Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Pytorch frame: A modular framework for multi-modal tabular learning
We present PyTorch Frame, a PyTorch-based framework for deep learning over multi-modal
tabular data. PyTorch Frame makes tabular deep learning easy by providing a PyTorch …
tabular data. PyTorch Frame makes tabular deep learning easy by providing a PyTorch …
CARTE: pretraining and transfer for tabular learning
MJ Kim, L Grinsztajn, G Varoquaux - arxiv preprint arxiv:2402.16785, 2024 - arxiv.org
Pretrained deep-learning models are the go-to solution for images or text. However, for
tabular data the standard is still to train tree-based models. Indeed, transfer learning on …
tabular data the standard is still to train tree-based models. Indeed, transfer learning on …
Anygraph: Graph foundation model in the wild
The growing ubiquity of relational data structured as graphs has underscored the need for
graph learning models with exceptional generalization capabilities. However, current …
graph learning models with exceptional generalization capabilities. However, current …
Towards graph foundation models for personalization
A Damianou, F Fabbri, P Gigioli, M De Nadai… - … Proceedings of the …, 2024 - dl.acm.org
In the realm of personalization, integrating diverse information sources such as consumption
signals and content-based representations is becoming increasingly critical to build state-of …
signals and content-based representations is becoming increasingly critical to build state-of …
Automating Feature Extraction from Entity-Relation Models: Experimental Evaluation of Machine Learning Methods for Relational Learning
With the exponential growth of data, extracting actionable insights becomes resource-
intensive. In many organizations, normalized relational databases store a significant portion …
intensive. In many organizations, normalized relational databases store a significant portion …
Revisiting Data Analysis with Pre-trained Foundation Models
Data analysis focuses on harnessing advanced statistics, programming, and machine
learning techniques to extract valuable insights from vast datasets. An increasing volume …
learning techniques to extract valuable insights from vast datasets. An increasing volume …
Learning Efficient Positional Encodings with Graph Neural Networks
CI Kanatsoulis, E Choi, S Jegelka, J Leskovec… - arxiv preprint arxiv …, 2025 - arxiv.org
Positional encodings (PEs) are essential for effective graph representation learning because
they provide position awareness in inherently position-agnostic transformer architectures …
they provide position awareness in inherently position-agnostic transformer architectures …
ContextGNN: Beyond Two-Tower Recommendation Systems
Recommendation systems predominantly utilize two-tower architectures, which evaluate
user-item rankings through the inner product of their respective embeddings. However, one …
user-item rankings through the inner product of their respective embeddings. However, one …
Hypergraph Neural Networks with Logic Clauses
JP Gandarela de Souza, G Zaverucha… - … Joint Conference on …, 2024 - openaccess.city.ac.uk
The analysis of structure in complex datasets has become essential to solving difficult
Machine Learning problems. Relational aspects of data, capturing relationships between …
Machine Learning problems. Relational aspects of data, capturing relationships between …
TabGraphs: A Benchmark and Strong Baselines for Learning on Graphs with Tabular Node Features
G Bazhenov, O Platonov, L Prokhorenkova - arxiv preprint arxiv …, 2024 - arxiv.org
Tabular machine learning is an important field for industry and science. In this field, table
rows are usually treated as independent data samples, but additional information about …
rows are usually treated as independent data samples, but additional information about …