Papers

Field

Top tools here

Open problems in Neural operators

Stable and efficient online reinforcement learning for diffusion models3 papers raise it · 3 address it
Current methods are either inefficient due to poor credit assignment or unstable without careful tuning, limiting practical deployment.
- DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Modelsraises · addresses
- Routing Policy Optimization for Online Diffusion Reinforcement Learningraises · addresses
- MEND: RL For Flow Models via Proximal Velocity Matchingraises · addresses
Scalable minibatch optimal transport for flow matching1 paper raises it · 1 addresses it
Small minibatches limit benefits of optimal transport, while large minibatches are computationally prohibitive, and theoretical gaps remain in gradient and integration stability.
- Scaling Online Optimal Transport Minibatches for Flow Matchingraises · addresses
Benchmarking System One models on graph tasks1 paper raises it · 1 addresses it
Capabilities of fast, choice-based models on graph-related tasks are unclear, and existing evaluations lack standardized, diagnostic benchmarks.
- GraphDecisionBench: Benchmarking System One Models on Graph Tasksraises · addresses
Efficient edge sparsification for graph neural networks1 paper raises it · 1 addresses it
Reusing a fixed sparse graph locks training to a fixed topology, while dynamic sparsification is computationally expensive.
- EDiS: Edge Disjoint Subgraph Sparsification Framework for Graph Neural Networksraises · addresses

Named by an AI from these papers' abstracts.

Open problems

Recently funded

Topics in this field

neural operators · physics informed neural · informed neural networks375 papers34 this week
graph neural · graph learning · graph neural networks312 papers30 this week
relu networks · gradient descent · deep neural161 papers24 this week
deep neural · neural networks · neural network163 papers22 this week
flow matching · normalizing flows · flow205 papers18 this week
bayesian neural · bayesian networks · amortized bayesian106 papers16 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

Activity

Recent papers

All papers in Research

arXiv cs.LGOct 28
Benchmarking Graph Neural Networks for AC Power Flow: The Role of Inductive Biases and Physical Constraints
Federico Clerici, Ferran Bohigas-Daranas, Hamid Latif-Martínez, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt +1
Fast and accurate models for AC power flow (ACPF) are increasingly important as modern electrical grids require more frequent system analyses. Graph neural networks (GNNs) naturally exploit electrical grid topology, yet the effects of architectural design choices on predictive performance remain poorly understood given differences in graph representations and evaluation settings across studies. We present a unified benchmark of representative GNN architectures for ACPF prediction under a common experimental framework. All models use identical preprocessing and training procedures and comparable parameter budgets, isolating the impact of message-passing mechanisms and inductive biases. We also analyze errors by distance from the slack bus and systematically ablate key design components. We evaluate predictive accuracy and physical consistency on the IEEE 14-bus and IEEE 118-bus systems. Results indicate that anisotropic message-passing architectures offer the most favorable trade-off between predictive accuracy and physical consistency under the studied parameter and topology constraints. Explicit edge features encoding line impedances and global context aggregation are…
PDF arXiv
arXiv stat.MLOct 28
Memorization Capacity of Invariant Convolutional Neural Nets
Sjoerd Dirksen, Patrick Finke, Paul Geuchen
Neural networks are powerful memorizers which are often able to interpolate their training data even when the labels have been randomized. While there exists an extensive literature on the memorization capacity of fully connected networks, in practice more specific architectures with problem-dependent properties are preferred, such as convolutional networks for image classification. In this paper we consider convolutional networks which are invariant with respect to a given arbitrary finite group of orthogonal transformations, which may include translations, reflections, and/or rotations of images and invariances of higher order tensors. We prove upper bounds on the size of an invariant convolutional neural network with threshold activations that is required to interpolate a given dataset with two classes. Our bounds are formulated in terms of a complexity measure that takes the invariant structure into account and is instance-specific, i.e., it is related to the mutual arrangement of the classes in the given dataset and their geometric properties rather than the number of samples. Our proof is constructive: for given input data, instances of an interpolating network can be…
PDF arXiv
arXiv cs.LGOct 28
Discovery of Diverse and Realistic Financial Tail-Risk Using Generative Flow Networks
Adir Serruya, Gilad Katz
Accurate modeling of rare, high-impact financial events is essential for robust risk management. Sampling-based techniques such as Sequential Markov Chain Monte Carlo (SMCMC) generate diverse scenarios but inefficiently explore high-risk regions, while Deep Reinforcement Learning (DRL) converges on narrow sets of scenarios, limiting diversity. We present GRID, which uses Generative Flow Networks (GFlowNets) -- samplers that draw objects in proportion to a reward -- to systematically generate diverse and realistic financial tail-risk scenarios. Building on continuous GFlowNets, GRID introduces a flexible mixture-policy architecture with variable-specific distribution families for high-dimensional, sequential macro-financial data, together with plausibility constraints that keep generated trajectories realistic. A central practical advantage is robustness to reward-proxy imperfection: because GFlowNets sample proportionally rather than maximize, they remain broadly exploratory even when the oracle's out-of-sample accuracy is low -- an unavoidable reality in financial forecasting. On three real-world crises, GRID improves scenario diversity, realism, and tail-risk coverage over RL…
PDF arXiv
arXiv cs.LGOct 28
ExplainCC: Explaining Correlated Clusters by Node Labels
Nadeesha Shyami Kumari Herath Mudiyanselage, Florian Adriaens, Nikolaj Tatti
An increasingly important requirement for clustering algorithms is that decisions and outcomes need to be clearly understandable for human users. We address this requirement for the Correlation Clustering (CC) problem, in case the objects to be clustered are accompanied by labels. In CC one seeks to partition a set of objects into clusters based on pairwise similarity and dissimilarity information. Our proposed model only considers clusters that can be explained by a label tree, which is essentially a decision tree that partitions the objects according to labels. The goal is to find an explainable partition of the data that minimizes the CC objective function. We formalize this problem from a theoretical viewpoint, and prove several hardness of approximation results, also when constraining the size of the tree. Our proposed greedy method finds locally optimal splits, and works recursively in a top-down approach. To efficiently compute the best split, we carefully maintain certain counters. An experimental evaluation demonstrates its effectiveness. The proposed algorithm processes graphs with over million edges in a few minutes, can find the ground truth in synthetic data, and is…
PDF arXiv
arXiv cs.LGOct 28
Logical Guidance of Stochastic Interpolants
Gerrit Gerhartz, Francesco Alesiani, Jonathan Warrell, Henrik Christiansen, Matheus Ferraz +1
Guidance and composition of generative models have applications in visual design, chemistry, and biology. Logical guidance composes conditional scores exactly for diffusion models. However, Flow Matching (FM) and Stochastic Interpolants (SI) learn velocity fields. We show that negation and exclusive disjunction admit exact conditional velocity composition on any interpolant, whereas conjunction, and therefore any formula containing one, is exact on Gaussian paths but in general incurs a drift-interaction error, so composed velocities need not sample the logical target. LOGSI solves this by using score-based re-equilibration as our principled mechanism and falling back to the Gaussian-path velocity rule as an approximate transport predictor. In non-Gaussian bridge problems, generation alternates transport steps with score re-equilibration steps. On image trans fer (EMNIST to CMNIST, CelebA) and QM9 source-to-target molecular editing with logical attribute constraints, LOGSI improves rule conformity at the same network-evaluation budget over composed-velocity baselines, while also improving fidelity on the image tasks. More broadly, LOGSI applies wherever stochastic interpolants…
PDF arXiv
arXiv cs.LGOct 28
CFKS: Continuum-Feature Kernel PDE Solvers
Vikas Dwivedi, Balaji Srinivasan, Monica Sigovan, Bruno Sixou
Neural-network-based PDE solvers require architectural choices, particularly the number of layers and neurons per layer. Fixing a single Gaussian radial basis function (RBF) feature layer and taking the number of features to the continuum limit removes the need to prescribe a finite feature count or select individual feature centers, thereby reducing representation arbitrariness. Motivated by this principle, we introduce Continuum-Feature Kernel Solver (CFKS), a deterministic framework for PDEs obtained by analytically marginalizing a bounded continuum of Gaussian RBF features. CFKS works directly with the resulting continuum-feature mean kernel and its operator-applied counterparts, parameterized by prescribed Gaussian widths and bounded center distributions. For linear PDEs, CFKS leads to symmetric operator-kernel collocation systems with null-space enforcement of physical constraints. Nonlinear PDEs are addressed through linearized initialization and nonlinear least-squares residual minimization using analytical kernel derivatives and Jacobians. Residual-guided collocation enrichment and sequential space--time-slab marching provide additional resolution strategies, while…
PDF arXiv

Papers

Topics in this field

neural operators · physics informed neural · informed neural networks375 papers34 this week
graph neural · graph learning · graph neural networks312 papers30 this week
relu networks · gradient descent · deep neural161 papers24 this week
deep neural · neural networks · neural network163 papers22 this week
flow matching · normalizing flows · flow205 papers18 this week
bayesian neural · bayesian networks · amortized bayesian106 papers16 this week

Active contributors · 90 days

Recent papers

All papers in Research

arXiv cs.LGOct 28
Benchmarking Graph Neural Networks for AC Power Flow: The Role of Inductive Biases and Physical Constraints
Federico Clerici, Ferran Bohigas-Daranas, Hamid Latif-Martínez, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt +1
Fast and accurate models for AC power flow (ACPF) are increasingly important as modern electrical grids require more frequent system analyses. Graph neural networks (GNNs) naturally exploit electrical grid topology, yet the effects of architectural design choices on predictive performance remain poorly understood given differences in graph representations and evaluation settings across studies. We present a unified benchmark of representative GNN architectures for ACPF prediction under a common experimental framework. All models use identical preprocessing and training procedures and comparable parameter budgets, isolating the impact of message-passing mechanisms and inductive biases. We also analyze errors by distance from the slack bus and systematically ablate key design components. We evaluate predictive accuracy and physical consistency on the IEEE 14-bus and IEEE 118-bus systems. Results indicate that anisotropic message-passing architectures offer the most favorable trade-off between predictive accuracy and physical consistency under the studied parameter and topology constraints. Explicit edge features encoding line impedances and global context aggregation are…
PDF arXiv
arXiv stat.MLOct 28
Memorization Capacity of Invariant Convolutional Neural Nets
Sjoerd Dirksen, Patrick Finke, Paul Geuchen
Neural networks are powerful memorizers which are often able to interpolate their training data even when the labels have been randomized. While there exists an extensive literature on the memorization capacity of fully connected networks, in practice more specific architectures with problem-dependent properties are preferred, such as convolutional networks for image classification. In this paper we consider convolutional networks which are invariant with respect to a given arbitrary finite group of orthogonal transformations, which may include translations, reflections, and/or rotations of images and invariances of higher order tensors. We prove upper bounds on the size of an invariant convolutional neural network with threshold activations that is required to interpolate a given dataset with two classes. Our bounds are formulated in terms of a complexity measure that takes the invariant structure into account and is instance-specific, i.e., it is related to the mutual arrangement of the classes in the given dataset and their geometric properties rather than the number of samples. Our proof is constructive: for given input data, instances of an interpolating network can be…
PDF arXiv
arXiv cs.LGOct 28
Discovery of Diverse and Realistic Financial Tail-Risk Using Generative Flow Networks
Adir Serruya, Gilad Katz
Accurate modeling of rare, high-impact financial events is essential for robust risk management. Sampling-based techniques such as Sequential Markov Chain Monte Carlo (SMCMC) generate diverse scenarios but inefficiently explore high-risk regions, while Deep Reinforcement Learning (DRL) converges on narrow sets of scenarios, limiting diversity. We present GRID, which uses Generative Flow Networks (GFlowNets) -- samplers that draw objects in proportion to a reward -- to systematically generate diverse and realistic financial tail-risk scenarios. Building on continuous GFlowNets, GRID introduces a flexible mixture-policy architecture with variable-specific distribution families for high-dimensional, sequential macro-financial data, together with plausibility constraints that keep generated trajectories realistic. A central practical advantage is robustness to reward-proxy imperfection: because GFlowNets sample proportionally rather than maximize, they remain broadly exploratory even when the oracle's out-of-sample accuracy is low -- an unavoidable reality in financial forecasting. On three real-world crises, GRID improves scenario diversity, realism, and tail-risk coverage over RL…
PDF arXiv
arXiv cs.LGOct 28
ExplainCC: Explaining Correlated Clusters by Node Labels
Nadeesha Shyami Kumari Herath Mudiyanselage, Florian Adriaens, Nikolaj Tatti
An increasingly important requirement for clustering algorithms is that decisions and outcomes need to be clearly understandable for human users. We address this requirement for the Correlation Clustering (CC) problem, in case the objects to be clustered are accompanied by labels. In CC one seeks to partition a set of objects into clusters based on pairwise similarity and dissimilarity information. Our proposed model only considers clusters that can be explained by a label tree, which is essentially a decision tree that partitions the objects according to labels. The goal is to find an explainable partition of the data that minimizes the CC objective function. We formalize this problem from a theoretical viewpoint, and prove several hardness of approximation results, also when constraining the size of the tree. Our proposed greedy method finds locally optimal splits, and works recursively in a top-down approach. To efficiently compute the best split, we carefully maintain certain counters. An experimental evaluation demonstrates its effectiveness. The proposed algorithm processes graphs with over million edges in a few minutes, can find the ground truth in synthetic data, and is…
PDF arXiv
arXiv cs.LGOct 28
Logical Guidance of Stochastic Interpolants
Gerrit Gerhartz, Francesco Alesiani, Jonathan Warrell, Henrik Christiansen, Matheus Ferraz +1
Guidance and composition of generative models have applications in visual design, chemistry, and biology. Logical guidance composes conditional scores exactly for diffusion models. However, Flow Matching (FM) and Stochastic Interpolants (SI) learn velocity fields. We show that negation and exclusive disjunction admit exact conditional velocity composition on any interpolant, whereas conjunction, and therefore any formula containing one, is exact on Gaussian paths but in general incurs a drift-interaction error, so composed velocities need not sample the logical target. LOGSI solves this by using score-based re-equilibration as our principled mechanism and falling back to the Gaussian-path velocity rule as an approximate transport predictor. In non-Gaussian bridge problems, generation alternates transport steps with score re-equilibration steps. On image trans fer (EMNIST to CMNIST, CelebA) and QM9 source-to-target molecular editing with logical attribute constraints, LOGSI improves rule conformity at the same network-evaluation budget over composed-velocity baselines, while also improving fidelity on the image tasks. More broadly, LOGSI applies wherever stochastic interpolants…
PDF arXiv
arXiv cs.LGOct 28
CFKS: Continuum-Feature Kernel PDE Solvers
Vikas Dwivedi, Balaji Srinivasan, Monica Sigovan, Bruno Sixou
Neural-network-based PDE solvers require architectural choices, particularly the number of layers and neurons per layer. Fixing a single Gaussian radial basis function (RBF) feature layer and taking the number of features to the continuum limit removes the need to prescribe a finite feature count or select individual feature centers, thereby reducing representation arbitrariness. Motivated by this principle, we introduce Continuum-Feature Kernel Solver (CFKS), a deterministic framework for PDEs obtained by analytically marginalizing a bounded continuum of Gaussian RBF features. CFKS works directly with the resulting continuum-feature mean kernel and its operator-applied counterparts, parameterized by prescribed Gaussian widths and bounded center distributions. For linear PDEs, CFKS leads to symmetric operator-kernel collocation systems with null-space enforcement of physical constraints. Nonlinear PDEs are addressed through linearized initialization and nonlinear least-squares residual minimization using analytical kernel derivatives and Jacobians. Residual-guided collocation enrichment and sequential space--time-slab marching provide additional resolution strategies, while…
PDF arXiv

Activity · 12 weeks

Fields · AI · updated 26m ago

Neural operators

Found by the topic model: neural operators, graph neural, flow matching.

184papers this week−21.7% vs last week

All papers · 1.8k With code · 11

Newest Most relevant

1.1k papers this month

Open problems in Neural operators

Stable and efficient online reinforcement learning for diffusion models3 papers raise it · 3 address it
Current methods are either inefficient due to poor credit assignment or unstable without careful tuning, limiting practical deployment.
- DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Modelsraises · addresses
- Routing Policy Optimization for Online Diffusion Reinforcement Learningraises · addresses
- MEND: RL For Flow Models via Proximal Velocity Matchingraises · addresses
Scalable minibatch optimal transport for flow matching1 paper raises it · 1 addresses it
Small minibatches limit benefits of optimal transport, while large minibatches are computationally prohibitive, and theoretical gaps remain in gradient and integration stability.
- Scaling Online Optimal Transport Minibatches for Flow Matchingraises · addresses
Benchmarking System One models on graph tasks1 paper raises it · 1 addresses it
Capabilities of fast, choice-based models on graph-related tasks are unclear, and existing evaluations lack standardized, diagnostic benchmarks.
- GraphDecisionBench: Benchmarking System One Models on Graph Tasksraises · addresses
Efficient edge sparsification for graph neural networks1 paper raises it · 1 addresses it
Reusing a fixed sparse graph locks training to a fixed topology, while dynamic sparsification is computationally expensive.
- EDiS: Edge Disjoint Subgraph Sparsification Framework for Graph Neural Networksraises · addresses

Named by an AI from these papers' abstracts.