Author name cluster

Tuan Le

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6 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Diffusion Generative Modeling on Lie Group Representations

Marco Bertolini
Tuan Le
Djork-Arné Clevert

We introduce a novel class of score-based diffusion processes that operate directly in the representation space of Lie groups. Leveraging the framework of Generalized Score Matching, we derive a class of Langevin dynamics that decomposes as a direct sum of Lie algebra representations, enabling the modeling of any target distribution on any (non-Abelian) Lie group. Standard score-matching emerges as a special case of our framework when the Lie group is the translation group. We prove that our generalized generative processes arise as solutions to a new class of paired stochastic differential equations (SDEs), introduced here for the first time. We validate our approach through experiments on diverse data types, demonstrating its effectiveness in real-world applications such as $\text{SO}(3)$-guided molecular conformer generation and modeling ligand-specific global $\text{SE}(3)$ transformations for molecular docking, showing improvement in comparison to Riemannian diffusion on the group itself. We show that an appropriate choice of Lie group enhances learning efficiency by reducing the effective dimensionality of the trajectory space and enables the modeling of transitions between complex data distributions.

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ICLR Conference 2024 Conference Paper

Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation

Tuan Le
Julian Cremer
Frank Noé
Djork-Arné Clevert
Kristof Schütt

Deep generative diffusion models are a promising avenue for 3D de novo molecular design in materials science and drug discovery. However, their utility is still limited by suboptimal performance on large molecular structures and limited training data. To address this gap, we explore the design space of E(3)-equivariant diffusion models, focusing on previously unexplored areas. Our extensive comparative analysis evaluates the interplay between continuous and discrete state spaces. From this investigation, we present the EQGAT-diff model, which consistently outperforms established models for the QM9 and GEOM-Drugs datasets. Significantly, EQGAT-diff takes continuous atom positions, while chemical elements and bond types are categorical and uses time-dependent loss weighting, substantially increasing training convergence, the quality of generated samples, and inference time. We also showcase that including chemically motivated additional features like hybridization states in the diffusion process enhances the validity of generated molecules. To further strengthen the applicability of diffusion models to limited training data, we investigate the transferability of EQGAT-diff trained on the large PubChem3D dataset with implicit hydrogen atoms to target different data distributions. Fine-tuning EQGAT-diff for just a few iterations shows an efficient distribution shift, further improving performance throughout data sets. Finally, we test our model on the Crossdocked data set for structure-based de novo ligand generation, underlining the importance of our findings showing state-of-the-art performance on Vina docking scores.

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NeurIPS Conference 2022 Conference Paper

Unsupervised Learning of Group Invariant and Equivariant Representations

Robin Winter
Marco Bertolini
Tuan Le
Frank Noe
Djork-Arné Clevert

Equivariant neural networks, whose hidden features transform according to representations of a group $G$ acting on the data, exhibit training efficiency and an improved generalisation performance. In this work, we extend group invariant and equivariant representation learning to the field of unsupervised deep learning. We propose a general learning strategy based on an encoder-decoder framework in which the latent representation is separated in an invariant term and an equivariant group action component. The key idea is that the network learns to encode and decode data to and from a group-invariant representation by additionally learning to predict the appropriate group action to align input and output pose to solve the reconstruction task. We derive the necessary conditions on the equivariant encoder, and we present a construction valid for any $G$, both discrete and continuous. We describe explicitly our construction for rotations, translations and permutations. We test the validity and the robustness of our approach in a variety of experiments with diverse data types employing different network architectures.

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IROS Conference 2017 Conference Paper

A multi-functional inspection robot for civil infrastructure evaluation and maintenance

Spencer Gibb
Tuan Le
Hung Manh La
Ryan Schmid
Tony Berendsen

Satisfactory operation of civil infrastructure is of critical importance to an economy. In order to maintain performance, infrastructure needs to be properly maintained. Inspecting infrastructure is inherently labor-intensive work and costly. In this paper, we propose a solution to cost-effective infrastructure inspection by developing a multi-functional inspection robot. The robot is equipped with several state-of-the-art non-destructive evaluation (NDE) sensors to perform inspection. The robot is able to perform selected inspection methods in certain areas based on multiple sensor data fusion. With this, the overall inspection time is reduced, which in turn reduces maintenance cost. An inspection framework based on multiple NDE data sensor fusion is proposed. Detailed discussions include robot design, robot navigation and sensor data fusion.

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ICRA Conference 2017 Conference Paper

Autonomous robotic system using non-destructive evaluation methods for bridge deck inspection

Tuan Le
Spencer Gibb
Nhan H. Pham
Hung Manh La
Logan Falk
Tony Berendsen

Bridge condition assessment is important to maintain the quality of highway roads for public transport. Bridge deterioration with time is inevitable due to aging material, environmental wear and in some cases, inadequate maintenance. Non-destructive evaluation (NDE) methods are preferred for condition assessment for bridges, concrete buildings, and other civil structures. Some examples of NDE methods are ground penetrating radar (GPR), acoustic emission, and electrical resistivity (ER). NDE methods provide the ability to inspect a structure without causing any damage to the structure in the process. In addition, NDE methods typically cost less than other methods, since they do not require inspection sites to be evacuated prior to inspection, which greatly reduces the cost of safety related issues during the inspection process. In this paper, an autonomous robotic system equipped with three different NDE sensors is presented. The system employs GPR, ER, and a camera for data collection. The system is capable of performing real-time, cost-effective bridge deck inspection, and is comprised of a mechanical robot design and machine learning and pattern recognition methods for automated steel rebar picking to provide realtime condition maps of the corrosive deck environments.

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AAAI Conference 2014 Conference Paper

Manifold Learning for Jointly Modeling Topic and Visualization

Tuan Le
Hady Lauw

Classical approaches to visualization directly reduce a document’s high-dimensional representation into visualizable two or three dimensions, using techniques such as multidimensional scaling. More recent approaches consider an intermediate representation in topic space, between word space and visualization space, which preserves the semantics by topic modeling. We call the latter semantic visualization problem, as it seeks to jointly model topic and visualization. While previous approaches aim to preserve the global consistency, they do not consider the local consistency in terms of the intrinsic geometric structure of the document manifold. We therefore propose an unsupervised probabilistic model, called SE- MAFORE, which aims to preserve the manifold in the lowerdimensional spaces. Comprehensive experiments on several real-life text datasets of news articles and web pages show that SEMAFORE significantly outperforms the state-of-the-art baselines on objective evaluation metrics.

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