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Found 17 papers, 7 papers with code
Hierarchical clustering with maximum density paths and mixture models
Hierarchical clustering is an effective and interpretable technique for analyzing structure in data, offering a nuanced understanding by revealing insights at multiple scales and resolutions.
DISCO: Internal Evaluation of Density-Based Clustering
In this paper, we propose DISCO, a Density-based Internal Score for Clustering Outcomes, which is the first CVI that also evaluates the quality of noise labels.
MNIST-Nd: a set of naturalistic datasets to benchmark clustering across dimensions
Driven by advances in recording technology, large-scale high-dimensional datasets have emerged across many scientific disciplines.
Distinguished In Uniform: Self Attention Vs. Virtual Nodes
We first clarify that this form of universality is not unique to GTs: Using the same positional encodings, also pure MPGNNs and even 2-layer MLPs are non-uniform universal approximators.
Boosting, Voting Classifiers and Randomized Sample Compression Schemes
At the center of this paradigm lies the concept of building the strong learner as a voting classifier, which outputs a weighted majority vote of the weak learners.
Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark
The recent Long-Range Graph Benchmark (LRGB, Dwivedi et al. 2022) introduced a set of graph learning tasks strongly dependent on long-range interaction between vertices.
Ranked #2 on
Link Prediction
on PCQM-Contact
(MRR-ext-filtered metric)
AdaBoost is not an Optimal Weak to Strong Learner
AdaBoost is a classic boosting algorithm for combining multiple inaccurate classifiers produced by a weak learner, to produce a strong learner with arbitrarily high accuracy when given enough training data.
Optimal Weak to Strong Learning
The classic algorithm AdaBoost allows to convert a weak learner, that is an algorithm that produces a hypothesis which is slightly better than chance, into a strong learner, achieving arbitrarily high accuracy when given enough training data.
Graph Machine Learning for Design of High-Octane Fuels
We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space.
On the Parameterized Complexity of Learning First-Order Logic
We analyse the complexity of learning first-order queries in a model-theoretic framework for supervised learning introduced by (Grohe and Tur\'an, TOCS 2004).
Logic in Computer Science
Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing
As the theoretical basis for our approach, we prove a theorem stating that the expressiveness of CRaWl is incomparable with that of the Weisfeiler Leman algorithm and hence with graph neural networks.
Ranked #1 on
Graph Classification
on REDDIT-B
The Effects of Randomness on the Stability of Node Embeddings
We systematically evaluate the (in-)stability of state-of-the-art node embedding algorithms due to randomness, i. e., the random variation of their outcomes given identical algorithms and graphs.
Learning definable hypotheses on trees
We study the problem of learning properties of nodes in tree structures.
Graph Neural Networks for Maximum Constraint Satisfaction
Many combinatorial optimization problems can be phrased in the language of constraint satisfaction problems.
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
We show that GNNs have the same expressiveness as the $1$-WL in terms of distinguishing non-isomorphic (sub-)graphs.
Ranked #5 on
Graph Classification
on NCI1
Learning MSO-definable hypotheses on string
We study the classification problems over string data for hypotheses specified by formulas of monadic second-order logic MSO.
Learning first-order definable concepts over structures of small degree
We consider a declarative framework for machine learning where concepts and hypotheses are defined by formulas of a logic over some background structure.
