A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper proposes a robust deep graph-based classifier for noisy labels.
DeepSphere improves spherical CNNs by balancing efficiency and rotation equivariance.
OCmst detects anomalies using CNN features and MSTs.
PETNet improves AD diagnosis using graph-based CNN on PET images.
We propose an extension of Convolutional Neural Networks (CNNs) to graph-structured data, including strided convolutions and data augmentation on graphs. Our method matches the accuracy of state-of-the-art CNNs when applied on images, without any prior about their 2D regular structure. On fMRI data, we obtain a signifi…
We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform. Different from graph Fourier transform, graph wavelet transform can be obtained …
In this paper, we propose a new experimental protocol and use it to benchmark the data efficiency --- performance as a function of training set size --- of two deep learning algorithms, convolutional neural networks (CNNs) and hierarchical information-preserving graph-based slow feature analysis (HiGSFA), for tasks in …
Using predictive models to identify patterns that can act as biomarkers for different neuropathoglogical conditions is becoming highly prevalent. In this paper, we consider the problem of Autism Spectrum Disorder (ASD) classification where previous work has shown that it can be beneficial to incorporate a wide variety …
Spherical data is found in many applications. By modeling the discretized sphere as a graph, we can accommodate non-uniformly distributed, partial, and changing samplings. Moreover, graph convolutions are computationally more efficient than spherical convolutions. As equivariance is desired to exploit rotational symmet…
Interactive news recommendation has been launched and attracted much attention recently. In this scenario, user's behavior evolves from single click behavior to multiple behaviors including like, comment, share etc. However, most of the existing methods still use single click behavior as the unique criterion of judging…
Predicts user age and gender on Tumblr using rich content.
GraphCNNpred predicts stock market indices using deep learning.
UAG defends GNNs against adversarial attacks by quantifying and explaining uncertainties.
Recent advances in representation learning on graphs, mainly leveraging graph convolutional networks, have brought a substantial improvement on many graph-based benchmark tasks. While novel approaches to learning node embeddings are highly suitable for node classification and link prediction, their application to graph…
Graph Convolutional Networks (GCNs) have recently become the primary choice for learning from graph-structured data, superseding hash fingerprints in representing chemical compounds. However, GCNs lack the ability to take into account the ordering of node neighbors, even when there is a geometric interpretation of the …
New algorithm improves graph-based active learning by identifying unexplored regions.
Bayesian analysis shows unlabeled data improve graph-based semi-supervised learning.
Convolution Neural Network (CNN) has gained tremendous success in computer vision tasks with its outstanding ability to capture the local latent features. Recently, there has been an increasing interest in extending convolution operations to the non-Euclidean geometry. Although various types of convolution operations h…
Knowledge graph based simple question answering (KBSQA) is a major area of research within question answering. Although only dealing with simple questions, i.e., questions that can be answered through a single knowledge base (KB) fact, this task is neither simple nor close to being solved. Targeting on the two main ste…
End-to-end graph-based SSL learns all graph factors dynamically.
Graph-based methods for anomaly detection and semi-supervised learning.
Improves graph-based active learning for non-Gaussian models.
Galerkin method outperforms graph-based methods in spectral decompositions.
Survey of methods to incorporate external knowledge into stock price prediction.
This paper introduces a novel graph signal processing framework for building graph-based models from classes of filtered signals. In our framework, graph-based modeling is formulated as a graph system identification problem, where the goal is to learn a weighted graph (a graph Laplacian matrix) and a graph-based filter…
Graph-based weather prediction adapted for local models.
Paper proposes a hybrid loss function for graph self-supervised learning.
Active graph-based semi-supervised learning (AG-SSL) aims to select a small set of labeled examples and utilize their graph-based relation to other unlabeled examples to aid in machine learning tasks. It is also closely related to the sampling theory in graph signal processing. In this paper, we revisit the original fo…
In a number of disciplines, the data (e.g., graphs, manifolds) to be analyzed are non-Euclidean in nature. Geometric deep learning corresponds to techniques that generalize deep neural network models to such non-Euclidean spaces. Several recent papers have shown how convolutional neural networks (CNNs) can be extended …
The paper extends graph-based semi-supervised learning to infinite-dimensional Wasserstein space.
Shapley Flow interprets model predictions using a graph-based approach to feature importance.
Improves document summarization by combining word embeddings and n-grams.
This paper detects function-level obfuscation in binary code using graph-based methods.
GATES improves neural architecture search by modeling operations as information transformation.
Improved graph-based semi-supervised learning with model change active learning.
Proposes a new algorithm for graph-based semi-parametric contextual bandits.
Poisson learning improves graph-based semi-supervised learning at very low label rates.
Proposes CI-GMVC to improve graph-based multi-view clustering performance.
Graph-based rehearsal improves continual learning performance.
Paper proposes graph-based separable transforms for video coding.
Recommender system is an important component of many web services to help users locate items that match their interests. Several studies showed that recommender systems are vulnerable to poisoning attacks, in which an attacker injects fake data to a given system such that the system makes recommendations as the attacke…
We consider a family of problems that are concerned about making predictions for the majority of unlabeled, graph-structured data samples based on a small proportion of labeled samples. Relational information among the data samples, often encoded in the graph/network structure, is shown to be helpful for these semi-sup…
A scalable graph-based SSL method for large-scale data with few labels.
Graph-based Bayesian SSL uses graph theory to propagate labels from a few to many unlabeled features.
Knowledge distillation (KD) is a technique to derive optimal performance from a small student network (SN) by distilling knowledge of a large teacher network (TN) and transferring the distilled knowledge to the small SN. Since a role of convolutional neural network (CNN) in KD is to embed a dataset so as to perform a g…
GrADE uses graph neural networks and Neural ODE for solving time-dependent nonlinear PDEs efficiently.
New approximative kernels improve PDE-G-CNNs for geometric deep learning.
In recent years, deep learning poses a deep technical revolution in almost every field and attracts great attentions from industry and academia. Especially, the convolutional neural network (CNN), one representative model of deep learning, achieves great successes in computer vision and natural language processing. How…