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Search · four archives
21 papers · ranked by Valyu relevance
Liam A. Kruse, Houjun Liu, Alexandros E. Tzikas, Mansur M. Arief + 1 more
Normalizing flows are powerful generative models that learn an invertible mapping between complex data distributions and simple latent distributions, typically a standard normal density. However, this choice of latent density can impose unnecessary complexity on the learned flow transformation due to the topological…
Enrico Bothmann, Timo Janßen, Max Knobbe, Bernhard Schmitzer + 1 more
We apply Continuous Normalizing Flows trained with the Flow Matching method to the problem of phase-space sampling in Monte Carlo event generation for high-energy collider physics. Focusing on lepton-pair and top quark pair production with multiple jets, the two computationally most expensive processes at the Large…
Alexander Vidal, Samy Wu Fung, Luis Tenorio, Stanley Osher + 1 more
'Levon Nurbekyan'] A normalizing flow (NF) is a mapping that transforms a chosen probability distribution to a normal distribution. Such flows are a common technique used for data generation and density estimation in machine learning and data science. The density estimate obtained with a NF requires a change of…
Alberto Cabezas, Louis Sharrock, Christopher Nemeth
Flows Authors: ['Alberto Cabezas' 'Louis Sharrock' 'Christopher Nemeth'] Continuous normalizing flows (CNFs) learn the probability path between a reference and a target density by modeling the vector field generating said path using neural networks. Recently, Lipman et al. [42] introduced a simple and inexpensive…
Yao Xie, Xiuyuan Cheng
space Authors: ['Yao Xie' 'Xiuyuan Cheng'] Generative AI (GenAI) has revolutionized data-driven modeling by enabling the synthesis of high-dimensional data across various applications, including image generation, language modeling, biomedical signal processing, and anomaly detection. Flow-based generative models…
Etrit Haxholli, Marco Lorenzi
While the neural ODE formulation of normalizing flows such as in FFJORD enables us to calculate the determinants of free form Jacobians in O(D) time, the flexibility of the transformation underlying neural ODEs has been shown to be suboptimal. In this paper, we present AFFJORD, a neural ODE-based normalizing flow which…
Yikai Liu, Guang Lin, Ming Chen
Molecular dynamics (MD) simulations remain the standard tool for characterizing protein conformational landscapes, but their high computational cost limits large-scale and long-timescale applications. Recent generative models, especially diffusion-based approaches, provide promising alternatives by learning equilibrium…
Keegan Kelly, Lorena Piedras, Sukrit Rao, David J. Roth
Normalizing Flows (NFs) describe a class of models that express a complex target distribution as the composition of a series of bijective transformations over a simpler base distribution. By limiting the space of candidate transformations to diffeomorphisms, NFs enjoy efficient, exact sampling and density evaluation…
Jin Sub Lee, Philip M. Kim
Accurate prediction of protein side-chain conformations is necessary to understand protein folding, protein-protein interactions and facilitate de novo protein design. Here we apply torsional flow matching and equivariant graph attention to develop FlowPacker, a fast and performant model to predict protein side-chain…
Sahil Sidheekh, Chris B. Dock, Tushar Jain, Radu Bălan + 1 more
'Maneesh Singh'] Normalizing flows provide an elegant approach to generative modeling that allows for efficient sampling and exact density evaluation of unknown data distributions. However, current techniques have significant limitations in their expressivity when the data distribution is supported on a lowdimensional…
Alexander Denker, Maximilian Schmidt, Johannes Leuschner, Peter Maass + 2 more
'Peter Maass' 'Fabiana Zama' 'Elena Loli Piccolomini'] Over recent years, deep learning methods have become an increasingly popular choice for solving tasks from the field of inverse problems. Many of these new data-driven methods have produced impressive results, although most only give point estimates for the…
Artem Ryzhikov, Maxim Borisyak, Andrey Ustyuzhanin, Denis Derkach + 1 more
'Donghyun Kim'] Anomaly detection is a challenging task that frequently arises in practically all areas of industry and science, from fraud detection and data quality monitoring to finding rare cases of diseases and searching for new physics. Most of the conventional approaches to anomaly detection, such as one-class…
Yikai Liu, Ming Chen, Guang Lin
Molecular dynamics (MD) provides a principled method for modeling equilibrium protein conformational energy landscapes, but its computational cost limits access to long timescales and larger protein systems. Recently, generative protein ensemble models and machine-learned coarse-grained force fields have emerged as…
Felix Draxler, Stefan Wahl, Christoph Schnörr, Ullrich Köthe
We present a novel theoretical framework for understanding the expressive power of couplingbased normalizing flows such as RealNVP (Dinh et al., 2017). Despite their prevalence in scientific applications, a comprehensive understanding of coupling flows remains elusive due to their restricted architectures. Existing…
Nicholas J. Tustison, Brian B. Avants, Philip A. Cook, James C. Gee + 1 more
In modeling complex probability distributions, normalizing flows provide exact-likelihood, bijective mappings between empirical data and tractable latent spaces. Building on this foundation, latent-aligned multiview normalizing (LAMNr) flows leverage these salient properties to learn shared latent subspaces across…
Nathan Mancheun Lui, Max D Li, Matthew Ford
Deep generative models for molecular graphs offer a new avenue for property optimization in drug discovery. Optimizing differentiable models that generate molecular graphs is certainly faster, cheaper, and much more accessible than traditional methods of chemical synthesis. Recent advances in generative modeling have…
Josué Page Vizcaíno, Panagiotis Symvoulidis, Zeguan Wang, Jonas Jelten + 3 more
Real-time 3D fluorescence microscopy is crucial for the spatiotemporal analysis of live organisms, such as neural activity monitoring. The eXtended field-of-view light field microscope (XLFM), also known as Fourier light field microscope, is a straightforward, single snapshot solution to achieve this. The XLFM acquires…
Authors not listed
Inverse problems, where we seek the values of inputs to a model that lead to a desired set of outputs, are a challenges subset of problems in science and engineering. In this work we demonstrate the use of two generative AI methods to solve inverse problems. We compare this approach to two more conventional approaches…
Mike Boss, Michele Volpi, Lukas Roth
In this work, we investigate modeling plant traits over time using neural processes, a class of machine learning models that learn distributions over functions. Plant growth is an inherently stochastic process with complex dynamics measured mostly at irregular times throughout the growing seasons. While individual…
Saulo de Oliveira, Aryan Pedawi, Victor Kenyon, Henry van den Bedem
Commercially available, synthesis-on-demand virtual libraries contain upwards of trillions of readily synthesizable compounds for drug discovery campaigns. These libraries are a critical resource for rapid cycles of in silico discovery, property optimization and in vitro validation. However, as these libraries continue…
Yuanqi Du, Xian Liu, Shengchao Liu, Jieyu Zhang + 1 more
Discovering new structures in the chemical space is a long-standing challenge and has important applications to various fields such as chemistry, material science, and drug discovery. Deep generative models have been used in de novo molecule design to embed molecules in a meaningful latent space and then sample new…