New algorithm infers smooth trajectories from unpaired snapshots.
problem Inferring smooth trajectories from unpaired snapshots.
method Lifts interpolation problem to phase space, regresses onto explicit acceleration field.
result Our algorithm is competitive or superior to existing methods on benchmark problems.
GER learns particle dynamics from unpaired snapshots using physics-informed GANs.
problem Learning particle dynamics from unpaired snapshots with physics constraints.
method Physics-informed generative model to fit particle ensemble distributions.
result Inferred dynamics of particle ensembles governed by SODEs up to 100 dimensions.
DDD reformulated for sparse matrices, integrating trajectory and snapshot time series data.
problem Efficiently integrate trajectory and snapshot time series data.
method Reformulate DDD to use compact basis functions, reducing parameter scaling.
result Inference of sparse matrices reduces the number of parameters in DDD.
UNSB uses neural Schrödinger Bridge to solve unpaired image-to-image translation.
problem Difficulties in unpaired image-to-image translation with diffusion models.
method Expresses SB problem as adversarial learning problems, incorporating advanced discriminators and regularization.
result Successfully solves various unpaired image-to-image translation tasks.
This research shows how to learn shared representations from unpaired data.
problem Learning shared representations from unpaired data.
method Spectral embeddings of random walk matrices from each unimodal representation.
result Shared representations can be learned almost exclusively from unpaired data.
Paper investigates multimodal contrastive learning and incorporates unpaired data.
problem Improving feature learning ability of multimodal models under noisy data.
method Initiates investigation of nonlinear loss functions for multimodal contrastive learning, analyzes performance, proposes new loss incorporating unpaired data.
result MMCL can outperform unimodal contrastive learning and robustly handle noisy data.
CrossNet uses cross-consistency to improve unpaired image translation.
problem Image-to-image translation without paired data.
method Introduced a novel architecture with cross-translators and latent cross-consistency constraints.
result CrossNet outperforms state-of-the-art on various image translation tasks.
The paper tightens bounds for estimating Schrödinger potentials in unpaired data translation.
problem Estimating Schrödinger potentials in unpaired data translation.
method Using stochastic optimal control and Ornstein-Uhlenbeck process, the paper derives tight bounds on the generalization ability of an empirical risk minimizer.
result The approach achieves almost optimal convergence rates for Gaussian mixtures.
Unpaired multi-domain causal representation learning is possible with sufficient conditions.
problem Learning shared causal representation from unpaired data across domains.
method Identify sufficient conditions for joint distribution and shared causal graph recovery.
result Practical method to recover shared latent causal graph from marginal distributions.
The paper analyzes CycleGAN's error components for unpaired data generation.
problem Analyzing approximation and estimation errors in CycleGAN for unpaired data.
method Decomposes risk into approximation and estimation errors, analyzing each separately and considering their trade-offs.
result Theoretical insights into CycleGAN's performance through error analysis.
Unpaired deep learning reconstructs MRI images from accelerated data.
problem Difficulty in acquiring matched fully sampled k-space data for supervised deep learning.
method Optimal transport driven cycleGAN architecture.
result Reconstructs high resolution MR images from accelerated k-space data.
LSDM uses unpaired data to match latent space distributions for generative modeling.
problem Generating high-quality images with limited paired data.
method Two-stage approach: latent space learning from paired and unpaired data, followed by joint distribution matching.
result LSDM enhances geometric fidelity in generated outputs and provides theoretical insights into LDMs.
New algorithm computes Schrödinger Bridge for unpaired data translation.
problem Computing optimal transport maps for unpaired data translation.
method Schrödinger Bridge Flow, a discretization of a flow of path measures.
result Eliminates the need to train multiple DDM-like models.
CT-OT Flow estimates continuous-time dynamics from discrete snapshots.
problem Estimating continuous-time dynamics from temporally aggregated snapshots with noisy or uncertain timestamps.
method Two-stage framework: aligning neighboring intervals via partial optimal transport (POT) and reconstructing a continuous-time distribution through temporal kernel smoothing.
result Reduces distributional and trajectory errors compared with existing methods across synthetic and real datasets.
Extends image-to-image translation to multiple distributions, allowing composite functions.
problem Limited to single pair translations, new mechanism scalable to multiple distributions.
method Decoupled training mechanism for multiple distributions, composite translation functions.
result Generates images with characteristics not seen in training set.
A new method for image translation without paired data.
problem Image-to-image translation between two domains with content preservation.
method Energy-based model in latent space of pretrained autoencoder.
result Improved translation quality and content preservation.
Study on estimating causal effects with limited data and multiple environments.
problem Estimating causal effects under hidden confounding with unpaired data and sparse effects.
method Instrumental variable (IV) regression with cross-fold sample splitting and ℓ1-regularized estimation. result Proposed GMM-type estimator is consistent as the number of environments grows.
A neural network improves DOA estimation from a single snapshot.
problem Estimating DOAs from a single snapshot with limited aperture.
method Deep learning architecture trained to generate high-resolution spatial spectrum.
result Our (SP)2-Net outperforms classical methods. This paper tackles unpaired data in multi-view learning, proposing a new framework and models.
problem Handling unpaired data in multi-view learning, which is more common than paired data.
method Generalized uncorrelated multi-view subspace learning framework with successive alternating approximation (SAA) method.
result Proposed models perform competitively or better than baselines in multi-view feature extraction and multi-modality classification.
Physics-informed methods infer spatial dynamics from static snapshots, but limits exist.
problem Inferring spatial dynamics from static molecular patterns.
method Combining flexible representations with mechanistic constraints, analyzing structural identifiability, and adapting physics-informed schemes.
result Static spatial patterns can identify spatially varying dynamics, but limits exist due to modeling choices.
DyMoN models complex systems from short snapshots using deep neural networks.
problem Modeling high-dimensional stochastic systems from limited snapshot data.
method Dynamics Modeling Network (DyMoN) as a deep generative Markov model trained on current and next-state pairs.
result DyMoN outperforms shallow and deep models in capturing system dynamics and generating longitudinal trajectories.
Unified framework for discrete diffusion modeling with flexible noising processes.
problem Efficient modeling of large discrete state spaces with arbitrary corruption dynamics.
method Generalized Discrete Diffusion from Snapshots (GDDS) framework that supports uniformization for fast noising and snapshot-based ELBO for reverse process.
result GDDS outperforms existing discrete diffusion methods in training efficiency and generation quality.
Direction of arrival (DOA) estimation is a classical problem in signal processing with many practical applications. Its research has recently been advanced owing to the development of methods based on sparse signal reconstruction. While these methods have shown advantages over conventional ones, there are still difficu…
A neural network, IHT-Net, improves DOA estimation with sparse arrays.
problem Single-snapshot DOA estimation with sparse arrays in dynamic settings.
method IHT-inspired neural network with recurrent neural network and autoencoders.
result IHT-Net achieves faster convergence and higher accuracy in DOA estimation.
Proposes a method for translating synthetic images to real-world scenarios.
problem Ill-posed nature of unpaired image-to-image translation.
method Explicitly enforce preservation of ground-truth labels on translated images.
result Significantly improved performance over state-of-the-art model for semantic segmentation.
Bayesian method predicts future network configurations from past snapshots.
problem Reconstructing evolving networks from partial observations.
method Bayesian approach using past network snapshots to inform future predictions.
result Method accurately predicts link probabilities and network structure.
Model learns association between text and speech without paired data.
problem Learning association between text and speech with limited paired data.
method Noisy channel generative model with variational inference and KL encoder loss.
result Tiny paired data (5 minutes) sufficient to learn association.
DMAE learns shared latent space from unpaired data.
problem Learning shared latent space from unpaired multi-modal data.
method Formulates cross-domain representation learning and object matching problem, optimizes autoencoders and pairing.
result Promising results in image captioning and unsupervised classifier learning.
The paper proves probabilistic alignment between unseen modalities using contrastive learning.
problem Aligning unseen modalities in unsupervised learning.
method Bayesian approach and direct comparison of contrastive representations.
result Direct comparison of contrastive representations recovers the same likelihood ratio as probabilistic graphical models.
DPOT uses deep learning to compute optimal transport efficiently.
problem Computing optimal transport between continuous distributions from unpaired samples.
method DeepParticle methods for min-min optimization without network structure restrictions.
result Established weak convergence and error bounds between learned and optimal maps.
Paper presents a method to align unpaired samples across different modalities.
problem Challenges in collecting paired samples for multimodal representation learning.
method Uses propensity score alignment based on Rubin's framework to estimate a common space for unpaired samples.
result Optimal transport matching significantly improves alignment in real-world data.
TNDE quantifies dynamic gene drivers from single-cell snapshots.
problem Reconstructing time-resolved regulatory effects in biological processes.
method Time-varying Network Driver Estimation (TNDE) using shared graph attention encoder and partial optimal transport.
result TNDE identifies stage-specific driver genes in mouse erythropoiesis.
New method identifies shared components from unpaired multimodal mixtures.
problem Identify shared components from unpaired multimodal mixtures.
method Distribution divergence minimization-based loss with sufficient conditions for identifiability.
result Sufficient conditions for shared component identifiability from unaligned multimodal mixtures.
FDBM models use fractional Brownian motion to model complex stochastic processes.
problem Capturing memory effects and long-range dependencies in stochastic processes.
method Developed a generative diffusion bridge framework using a Markovian approximation of fractional Brownian motion.
result FDBM outperforms standard models in predicting future states and unpaired data translation.
Recently a variety of LSTM-based conditional language models (LM) have been applied across a range of language generation tasks. In this work we study various model architectures and different ways to represent and aggregate the source information in an end-to-end neural dialogue system framework. A method called snaps…
trVAE improves conditional out-of-sample generation for unpaired data.
problem Challenges in generating high-dimensional samples conditional on low-dimensional descriptors out-of-sample.
method trVAE uses maximum mean discrepancy (MMD) to match distributions across conditions in the decoder layer.
result Improved robustness and accuracy in predicting cellular perturbation responses and disease.
AdaFlow adapts DNN density estimators to new distributions for anomaly detection and cross-domain translation.
problem Unsupervised anomaly detection and cross-domain translation with limited paired data.
method AdaFlow combines Normalizing Flows and Adaptive Batch-Normalizations for domain adaptation.
result AdaFlow efficiently adapts to new distributions with minimal computational resources.
TURB-Rot provides a large database of turbulent rotating flow snapshots for research.
problem Lack of large-scale, high-resolution datasets for turbulent rotating flows.
method Direct Numerical Simulations of Navier-Stokes equations with rotation.
result Provides a diverse set of 300K complex images and fields for testing.
The paper proposes a method to estimate joint probability from unpaired data using entropic transport kernels.
problem Estimating joint probability from unpaired data with unknown internal ordering.
method Maximum-likelihood inference, entropic optimal transport kernels, EMML algorithm.
result The method can recover true density from empirical approximations as the number of blocks increases.
Study characterizes spike deconvolution basin for noisy data.
problem Recover spike locations from noisy convolution with PSF across multiple snapshots.
method Variable-projection formulation, explicit basin of convexity characterization, local convergence guarantees.
result Consistent estimator within basin of convexity under stochastic noise, complementary error bound under adversarial noise.
Paper improves DOA estimation in sparse arrays using Siamese neural networks.
problem Challenges in DOA estimation with limited snapshots in sparse linear arrays.
method Introduces a Siamese neural network with a sparse augmentation layer for enhanced signal feature embedding.
result Demonstrates improved DOA estimation accuracy in sparse arrays.
Study on inferring dynamic communities and links in networks with memory.
problem Inferring communities and links in dynamic networks with memory.
method Maximum likelihood inference from single snapshot observations, analytical and numerical analysis.
result Link persistence makes community detection harder, while community persistence makes it easier.
Deep Confidence provides efficient error estimation for deep neural networks.
problem Estimating the reliability of predictions from deep learning models.
method Snapshot Ensembling and conformal prediction.
result Deep Confidence generates narrower confidence intervals than alternative methods.
DiffVolume generates realistic volume snapshots for LOBs.
problem Generating high-dimensional volume snapshots in LOBs is challenging.
method Conditional Diffusion model for volume generation.
result DiffVolume outperforms in realism, counterfactual generation, and downstream prediction.
A new method improves robustness in image translation by modeling uncertainty.
problem Performance degradation in image translation models due to lack of robustness to outliers and uncertainty.
method UGAC method based on Uncertainty-aware Generalized Adaptive Cycle Consistency, modeling per-pixel residual with generalized Gaussian distribution.
result Our method exhibits stronger robustness towards unseen perturbations in test data.
New method infers population dynamics from snapshots using path space optimization.
problem Recover dynamics of a population from its temporal marginals.
method Grid-free algorithm using Schrödinger bridges coupled via noisy gradient descent in mean-field limit.
result Global convergence to min-entropy estimator with end-to-end theoretical guarantees.
New method interprets quantum many-body snapshots for phase detection.
problem Classifying phases of matter from quantum simulations.
method Confusion learning with correlation convolutional neural networks.
result Network detects changes in thermodynamic properties of quantum systems.
SnapMMD forecasts cell differentiation outcomes from snapshot data.
problem Forecasting cell differentiation outcomes from limited snapshot data.
method SnapMMD learns dynamics by directly fitting the joint distribution of state measurements and observation time with MMD loss, allowing for unknown and state-dependent volatilities.
result SnapMMD delivers accurate forecasts and an R2-style statistic for diagnosing fit.