On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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5 results for “augmentation-free”

T-JEPA: Augmentation-Free Self-Supervised Learning for Tabular Datacs.LG

T-JEPA learns tabular data representations without augmentations, outperforming traditional methods.

problem Challenges in self-supervised learning for tabular data due to lack of data augmentations.

method T-JEPA uses a Joint Embedding Predictive Architecture (JEPA) to predict latent representations of different subsets of features within the same sample.

result Significant improvement in classification and regression tasks, outperforming traditional methods.

self-supervised learning tabular data augmentation-free latent representation mask reconstruction

2024-10-07abs ↗pdf ↗

Predicting Adversarial Examples with High Confidencecs.LG

It has been suggested that adversarial examples cause deep learning models to make incorrect predictions with high confidence. In this work, we take the opposite stance: an overly confident model is more likely to be vulnerable to adversarial examples. This work is one of the most proactive approaches taken to date, as…

2018-02-13abs ↗pdf ↗

An "augmentation-free" rotation invariant classification scheme on point-cloud and its application to neuroimagingeess.IV

Recent years have witnessed the emergence and increasing popularity of 3D medical imaging techniques with the development of 3D sensors and technology. However, achieving geometric invariance in the processing of 3D medical images is computationally expensive but nonetheless essential due to the presence of possible er…

2019-11-05abs ↗pdf ↗

Contrastive Graph Clustering in Curvature Spacescs.LG

A new model for graph clustering using curvature spaces.

problem Graph clustering from a geometric perspective.

method Introducing a heterogeneous curvature space and a contrastive learning approach.

result CONGREGATE model outperforms state-of-the-art competitors.

graph clustering curvature spaces contrastive learning Ricci curvature deep graph clustering

2023-05-05abs ↗pdf ↗

EntProp: High Entropy Propagation for Improving Accuracy and Robustnessstat.ML

EntProp increases entropy of clean samples to generate out-of-distribution data for better DNN performance.

problem Improving deep neural networks' accuracy and robustness to out-of-distribution data.

method High entropy propagation using data augmentation and free adversarial training.

result EntProp achieves higher standard accuracy and robustness with lower training cost.

deep learning entropy out-of-distribution data augmentation free adversarial training

2024-05-29abs ↗pdf ↗