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Found 14 papers, 6 papers with code
Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design
Our approach achieves state-of-the-art co-design performance while allowing the same multimodal model to be used for flexible generation of the sequence or structure.
Improved motif-scaffolding with SE(3) flow matching
The first is motif amortization, in which FrameFlow is trained with the motif as input using a data augmentation strategy.
Hyperspectral Lightcurve Inversion for Attitude Determination
The theoretical and practical aspects of this task are investigated, and the methodology is tested on synthetic data.
MALCOM-PSGD: Inexact Proximal Stochastic Gradient Descent for Communication-Efficient Decentralized Machine Learning
Recent research indicates that frequent model communication stands as a major bottleneck to the efficiency of decentralized machine learning (ML), particularly for large-scale and over-parameterized neural networks (NNs).
Fast protein backbone generation with SE(3) flow matching
We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching.
Space Object Identification and Classification from Hyperspectral Material Analysis
Two techniques are used for material identification and classification: one based on machine learning and the other based on a least square match with a library of known spectra.
Rare Life Event Detection via Mobile Sensing Using Multi-Task Learning
Rare life events significantly impact mental health, and their detection in behavioral studies is a crucial step towards health-based interventions.
Diffusion Schrödinger Bridge Matching
However, while it is desirable in many applications to approximate the deterministic dynamic Optimal Transport (OT) map which admits attractive properties, DDMs and FMMs are not guaranteed to provide transports close to the OT map.
normflows: A PyTorch Package for Normalizing Flows
It allows to build normalizing flow models from a suite of base distributions, flow layers, and neural networks.
A Continuous Time Framework for Discrete Denoising Models
We provide the first complete continuous time framework for denoising diffusion models of discrete data.
Online Variational Filtering and Parameter Learning
We present a variational method for online state estimation and parameter learning in state-space models (SSMs), a ubiquitous class of latent variable models for sequential data.
Patient-independent Schizophrenia Relapse Prediction Using Mobile Sensor based Daily Behavioral Rhythm Changes
In this work, we investigated a machine learning based schizophrenia relapse prediction model using mobile sensing data to characterize behavioral features.
Routine Clustering of Mobile Sensor Data Facilitates Psychotic Relapse Prediction in Schizophrenia Patients
The clustering model based features, together with other features characterizing the mobile sensing data, resulted in an F2 score of 0. 24 for the relapse prediction task in a leave-one-patient-out evaluation setting.
Gradient-based tuning of Hamiltonian Monte Carlo hyperparameters
Existing approaches for automating this task either optimise a proxy for mixing speed or consider the HMC chain as an implicit variational distribution and optimize a tractable lower bound that is too loose to be useful in practice.
