arxiv_cl
Abstract: Abstract: We propose a Graph Neural Network (GNN)-based approach for Handwritten
Mathematical Expression (HME) recognition by modeling HMEs as graphs, where
nodes represent symbols and edges capture spatial dependencies. A deep BLSTM
network is used ...
#Graph Neural Networks#Pattern Recognition#Document Analysis#Symbol Recognition#Structural Analysis
arxiv_cl
Abstract: Abstract: Large language models (LLMs) are increasingly used to simulate humans, with
applications ranging from survey prediction to decision-making. However, are
LLMs strictly necessary, or can smaller, domain-grounded models suffice? We
identify a ...
#Graph Neural Networks#Human Behavior Modeling#Agent-Based Simulation#LLM Alternatives#Decision Making
arxiv_ml
Abstract: Abstract: Identifying evolutionary correspondences between cell types across species is
a fundamental challenge in comparative genomics and evolutionary biology.
Existing approaches often rely on either reference-based matching, which
imposes asymmet...
#Computational Biology#Genomics#Evolutionary Biology#Machine Learning#Graph Theory#Optimization
arxiv_ml
Abstract: Abstract: Graph neural networks (GNNs) are powerful tools for analyzing and learning
from graph-structured (GS) data, facilitating a wide range of services.
Deploying such services in privacy-critical cloud environments necessitates the
development o...
#Secure Machine Learning#Graph Analysis#Privacy in Cloud Computing#Cryptographic Techniques#Efficient Inference
arxiv_ml
Abstract: Abstract: Molecular discovery has brought great benefits to the chemical industry.
Various molecule design techniques are developed to identify molecules with
desirable properties. Traditional optimization methods, such as genetic
algorithms, continu...
#Molecular Design#Drug Discovery#Optimization Algorithms#Machine Learning
arxiv_ml
Abstract: Abstract: Solar-flare forecasting has been extensively researched yet remains an open
problem. In this paper, we investigate the contributions of elastic distance
measures for detecting patterns in the solar-flare dataset, SWAN-SF. We employ
a simple...
#Solar Physics#Time Series Analysis#Machine Learning#Clustering Algorithms#Data Mining
arxiv_ml
Abstract: Abstract: Pulsar Timing Arrays provide a powerful framework to measure low-frequency
gravitational waves, but accuracy and robustness of the results are challenged
by complex noise processes that must be accurately modeled. Standard PTA
analyses assi...
#Astrophysics#Gravitational Wave Detection#Statistical Modeling#Bayesian Inference#Signal Processing
arxiv_ml
Abstract: Abstract: The number of graph neural network (GNN) architectures has increased rapidly
due to the growing adoption of graph analysis. Although we use GNNs in wide
application scenarios, it is a laborious task to design/select optimal GNN
architecture...
#Graph Neural Networks#Neural Architecture Search (NAS)#Machine Learning Interpretability#Graph Analysis#Algorithm Design
arxiv_ml
Abstract: Abstract: We study the problem of learning a $n$-variables $k$-CNF formula $\Phi$ from
its i.i.d. uniform random solutions, which is equivalent to learning a Boolean
Markov random field (MRF) with $k$-wise hard constraints. Revisiting Valiant's
algor...
#Computational Learning Theory#Theoretical Computer Science#Satisfiability (SAT)#Machine Learning Theory#Boolean Functions
arxiv_ml
Abstract: Abstract: Estimating causal effects from real-world relational data can be challenging
when the underlying causal model and potential confounders are unknown. While
several causal discovery algorithms exist for learning causal models with
latent conf...
#Causal Inference#Causal Discovery#Graph Theory#Machine Learning#Relational Data Analysis
arxiv_ml
Abstract: Abstract: We develop a Gaussian process framework for learning interaction kernels in
multi-species interacting particle systems from trajectory data. Such systems
provide a canonical setting for multiscale modeling, where simple microscopic
interact...
#Statistical Physics#Machine Learning#Bayesian Methods#Dynamical Systems#Scientific Computing
arxiv_ml
Abstract: Abstract: Machine learning force fields have emerged as promising tools for molecular
dynamics (MD) simulations, potentially offering quantum-mechanical accuracy
with the efficiency of classical MD. Inspired by foundational large language
models, rec...
#Computational Chemistry#Materials Science#Molecular Dynamics#Machine Learning for Science#Physics Simulation
arxiv_ml
Abstract: Abstract: Traditional anomalous traffic detection methods are based on single-view
analysis, which has obvious limitations in dealing with complex attacks and
encrypted communications. In this regard, we propose a Multi-view Feature
Fusion (MuFF) met...
#Network Security#Machine Learning#Data Analysis#Cybersecurity#Anomaly Detection
arxiv_ml
Abstract: Abstract: Associative memory, traditionally modeled by Hopfield networks, enables the
retrieval of previously stored patterns from partial or noisy cues. Yet, the
local computational principles which are required to enable this function
remain incomp...
#Computational Neuroscience#Machine Learning Theory#Information Theory#Memory Systems#Neural Networks
arxiv_ml
Abstract: Abstract: Single-cell RNA sequencing (scRNA-seq) enables the study of cellular
diversity at single cell level. It provides a global view of cell-type
specification during the onset of biological mechanisms such as developmental
processes and human or...
#Single-Cell Genomics#Bioinformatics#Machine Learning for Biology#Dimensionality Reduction#Cell Type Classification
arxiv_ml
Abstract: Abstract: We prove that there exist uniform $(+,\times,/)$-circuits of size $O(n^3)$ to
compute the basis generating polynomial of regular matroids on $n$ elements. By
tropicalization, this implies that there exist uniform $(\max,+,-)$-circuits
and R...
#Combinatorial Optimization#Theoretical Computer Science#Graph Theory#Machine Learning Theory#Linear Programming
arxiv_ml
Abstract: Abstract: Gene regulatory network inference (GRNI) aims to discover how genes causally
regulate each other from gene expression data. It is well-known that
statistical dependencies in observed data do not necessarily imply causation,
as spurious depe...
#Causal Inference#Systems Biology#Bioinformatics#Gene Regulatory Networks#Statistical Modeling
arxiv_ml
Abstract: Abstract: Federated learning offers a privacy-preserving framework for recommendation
systems by enabling local data processing; however, data localization
introduces substantial obstacles. Traditional federated recommendation
approaches treat each u...
#Federated Learning#Recommender Systems#Graph Neural Networks#Privacy-Preserving Machine Learning#Natural Language Processing
arxiv_ml
Abstract: Abstract: Noncovalent interactions--vdW dispersion, hydrogen/halogen bonding,
ion-$\pi$, and $\pi$-stacking--govern structure, dynamics, and emergent
phenomena in materials and molecular systems, yet accurately learning them
alongside covalent forces...
#Molecular Dynamics#Machine Learning for Chemistry#Force Fields#Computational Chemistry#Materials Science
arxiv_ml
Abstract: Abstract: Message passing neural networks (MPNNs) operate on graphs by exchanging
information between neigbouring nodes. MPNNs have been successfully applied to
various node-, edge-, and graph-level tasks in areas like molecular science,
computer vis...
#Graph Neural Networks#Machine Learning#Network Analysis#Representation Learning#Algorithm Design
arxiv_ai
Abstract: Abstract: Vector symbolic architectures (VSAs) are a family of information
representation techniques which enable composition, i.e., creating complex
information structures from atomic vectors via binding and superposition, and
have recently found wi...
#Information Representation#Coding Theory#Neurosymbolic AI#Artificial Intelligence#Signal Processing
arxiv_ml
Abstract: Abstract: Drug synergy prediction is a critical task in the development of effective
combination therapies for complex diseases, including cancer. Although existing
methods have shown promising results, they often operate as black-box
predictors that...
#Drug Discovery#Causal Inference#Interpretable Machine Learning#Molecular Representation#Predictive Modeling
arxiv_ml
Abstract: Abstract: Healthcare artificial intelligence systems routinely fail when deployed
across institutions, with documented performance drops and perpetuation of
discriminatory patterns embedded in historical data. This brittleness stems, in
part, from le...
#Causal AI#Graph Neural Networks#Healthcare Applications#Fairness in AI#Robustness in AI
arxiv_ml
Abstract: Abstract: Probabilistic relaxations of graph cuts offer a differentiable alternative to
spectral clustering, enabling end-to-end and online learning without
eigendecompositions, yet prior work centered on RatioCut and lacked general
guarantees and pr...
#Graph Theory#Clustering Algorithms#Differentiable Optimization#Machine Learning Theory#Representation Learning
arxiv_ml
Abstract: Abstract: Predicting crimes in urban environments is crucial for public safety, yet
existing prediction methods often struggle to align the knowledge across
diverse cities that vary dramatically in data availability of specific crime
types. We propos...
#Predictive Modeling#Urban Analytics#Knowledge Representation#Hypernetworks#Spatial-Temporal Data
arxiv_ml
Abstract: Abstract: Knowledge graph embedding (KGE) has become a fundamental technique for
representation learning on multi-relational data. Many seminal models, such as
TransE, operate in an unbounded Euclidean space, which presents inherent
limitations in mo...
#Knowledge Graphs#Representation Learning#Graph Embeddings#Geometric Deep Learning#Information Retrieval
arxiv_ml
Abstract: Abstract: Covalent organic frameworks (COFs) are promising adsorbents for gas
adsorption and separation, while identifying the optimal structures among their
vast design space requires efficient high-throughput screening. Conventional
machine-learnin...
#Predictive Modeling for Materials Science#Machine Learning for COF Design#Multi-modal Feature Learning#High-Throughput Screening of Adsorbents
arxiv_ml
Abstract: Abstract: Graph neural networks have shown remarkable success in exploiting the spatial
and temporal patterns on dynamic graphs. However, existing GNNs exhibit poor
generalization ability under distribution shifts, which is inevitable in
dynamic scen...
#Graph Neural Networks#Out-of-Distribution Generalization#Dynamic Systems#Causal Inference#Environment Modeling
arxiv_ml
Abstract: Abstract: We investigate theoretical guarantees for the false-negative rate (FNR) --
the fraction of true causal edges whose orientation is not recovered, under
single-variable random interventions and an $\epsilon$-interventional
faithfulness assump...
#Causal Inference#Graph Theory#Machine Learning Theory#Statistical Inference#Network Science
arxiv_ml
Abstract: Abstract: Predicting interspecies interactions is a key challenge in microbial ecology,
as these interactions are critical to determining the structure and activity of
microbial communities. In this work, we used data on monoculture growth
capabiliti...
#Graph Neural Networks#Microbial Ecology#Systems Biology#Predictive Modeling#Network Analysis