The Case for TVAE: When Fidelity is your number one priority: We know that Tabular Variational Autoencoder (TVAE) often beats other deep learning models when it comes to simple benchmarks, but why?
Abstract: In predictive medicine, accurately assessing the risk of heart disease remains quite difficult, particularly when dealing with small and diverse clinical tabular datasets. To address these ...
Recent, rapid advances in deep generative models for protein design have focused on small proteins with lots of data. Such models perform poorly on large proteins with limited natural sequences, for ...
The NLRP3 inflammasome, regulated by TLR4, plays a pivotal role in periodontitis by mediating inflammatory cytokine release and bone loss induced by Porphyromonas gingivalis. Periodontal disease ...
Generating synthetic data is useful when you have imbalanced training data for a particular class, for example, generating synthetic females in a dataset of employees that has many males but few ...
Recent advances in feature selection methods for breast cancer recurrence prediction: A systematic review. This is an ASCO Meeting Abstract from the 2025 ASCO Annual Meeting I. This abstract does not ...
description [NeurIPS 2025][Self-Supervised Learning][tabular data] This paper proposes TANDEM (Tree-And-Neural Dual Encoder Model), a hybrid autoencoder architecture that jointly trains a neural ...
Abstract: Sign language translation (SLT) traditionally requires costly human gloss annotations. Recently, gloss-free approaches, which directly generate text from video, have been studied and ...
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