what attracts them to the community, methodologies, applications, and innovation? 

Knowledge Graphs Gain Traction as AI Pushes Beyond Traditional Data Models

Speed up XGBoost training by 46x with one parameter change. Learn how GPU acceleration saves hours, boosts iteration, and scales to big data.


Stop Waiting: Make XGBoost 46x Faster With One Parameter Change

Fine-tune ResNet-18 from TensorFlow Model Garden to classify CIFAR-10 images with this step-by-step deep learning tutorial.

Fine-Tuning ResNet-18 with TensorFlow Model Garden for CIFAR-10 Classification

Google's acquisition of Wiz for $32 billion was a clear signal to the industry: the cloud security war has a winner.

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