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Found 31 papers, 16 papers with code
Approximating Metric Magnitude of Point Sets
We show that it can be cast as a convex optimization problem, but not as a submodular optimization.
Topological Generalization Bounds for Discrete-Time Stochastic Optimization Algorithms
Our experimental results demonstrate that our new complexity measures correlate highly with generalization error in industry-standards architectures such as transformers and deep graph networks.
Metric Space Magnitude for Evaluating the Diversity of Latent Representations
We develop a family of magnitude-based measures of the intrinsic diversity of latent representations, formalising a novel notion of dissimilarity between magnitude functions of finite metric spaces.
Inference and Interference: The Role of Clipping, Pruning and Loss Landscapes in Differentially Private Stochastic Gradient Descent
Differentially private stochastic gradient descent (DP-SGD) is known to have poorer training and test performance on large neural networks, compared to ordinary stochastic gradient descent (SGD).
Accelerated Shapley Value Approximation for Data Evaluation
In pre-trained networks the approach is found to bring more efficiency in terms of accurate evaluation using small subsets.
Machine learning and Topological data analysis identify unique features of human papillae in 3D scans
Models trained on these features with small volumes of data samples predict the type of papillae with an accuracy of 85%.
Towards Understanding the Interplay of Generative Artificial Intelligence and the Internet
Our results show that the quality and diversity of the generated images can degrade over time suggesting that incorporating AI-created data can have undesired effects on future versions of generative models.
Metric Space Magnitude and Generalisation in Neural Networks
Deep learning models have seen significant successes in numerous applications, but their inner workings remain elusive.
Combining Generative Artificial Intelligence (AI) and the Internet: Heading towards Evolution or Degradation?
Therefore, future versions of generative AI tools will be trained with Internet data that is a mix of original and AI-generated data.
Differentially Private Shapley Values for Data Evaluation
The Shapley value has been proposed as a solution to many applications in machine learning, including for equitable valuation of data.
On Cyclic Solutions to the Min-Max Latency Multi-Robot Patrolling Problem
For the Euclidean version of the problem, for instance, combining our results with known results on Euclidean TSP, yields a PTAS for approximating an optimal cyclic solution, and it yields a $(2(1-1/k)+\varepsilon)$-approximation of the optimal unrestricted solution.
Continual and Sliding Window Release for Private Empirical Risk Minimization
One version of the algorithm uses the entire data history to improve the model for the recent window.
The Shapley Value in Machine Learning
Over the last few years, the Shapley value, a solution concept from cooperative game theory, has found numerous applications in machine learning.
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing.
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks
Recurrent graph convolutional neural networks are highly effective machine learning techniques for spatiotemporal signal processing.
Euler Characteristic Surfaces
While topological data analysis of higher-dimensional parameter spaces using stronger invariants such as homology continues to be the subject of intense research, Euler characteristic is more manageable theoretically and computationally, and this analysis can be seen as an important intermediary step in multi-parameter topological data analysis.
Topological Data Analysis
Algebraic Topology
Computational Geometry
Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings
Proximity preserving and structural role-based node embeddings have become a prime workhorse of applied graph mining.
The Shapley Value of Classifiers in Ensemble Games
We argue that the Shapley value of models in these games is an effective decision metric for choosing a high performing subset of models from the ensemble.
Heterogeneous Interventions Reduce the Spread of COVID-19 in Simulations on Real Mobility Data
Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus.
Social and Information Networks Physics and Society
Stability Enhanced Privacy and Applications in Private Stochastic Gradient Descent
Private machine learning involves addition of noise while training, resulting in lower accuracy.
Little Ball of Fur: A Python Library for Graph Sampling
In this paper, we describe Little Ball of Fur a Python library that includes more than twenty graph sampling algorithms.
Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models
In this paper, we propose a flexible notion of characteristic functions defined on graph vertices to describe the distribution of vertex features at multiple scales.
Approximation Algorithms for Multi-Robot Patrol-Scheduling with Min-Max Latency
The problem is NP-hard, as it has the traveling salesman problem as a special case (when $k=1$ and all sites have the same weight).
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs
We present Karate Club a Python framework combining more than 30 state-of-the-art graph mining algorithms which can solve unsupervised machine learning tasks.
Fast Sequence-Based Embedding with Diffusion Graphs
A graph embedding is a representation of graph vertices in a low-dimensional space, which approximately preserves properties such as distances between nodes.
Multi-scale Attributed Node Embedding
We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram.
Fast Sequence Based Embedding with Diffusion Graphs
A graph embedding is a representation of the vertices of a graph in a low dimensional space, which approximately preserves proper-ties such as distances between nodes.
GEMSEC: Graph Embedding with Self Clustering
In this paper we propose GEMSEC a graph embedding algorithm which learns a clustering of the nodes simultaneously with computing their features.
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Social and Information Networks
Distributed Submodular Maximization
Such problems can often be reduced to maximizing a submodular set function subject to various constraints.
Distributed Submodular Maximization: Identifying Representative Elements in Massive Data
Such problems can often be reduced to maximizing a submodular set function subject to cardinality constraints.
