Papers Tree Decomposition
“Tree Decomposition” 태그가 달린 논문 34편 · 필터 해제
HCAF-DTA: drug-target binding affinity prediction with cross-attention fused hypergraph neural networks
Accurate prediction of the binding affinity between drugs and target proteins is a core task in computer-aided drug design. Existing deep learning methods tend to ignore the information of internal sub-structural feature…
Drug DesignPredictionTree DecompositionGraph Inference with Effective Resistance Queries
The goal of graph inference is to design algorithms for learning properties of a hidden graph using queries to an oracle that returns information about the graph. Graph reconstruction, verification, and property testing …
Graph ReconstructionTree DecompositionA Hybrid Technique for Plant Disease Identification and Localisation in Real-time
Over the past decade, several image-processing methods and algorithms have been proposed for identifying plant diseases based on visual data. DNN (Deep Neural Networks) have recently become popular for this task. Both tr…
Tree DecompositionBounds on the Treewidth of Level-k Rooted Phylogenetic Networks
Phylogenetic networks are directed acyclic graphs that depict the genomic evolution of related taxa. Reticulation nodes in such networks (nodes with more than one parent) represent reticulate evolutionary events, such as…
Tree DecompositionImproving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning
Answering complex queries over incomplete knowledge graphs (KGs) is a challenging job. Most previous works have focused on learning entity/relation embeddings and simulating first-order logic operators with various neura…
Knowledge GraphsLogical ReasoningTree DecompositionWorld KnowledgeLifting uniform learners via distributional decomposition
We show how any PAC learning algorithm that works under the uniform distribution can be transformed, in a blackbox fashion, into one that works under an arbitrary and unknown distribution $\mathcal{D}$. The efficiency of…
LEMMAPAC learningTree DecompositionCalibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs
We propose a new approach, the calibrated nonparametric scan statistic (CNSS), for more accurate detection of anomalous patterns in large-scale, real-world graphs. Scan statistics identify connected subgraphs that are in…
Tree DecompositionPractical Fixed-Parameter Algorithms for Defending Active Directory Style Attack Graphs
Active Directory is the default security management system for Windows domain networks. We study the shortest path edge interdiction problem for defending Active Directory style attack graphs. The problem is formulated a…
ManagementTree DecompositionLatent Tree Decomposition Parsers for AMR-to-Text Generation
Graph encoders in AMR-to-text generation models often rely on neighborhood convolutions or global vertex attention. While these approaches apply to general graphs, AMRs may be amenable to encoders that target their tree-…
AMR-to-Text GenerationClusteringMolecular Property PredictionProperty Prediction+2Tree Decomposed Graph Neural Network
Graph Neural Networks (GNNs) have achieved significant success in learning better representations by performing feature propagation and transformation iteratively to leverage neighborhood information. Nevertheless, itera…
Graph Neural NetworkNode ClassificationTree DecompositionComputing the Newton-step faster than Hessian accumulation
Computing the Newton-step of a generic function with $N$ decision variables takes $O(N^3)$ flops. In this paper, we show that given the computational graph of the function, this bound can be reduced to $O(m\tau^3)$, wher…
Tree DecompositionTD-GEN: Graph Generation With Tree Decomposition
We propose TD-GEN, a graph generation framework based on tree decomposition, and introduce a reduced upper bound on the maximum number of decisions needed for graph generation. The framework includes a permutation invari…
Graph GenerationTree DecompositionA Framework For Differentiable Discovery Of Graph Algorithms
Recently there is a surge of interests in using graph neural networks (GNNs) to learn algorithms. However, these works focus more on imitating existing algorithms, and are limited in two important aspects: the search spa…
Tree DecompositionLearning Elimination Ordering for Tree Decomposition Problem
We propose a Reinforcement Learning-based approach to approximately solve the Tree Decomposition problem. Recently, it was shown that learned heuristics could successfully solve combinatorial problems. We establish tha…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Tree DecompositionA New Approach for Texture based Script Identification At Block Level using Quad Tree Decomposition
A considerable amount of success has been achieved in developing monolingual OCR systems for Indic scripts. But in a country like India, where multi-script scenario is prevalent, identifying scripts beforehand becomes ob…
Optical Character Recognition (OCR)Tree DecompositionDPMC: Weighted Model Counting by Dynamic Programming on Project-Join Trees
We propose a unifying dynamic-programming framework to compute exact literal-weighted model counts of formulas in conjunctive normal form. At the center of our framework are project-join trees, which specify efficient pr…
Tree DecompositionConstraint-based Causal Structure Learning with Consistent Separating Sets
We consider constraint-based methods for causal structure learning, such as the PC algorithm or any PC-derived algorithms whose first step consists in pruning a complete graph to obtain an undirected graph skeleton, which…
Tree DecompositionMixtape: Breaking the Softmax Bottleneck Efficiently
The softmax bottleneck has been shown to limit the expressiveness of neural language models. Mixture of Softmaxes (MoS) is an effective approach to address such a theoretical limitation, but are expensive compared to sof…
Language ModelingLanguage ModellingMachine TranslationTranslation+1Graph Convolutional Policy for Solving Tree Decomposition via Reinforcement Learning Heuristics
We propose a Reinforcement Learning based approach to approximately solve the Tree Decomposition (TD) problem. TD is a combinatorial problem, which is central to the analysis of graph minor structure and computational co…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Tree DecompositionOrdered Tree Decomposition for HRG Rule Extraction
We present algorithms for extracting Hyperedge Replacement Grammar (HRG) rules from a graph along with a vertex order. Our algorithms are based on finding a tree decomposition of smallest width, relative to the vertex or…
Tree Decomposition