The study analyzes neural interactions using an Ising model to reveal contributions of pairwise interactions to sparseness and fluctuation.
problem Understanding the contributions of pairwise interactions to sparseness and fluctuation in neural activity.
method Inference methods for a time-dependent Ising model to analyze neural interactions and estimate time-dependent neural interactions with credible intervals.
result Pairwise interactions contribute to increasing sparseness and fluctuation in neural activity.
Automated tests detect interactions in unstructured data.
problem Detecting interactions between latent variables in low-dimensional systems.
method Derive two interaction tests based on pairwise interventions and integrate them into an active learning pipeline.
result Tests can identify more known biological interactions than random search and standard active learning baselines.
PIN models feature interactions using a neural network that mimics decision trees.
problem Modeling feature interactions in tabular data for predictive modeling.
method Tree-like Pairwise Interaction Network (PIN) architecture that captures pairwise feature interactions through a shared feed-forward neural network.
result PIN outperforms traditional and modern neural networks benchmarks in predictive accuracy.
Study shows attention-style models learn pairwise interactions efficiently.
problem Learning pairwise interactions in attention-style models.
method Proved minimax rate of convergence for learning pairwise interactions.
result Minimax rate is M−2β+12β independent of embedding dimension and token number. Deep model learns protein interfaces from high-order interactions.
problem Predicting protein interfaces from amino acid pairs.
method Graph neural networks and convolutional neural networks for 2D dense predictions.
result Our method consistently improves interface prediction performance.
New methods for estimating gradients of expectations using pairwise interactions.
problem Estimating gradients of expectations for complex models.
method Introducing new pairwise stochastic gradient estimators based on the log-derivative trick and reparameterisation.
result New estimators are unbiased and offer variance reduction compared to the log-derivative estimator.
Bayesian method reconstructs hidden higher-order interactions from network data.
problem Lack of explicit higher-order interactions in pairwise network data.
method Bayesian approach based on parsimony, infers higher-order structures when statistically supported.
result Demonstrated applicability to various datasets, synthetic and empirical.
Paper tackles interactive learning with noisy labels and comparisons, showing how comparison oracles can improve learning efficiency.
problem Interactive learning with noisy labels and comparisons.
method Characterizes comparison oracle as threshold function learning problem, presents algorithm with query complexity analysis.
result Lower bounds show almost optimal label and total query complexity.
GAMI-Net improves neural network interpretability while maintaining accuracy.
problem Lack of interpretability in neural network models.
method GAMI-Net is a disentangled feedforward network with multiple additive subnetworks designed for capturing main effects and pairwise interactions, considering sparsity, heredity, and marginal clarity.
result GAMI-Net achieves superior interpretability and competitive prediction accuracy compared to explainable boosting machine and other models.
This study explores how feature graphs enhance GNNs' performance in modeling interactions.
problem Improving GNNs' ability to model feature interactions effectively.
method Investigates feature graphs and their importance in GNNs, using experiments and theoretical support.
result Edges between interacting features are crucial for GNNs, while non-interaction edges can degrade performance.
Bayesian neural networks detect complex interactions with uncertainty.
problem Estimating global pairwise interaction effects with uncertainty.
method Bayesian neural network for modeling interactions, GEH for estimating interaction effects.
result The method empirically outperforms alternatives and detects interpretable interactions.
We present atomistic molecular dynamics simulations of two Polyethylene systems where all entanglements are trapped: a perfect network, and a melt with grafted chain ends. We examine microscopically at what level topological constraints can be considered as a collective entanglement effect, as in tube model theories, o…
A fast algorithm speeds up training of pairwise kernels.
problem Training pairwise kernels efficiently for large datasets.
method Generalized vec trick for Kronecker product kernels.
result Pairwise kernels can be expressed as sums of Kronecker products.
Researchers discover phase transitions in estimating object ranks from pairwise interactions.
problem Estimating the underlying ranks of objects from pairwise comparisons or collaborations.
method Characterized optimal statistical error rates for various signal-to-noise ratios.
result Phase transitions between optimal error rates of polynomial, exponential, zero, and trivial.
Maximum Entropy model captures musical styles without plagiarizing them.
problem Generating music that sounds like a specific style without copying.
method A Maximum Entropy model with pairwise interactions, avoiding Markov interactions.
result Generated melodies capture the style of the original corpus without plagiarism.
Signed pairwise interactions conflate uniqueness, redundancy, and synergy
problem Signed pairwise interactions conflate uniqueness, redundancy, and synergy
method Stochastic Hi-Fi
result Stochastic Hi-Fi recovers structure missed by scalar baselines
Surrogate-based analysis of interactions via local effect smooths
problem Detecting and characterizing feature interactions in machine learning models
method Surrogate-based analysis using generalized additive models
result Empirical validation of effectiveness for pairwise interactions
R-SQAIR adds relational bias to sequential object attention models for better object interactions.
problem Traditional sequential multi-object attention models struggle with relational inferences.
method Proposes R-SQAIR, a relational extension of SQAIR with a parallel pairwise interaction module.
result Demonstrates gains in object relations and combinatorial generalization over sequential mechanisms.
Spin models' simplicity depends on interaction arrangements, not just order.
problem Understanding the simplicity of spin models with various interactions.
method Analyzing the stochastic complexity of spin models with interactions of arbitrary order.
result Models with localized dependencies are simpler than fully connected pairwise models.
RaFM improves FM performance with variable-rank embeddings.
problem Learning pairwise interactions with varying feature frequencies.
method Introduces RaFM with variable-rank embeddings for better performance.
result RaFM achieves better performance on real-world datasets.
Introduces SM-games to analyze machine learning interactions.
problem Lack of understanding and control in n-player games.
method Introduces SM-games with pairwise zero-sum interactions.
result SM-games are amenable to first-order optimization methods.
Study reveals self-attention's role in learning and generalizing interactions.
problem Understanding self-attention's theoretical role in neural architectures.
method Interacting entities analysis, including multi-agent RL and genetic sequences.
result Self-attention efficiently represents, learns, and generalizes pairwise interactions.
iLOCO measures feature interactions without assumptions, providing statistical inference.
problem Lack of methods to statistically infer feature interactions.
method iLOCO metric and LOCO inference for distribution-free, efficient computation.
result First inferential approach to detecting feature interactions.
To date, testing interactions in high dimensions has been a challenging task. Existing methods often have issues with sensitivity to modeling assumptions and heavily asymptotic nominal p-values. To help alleviate these issues, we propose a permutation-based method for testing marginal interactions with a binary respons…
Financial markets are a typical example of complex systems where interactions between constituents lead to many remarkable features. Here, we show that a pairwise maximum entropy model (or auto-logistic model) is able to describe switches between ordered (strongly correlated) and disordered market states. In this frame…
New models for analyzing microbiome data with interactions.
problem Analyzing compositional data with interactions.
method Exponential family models with generalized score matching.
result Effective estimation methods for compositional data with interactions.
Novel ML model predicts solvation free energies from atom interactions.
problem Predicting solvation free energies from atomistic interactions.
method Two encoding functions extract atomic feature vectors, interactions calculated by inner product.
result Outstanding performance and transferability on 6,493 experimental measurements.
This work maps Boltzmann distributions to ARNNs for better physics-based model approximations.
problem Approximating Boltzmann distributions of binary systems.
method Exact mapping of Boltzmann distribution to autoregressive neural network architecture.
result New ARNN architectures derived from physical models show superior performance.
We investigate a generic problem of learning pairwise exponential family graphical models with pairwise sufficient statistics defined by a global mapping function, e.g., Mercer kernels. This subclass of pairwise graphical models allow us to flexibly capture complex interactions among variables beyond pairwise product. …
Kernel trick speeds up Bayesian interaction discovery.
problem Discovering interaction effects in high-dimensional data.
method Gaussian process representation and MCMC over hyper-parameters.
result Significant reduction in runtime and improved error rates.
COBRAS uses super-instances to quickly cluster data with user queries.
problem Clustering data with user-defined pairwise constraints efficiently.
method Top-down construction of super-instances, iterative refinement based on user queries.
result COBRAS produces high-quality clusterings at fast run times.
Study higher-order interactions in networks, proposing link prediction as a new benchmark.
problem Understanding higher-order interactions in complex systems.
method Temporal analysis of 19 datasets, focusing on interactions involving more than two nodes.
result Higher-order interactions are consistent across different systems, with tie strength and edge density influencing their presence.
Paper learns interaction laws from multiple trajectories of heterogeneous systems.
problem Estimating unknown interaction laws from multiple trajectories of heterogeneous systems.
method Nonparametric learning of interaction kernels based on pairwise distances, with convergence guarantees in L2 space. result Estimators converge at optimal min-max rate for 1-dimensional nonparametric regression.
Dynamic Vine Copulas detect and quantify time-varying higher-order interactions in multivariate systems.
problem Time-varying dependence in multivariate systems, including tail behavior, asymmetry, and conditional structure.
method Dynamic Vine Copulas (DVC) framework for estimating and diagnosing non-Gaussian dependence, using fixed-root-order C-vines and smooth parameter trajectories.
result DVC detects and quantifies time-varying higher-order interactions, distinguishing between pairwise and conditional dependence.
DeepSequence model predicts mutation effects better than existing methods.
problem Quantifying complex interactions in biomolecules.
method Latent variable models with nonlinear dependencies.
result DeepSequence predicts mutation effects significantly better than site-independent or pairwise models.
Unified model detects anomalies in heterogeneous categorical events.
problem Detecting anomalies in events with mixed types and large event space.
method APE model that embeds entities into a latent space and models event probability via pairwise interactions.
result Our model outperforms state-of-the-art techniques in detecting anomalies.
Proposes a new method to better understand complex system interactions.
problem Current methods like Granger causality and transfer entropy fail to capture higher-order interactions.
method Introduces a generalized approach to capture multivariate causal interactions.
result The method can distinguish causal roles in synergetic interactions.
CAN approximates explicit feature interactions for CTR prediction.
problem Learning explicit feature interactions from sparse features.
method Co-Action Network approximates explicit pairwise feature interactions without introducing too many additional parameters.
result CAN outperforms state-of-the-art CTR models and the cartesian product method.
Financial markets are a classical example of complex systems as they comprise many interacting stocks. As such, we can obtain a surprisingly good description of their structure by making the rough simplification of binary daily returns. Spin glass models have been applied and gave some valuable results but at the price…
Modeling continuous movement of entities in latent space for interaction timing.
problem Analyzing timing and frequency of instantaneous interactions.
method Latent position model with continuous trajectories.
result Individual trajectories estimated from interaction data.
Graph neural network predicts vehicle interactions and trajectories for autonomous driving.
problem Predicting future motion of vehicles in traffic scenes.
method Graph neural network that jointly predicts interaction modes and 5-second future trajectories.
result Jointly predicting trajectories and interaction modes leads to lower trajectory error.
In this study, we intend to solve a mutual information problem in interacting molecules of any type, such as proteins, nucleic acids, and small molecules. Using machine learning techniques, we accurately predict pairwise interactions, which can be of medical and biological importance. Graphs are are useful in this prob…
Shared workspace improves neural module coordination in deep learning.
problem Pairwise interactions in deep learning models lack global coordination.
method Introduce a shared global workspace with bandwidth limitations among neural modules.
result Capacity limitations encourage specialization and synchronization.
Bayesian method selects interacting regions in Markov models.
problem Estimating interacting regions in Markov Random Fields.
method Reversible Jump Monte Carlo Markov Chain algorithm with pseudoposteriors.
result Proposed method accurately selects interacting regions in simulations and real data.
A new model for sparse networks improves consistency of score estimators.
problem Statistical inference in large sparse networks.
method General pairwise comparison model with flexible parametrization.
result Maximum likelihood estimator is uniformly consistent under sparse conditions.
Generates random persistence diagrams for data analysis.
problem Generating random persistence diagrams for data analysis.
method Based on pairwise interacting point processes and RJ-MCMC algorithm.
result Demonstrates the efficacy and utility of RPDG in materials science.
Improved score matching for non-negative data models.
problem Estimating parameters of non-negative probability density functions.
method Generalized score matching method for non-negative data.
result Improved estimation efficiency and theoretical guarantees.
Study infers evolutionary interactions from protein sequences using regularization methods.
problem Inferring evolutionary interactions from protein sequences.
method Regularization methods, including L2 for fields and group L1 for couplings, with parameter tuning. result Effective regularization parameters for sparse couplings improve accuracy.