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.
Bayesian approach improves network lasso for multi-task learning.
VB approach for dynamic network models improves efficiency and accuracy.
A new method prunes deep networks in one go without specifying pruning levels.
Paper studies estimating network properties with missing data using SRL and GNN.
This paper proposes a novel approach to train deep neural networks by unlocking the layer-wise dependency of backpropagation training. The approach employs additional modules called local critic networks besides the main network model to be trained, which are used to obtain error gradients without complete feedforward …
The vast majority of network datasets contains errors and omissions, although this is rarely incorporated in traditional network analysis. Recently, an increasing effort has been made to fill this methodological gap by developing network reconstruction approaches based on Bayesian inference. These approaches, however, …
Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usu…
Simple iterative method reduces deep network size significantly.
A new probabilistic model detects communities in networks using both structure and node features.
There are many real-world knowledge based networked systems with multi-type interacting entities that can be regarded as heterogeneous networks including human connections and biological evolutions. One of the main issues in such networks is to predict information diffusion such as shape, growth and size of social even…
A Bayesian network is a widely used probabilistic graphical model with applications in knowledge discovery and prediction. Learning a Bayesian network (BN) from data can be cast as an optimization problem using the well-known score-and-search approach. However, selecting a single model (i.e., the best scoring BN) can b…
Networks are ubiquitous in biology and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially known network by integrating various measurements. Two main supervised frameworks have been proposed: the local appro…
We present MorphNet, an approach to automate the design of neural network structures. MorphNet iteratively shrinks and expands a network, shrinking via a resource-weighted sparsifying regularizer on activations and expanding via a uniform multiplicative factor on all layers. In contrast to previous approaches, our meth…
Alternative neural network training using monotone variational inequality.
There is often latent network structure in spatial and temporal data and the tools of network analysis can yield fascinating insights into such data. In this paper, we develop a nonparametric method for network reconstruction from spatiotemporal data sets using multivariate Hawkes processes. In contrast to prior work o…
Flow-based data sets are necessary for evaluating network-based intrusion detection systems (NIDS). In this work, we propose a novel methodology for generating realistic flow-based network traffic. Our approach is based on Generative Adversarial Networks (GANs) which achieve good results for image generation. A major c…
Capsule networks improve with dynamic routing using Wasserstein objective.
Graph neural networks detect structural perturbations from time series data.
We present a new approach to assessing the robustness of neural networks based on estimating the proportion of inputs for which a property is violated. Specifically, we estimate the probability of the event that the property is violated under an input model. Our approach critically varies from the formal verification f…
Paper evaluates CNN-based facial landmark detection methods.
Traffic forecasting approaches are critical to developing adaptive strategies for mobility. Traffic patterns have complex spatial and temporal dependencies that make accurate forecasting on large highway networks a challenging task. Recently, diffusion convolutional recurrent neural networks (DCRNNs) have achieved stat…
We present a greedy-based approach to construct an efficient single hidden layer neural network with the ReLU activation that approximates a target function. In our approach we obtain a shallow network by utilizing a greedy algorithm with the prescribed dictionary provided by the available training data and a set of po…
Bayesian neural networks improve likelihood-free inference efficiency.
Deep Neural Networks have shown tremendous success in the area of object recognition, image classification and natural language processing. However, designing optimal Neural Network architectures that can learn and output arbitrary graphs is an ongoing research problem. The objective of this survey is to summarize and …
Structure learning of Bayesian networks is an important problem that arises in numerous machine learning applications. In this work, we present a novel approach for learning the structure of Bayesian networks using the solution of an appropriately constructed traveling salesman problem. In our approach, one computes an…
Survey on ML for wireless network optimization across PHY, MAC, and network layers.
Paper proposes an efficient algorithm for learning sparse Bayesian networks from discrete high-dimensional data.
Unified platform SOCRATES for neural network analysis.
New method improves community detection for large networks.
Novel approach embeds loss tunnels in neural networks, revealing insights into their structure.
Probabilistic deep learning uses neural networks and models to handle uncertainty.
Hybrid approach for large-scale network synchronization using KF and PTP.
We propose a general framework for solving statistical mechanics of systems with finite size. The approach extends the celebrated variational mean-field approaches using autoregressive neural networks, which support direct sampling and exact calculation of normalized probability of configurations. It computes variation…
Proposes a deep neural network for multi-dimensional functional data classification.
Many approaches have been proposed to discover clusters within networks. Community finding field encompasses approaches which try to discover clusters where nodes are tightly related within them but loosely related with nodes of other clusters. However, a community network configuration is not the only possible latent …
Trained neural networks perform Bayesian reasoning for tasks beyond their initial scope.
New approach learns latent motifs in networks for mesoscale structure analysis.
This paper explores the relationships between migration and trade using a complex-network approach. We show that: (i) both weighted and binary versions of the networks of international migration and trade are strongly correlated; (ii) such correlations can be mostly explained by country economic/demographic size and ge…
Bayesian learning rule trains binary neural networks effectively.
Neural network approach simplifies multiscale problem homogenization.
Proposes a graph neural network for traffic forecasting in WANs.
Randomized neural networks improve optimal stopping problems efficiently.
Enhances deep neural networks for MRI reconstruction by increasing expressivity.
i-cNRL learns network differences with interpretability.
Bayesian approach learns linear networks from high-dimensional data.
Novel neural network approach on hyperbolic and SPD spaces.
Unified approach compares ERGM, GCN, and Word2Vec+MLP for collaboration network link prediction.
Bayesian approach adapts deep network structure for continual learning.