Cover Image

Molecules Meet Language: Confound-Aware Representation Learning and Chemical Property Steering in Transformer-VAE Latent Spaces

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Elabid, Zakaria, Andrzejewski, Jan, Brzoza, Bartosz, Cangi, Attila
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

TG-PhyNN: An Enhanced Physically-Aware Graph Neural Network framework for forecasting Spatio-Temporal Data
by: Elabid, Zakaria, et al.
Published: (2024)

Knowledge-Based Convolutional Neural Network for the Simulation and Prediction of Two-Phase Darcy Flows
by: Elabid, Zakaria, et al.
Published: (2024)

Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators
by: Shah, Karan, et al.
Published: (2024)

Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations
by: Shah, Karan, et al.
Published: (2025)

Scalable Machine Learning Model for Energy Decomposition Analysis in Aqueous Systems
by: Tahmasbi, Hossein, et al.
Published: (2025)

Causal Structure Representation Learning of Confounders in Latent Space for Recommendation
by: Xu, Hangtong, et al.
Published: (2023)

Materials Learning Algorithms (MALA): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations
by: Cangi, Attila, et al.
Published: (2024)

Autoregressivity in the Latent Space of a GP-VAE Language Model: An Empirical Ablation Study
by: Ruffenach, Yves
Published: (2025)

Steering Your Diffusion Policy with Latent Space Reinforcement Learning
by: Wagenmaker, Andrew, et al.
Published: (2025)

Leveraging VAE-Derived Latent Spaces for Enhanced Malware Detection with Machine Learning Classifiers
by: Ajayi, Bamidele, et al.
Published: (2025)

EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling
by: Kouzelis, Theodoros, et al.
Published: (2025)

Closed-Form Interpretation of Neural Network Latent Spaces with Symbolic Gradients
by: Wetzel, Sebastian J., et al.
Published: (2024)

VAE with Hyperspherical Coordinates: Improving Anomaly Detection from Hypervolume-Compressed Latent Space
by: Ascarate, Alejandro, et al.
Published: (2026)

A Unified Latent Space Disentanglement VAE Framework with Robust Disentanglement Effectiveness Evaluation
by: Lang, Xiaoan, et al.
Published: (2026)

Latent Chemical Space Searching for Plug-in Multi-objective Molecule Generation
by: Liu, Ningfeng, et al.
Published: (2024)

Structural Equation-VAE: Disentangled Latent Representations for Tabular Data
by: Zhang, Ruiyu, et al.
Published: (2025)

Molecule Design by Latent Prompt Transformer
by: Kong, Deqian, et al.
Published: (2023)

Molecule Design by Latent Prompt Transformer
by: Kong, Deqian, et al.
Published: (2024)

VQalAttent: a Transparent Speech Generation Pipeline based on Transformer-learned VQ-VAE Latent Space
by: Rodriguez, Armani, et al.
Published: (2024)

How Neural Losses Shape VAE Latents
by: Strano, Giorgio, et al.
Published: (2026)

STAR-VAE: Latent Variable Transformers for Scalable and Controllable Molecular Generation
by: Kwon, Bum Chul, et al.
Published: (2025)

DeCo-VAE: Learning Compact Latents for Video Reconstruction via Decoupled Representation
by: Yin, Xiangchen, et al.
Published: (2025)

Causal Structure Learning in Hawkes Processes with Complex Latent Confounder Networks
by: Jin, Songyao, et al.
Published: (2025)

Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE Model
by: Slautin, Boris N., et al.
Published: (2025)

Improved Anomaly Detection through Conditional Latent Space VAE Ensembles
by: Åström, Oskar, et al.
Published: (2024)

Relational Causal Discovery with Latent Confounders
by: Negro, Matteo, et al.
Published: (2025)

CAD-VAE: Leveraging Correlation-Aware Latents for Comprehensive Fair Disentanglement
by: Ma, Chenrui, et al.
Published: (2025)

TimewarpVAE: Simultaneous Time-Warping and Representation Learning of Trajectories
by: Rhodes, Travers, et al.
Published: (2023)

PhAME: Phenotype-Aware Molecular Editing via Latent Diffusion
by: Janisiów, Łukasz, et al.
Published: (2026)

A Deep Latent Space Model for Graph Representation Learning
by: Yang, Hanxuan, et al.
Published: (2021)

REPA-E: Unlocking VAE for End-to-End Tuning with Latent Diffusion Transformers
by: Leng, Xingjian, et al.
Published: (2025)

Contextual Molecule Representation Learning from Chemical Reaction Knowledge
by: Tang, Han, et al.
Published: (2024)

Addressing Instrument-Outcome Confounding in Mendelian Randomization through Representation Learning
by: Huang, Shimeng, et al.
Published: (2026)

Reducing Hallucinations in Vision-Language Models via Latent Space Steering
by: Liu, Sheng, et al.
Published: (2024)

Identification of Causal Direction under an Arbitrary Number of Latent Confounders
by: Chen, Wei, et al.
Published: (2025)

Recovering Latent Confounders from High-dimensional Proxy Variables
by: Mankovich, Nathan, et al.
Published: (2024)

Causal Discovery with Mixed Latent Confounding via Precision Decomposition
by: Asiaee, Amir, et al.
Published: (2025)

Spectral Representation for Causal Estimation with Hidden Confounders
by: Sun, Haotian, et al.
Published: (2024)

Impact of Latent Space Dimension on IoT Botnet Detection Performance: VAE-Encoder Versus ViT-Encoder
by: Wasswa, Hassan, et al.
Published: (2025)

CircuitVAE: Efficient and Scalable Latent Circuit Optimization
by: Song, Jialin, et al.
Published: (2024)