Explains a new approach to summarize network data using personalized PageRank matrices.
problem Understanding the essence of sparse and multifaceted network data.
method Develops a new algebraic approach using personalized PageRank matrices.
result Summarizes basic algebraic properties of personalized PageRank matrices.
New invariants measure how far spanning surfaces are from being compressible.
problem Understanding how essential spanning surfaces are in 3-manifolds.
method Introducing algebraic and geometric essence invariants, proving plumbing respects algebraic essence, and extending results to arbitrary 3-manifolds.
result Plumbing respects the algebraic essence of spanning surfaces, extending Ozawa's theorem.
ConvNets can be translated into CKNs that perform similarly.
problem The distinction between ConvNets and kernel-based methods.
method Translation of ConvNets into CKNs using a new gradient algorithm.
result CKNs perform as well as ConvNets, supporting the translation.
Mixup improves neural network generalization and robustness.
problem Desirable behaviors like memorization and sensitivity to adversarial examples in deep neural networks.
method Trains neural network on convex combinations of pairs of examples and their labels.
result Improves generalization of state-of-the-art neural network architectures.
Investigates latent variable models for useful generative concept representations.
problem Creating latent representations that support various concepts and attributes.
method Latent variable modeling, including latent variable models, latent representations, and latent spaces.
result Hierarchical latent representations and latent space vectors and geometry are effective for generative concept representations.
Identifies influential neurons in deep networks for better explanations.
problem Explaining complex behaviors of deep neural networks.
method Identifies neurons with high influence using an influence measure and provides interpretations.
result Identifies influential concepts that generalize across instances and isolates individual features used by the network.
Novel neural network predicts electricity prices with higher moments.
problem Probabilistic forecasting of volatile electricity prices.
method Distributional neural network with a probability layer.
result Significantly outperforms benchmarks in forecasting.
Variational Laplace improves Bayesian neural networks performance.
problem Improving Bayesian neural networks performance.
method Develops variational Laplace for BNNs, exploiting curvature of likelihood.
result Variational Laplace outperforms other inference methods.
Variational Laplace improves Bayesian neural network performance without sampling.
problem Improving Bayesian neural network performance and calibration.
method Develops a new variational Laplace method for BNNs, exploiting curvature of likelihood.
result Variational Laplace outperforms standard VI methods in test performance and calibration.
Neural networks predict stock prices better than traditional methods.
problem Predicting stock prices in volatile financial markets.
method Compared five neural network models (BP, RBF, GRNN, SVMR, LS-SVMR) on three stocks.
result BP neural network outperformed other models in accuracy.
Proposes volumization for neural networks to control bias-variance tradeoff.
problem Improving generalization and preventing memorization in neural networks.
method Defines a physical volume for weights, interpolating between L2 and L∞ regularization.
result Volumization interpolates between weight decay and clipping, improving generalization.
Paper introduces a new edge exchangeable block model for complex networks.
problem Limitations of the stochastic block model in analyzing complex networks.
method Develops a Bayesian nonparametric edge exchangeable block model.
result The new model outperforms state-of-the-art SBMs for link prediction.
mGRN improves multivariate time series prediction by managing marginal and joint memories.
problem Extracting dependencies in multivariate sequential data with strong serial and cross-sectional dependencies.
method Developed a novel recurrent network architecture, Memory-Gated Recurrent Networks (mGRN), with gates for marginal and joint memories.
result mGRN consistently outperforms state-of-the-art architectures on various public datasets.
Generative Adversarial Forests improve GAN performance through better conditioning.
problem Improving the stability and performance of Generative Adversarial Networks (GANs).
method Embedding decision forest capabilities into GAN discriminators.
result Significant performance improvements on the Oxford Flowers and Aligned Celebrity Faces datasets.
A simple formula captures the essence of optimal lifestyling.
problem Optimal investment in life-cycle economics with credit constraints.
method Provides a simple explicit formula for optimal lifestyling.
result Simple formula accurately captures the main essence of lifestyling effect.
This work analyzes industrial IoT data for security using machine learning.
problem Security vulnerabilities in industrial IoT networks.
method Transformed industrial network data into time series and analyzed with three algorithms.
result Matrix Profiles outperform other methods with minimal parameterization.
The paper analyzes implicit regularization in tensor factorization using neural networks.
problem Understanding implicit regularization in tensor factorization.
method Dynamical systems perspective and gradient descent analysis.
result Gradient descent induces a form of greedy low tensor rank search.
WaveFit uses fixed-point iteration to create high-quality neural vocoders.
problem Creating high-quality neural vocoders with fast inference.
method Integrates GANs' adversarial training into a DDPM-like iterative framework based on fixed-point iteration.
result WaveFit synthesizes speech with naturalness comparable to human speech, and is significantly faster than existing methods.
EKI solves machine learning tasks without gradients.
problem Machine learning tasks, especially those involving deep neural networks.
method Ensemble Kalman inversion (EKI) as a gradient-free algorithm.
result Wide applicability and robustness of EKI demonstrated.
This paper provides mathematical foundations for regression methods used in forward initial margin approximation.
problem Developing robust methods for approximating forward initial margin.
method Introduces mathematical rigor to show that regression methods are variations of approximating the conditional expectation function.
result Each regression method is a numerical estimation of the conditional expectation with a different functional form.
Proposes a new confidence criterion for deep neural networks to predict failures.
problem Predicting failures in deep neural networks.
method Introduces True Class Probability (TCP) as a new confidence criterion and proposes a learning scheme to estimate it.
result The proposed approach consistently outperforms existing methods in failure prediction.
2DSCNs improve image data analytics by extending SCN to handle spatial information.
problem Limitation of 1D SCNs in preserving spatial information of images.
method Extend SCN to 2DSCNs by stochastically configuring hidden nodes in a matrix-inputs framework.
result 2DSCNs outperform 1D SCNs in image data analytics tasks.
Graphical notation simplifies tensor operations and decompositions.
problem Complex tensor operations are difficult to understand and represent.
method Introduces graphical notation to represent tensor operations.
result Simplified representation of tensor operations and decompositions.
New measure FTC quantifies how much a ReLU network can fine-tune.
problem Analyzing memorization capacity in fine-tuned neural networks.
method Defined Fine-Tuning Capacity (FTC) for additive fine-tuning of ReLU networks.
result Upper and lower bounds on FTC for 2 and 3-layer ReLU networks.
CGRL improves graph neural networks' OOD generalization by blocking spurious correlations.
problem Graph Neural Networks struggle with out-of-distribution data due to learning spurious correlations.
method Formulates a causal graph, uses backdoor adjustment, and introduces a loss replacement strategy.
result Significantly improves OOD generalization of GNNs, stabilizing mutual information learning.
Globalization is one of the central concepts of our age. The common perception of the process is that, due to declining communication and transport costs, distance becomes less and less important. However, the distance coefficient in the gravity model of trade, which grows in time, indicates that the role of distance i…
This study uses complex networks to analyze influential spreaders and their effects on different market sectors.
problem Existing methods failed to distinguish between positive and negative influences of market sectors.
method LIEST (Local Influential Effects for Specific Target) method using complex network analysis.
result LIEST effectively distinguishes positive and negative influences of market sectors during different periods.
MMCGAN uses explicit manifold learning to improve GAN performance.
problem GAN mode collapse and unstable training.
method Introduces Minimum Manifold Coding (MMC) as a prior to guide GAN training.
result MMCGAN effectively alleviates mode collapse and stabilizes GAN training.
Unified framework for convolution and attention models.
problem Complex neural network structures and their parameter control.
method Unified framework for convolution and attention models.
result Attention models are a special case of convolution with adaptive structure.
Enhanced ELM reduces randomness in neural network training.
problem Challenges in ELM architecture design and sensitivity to random weight initialization.
method Introduces Effective Non-Random ELM (ENR-ELM) incorporating signal processing concepts.
result ENR-ELM simplifies architecture design and eliminates random weight selection.
STanHop predicts multivariate time series with memory-enhanced capabilities.
problem Predicting multivariate time series with memory-enhanced capabilities.
method Sparse Tandem Hopfield Network (STanHop) with two external memory modules.
result STanHop outperforms dense Hopfield models in memory retrieval error.
New method calibrates confidence of neural networks for better out-of-distribution detection.
problem Detecting samples from in-distribution vs. out-of-distribution in machine learning.
method Jointly trains classification and generative neural networks with additional terms to improve confidence calibration.
result Significantly improved detection performance on various image datasets.
NetSMF efficiently embeds large networks by sparse matrix factorization.
problem Learning latent representations for large-scale networks efficiently.
method NetSMF leverages spectral sparsification to efficiently sparsify and factorize a dense matrix.
result NetSMF achieves high efficiency and effectiveness on large-scale networks.
A new framework decouples SSL tasks into VDA and VLC, revealing VDA's importance.
problem Designing effective self-supervised learning tasks without manual annotation.
method Borrowing a multi-view perspective, the paper decouples popular pretext tasks into VDA and VLC, focusing on VDA's role in feature learning.
result VDA tasks dominate SSL performance, and integrating predictions from augmented views improves overall performance.
Few-shot unsupervised image-to-image translation model learns from a few examples.
problem Current unsupervised image-to-image translation methods require many images at training time.
method Coupling adversarial training with a novel network design for few-shot learning.
result Model achieves effective few-shot image-to-image translation.
We provide yet another proof of the existence of calibrated forecasters; it has two merits. First, it is valid for an arbitrary finite number of outcomes. Second, it is short and simple and it follows from a direct application of Blackwell's approachability theorem to carefully chosen vector-valued payoff function and …
Model learns to sort music clips in sequence.
problem Finding an optimal permutation of music clips.
method Proposed a music puzzle game for self-supervised learning of neural networks.
result Improved architecture (SEN) performs better on music medley.
Estimates and compares the capabilities of neural network architectures.
problem Developing quantitative methods to estimate and compare neural network architectures.
method Defining capacity as the logarithm of the number of functions an architecture can compute, and proving a cubic polynomial formula for layered architectures.
result The capacity of a neural network is given by a cubic polynomial in the layer sizes, with bottlenecks affecting the capacity.
The main goal of the paper is to solve some problems about shadow for the sphere generalized on the case of the ellipsoid. Here, the essence of the problem is to find the the minimal number of non-overlapping balls with centers on the sphere which are not holding the center of the sphere and such that every line passin…
Iterative algorithms are ubiquitous in the field of data mining. Widely known examples of such algorithms are the least mean square algorithm, backpropagation algorithm of neural networks. Our contribution in this paper is an improvement upon this iterative algorithms in terms of their respective performance metrics an…
New framework quantifies uncertainty in data and models using RKHS.
problem Quantifying uncertainty in data and models.
method Projecting data into RKHS, transforming PDF, decomposing gradient flow.
result Decomposes uncertainty moments, providing discriminative resolution.
Study reveals differences in medical image models' hidden representation refinement.
problem Understanding how intrinsic dimensionality changes in neural network hidden representations across different domains.
method Analysis of 11 natural and medical image datasets using 6 network architectures.
result Medical image models refine hidden representations earlier, suggesting differences in feature abstraction.
R2D2-GANs generate high-resolution images at real-time speed.
problem Generating high-resolution images at real-time speed.
method Conditional recursive GANs architecture.
result Synthesized images are indistinguishable from real ones.
This essay, an excerpt of the author's Ph.D. in Philosophy of mathematics (2012) thought of as being a companion to recent discoveries of new explicit Cartan geometry curvatures, analyzes how Gauss, after having devised the isometrically invariant character of curvature, struggled with elimination computations in order…
New Ricci flow method for directed graphs with balancing factor.
problem Analyzing asymmetry in directed networks.
method Rigorous formulation of Ricci flow on directed weighted graphs with balancing factor.
result Existence and uniqueness of discrete Ricci flow solutions.
SNAIL meta-learner outperforms existing methods on various tasks.
problem Meta-learning struggles with limited data and quick task adaptation.
method Simple and generic meta-learner using temporal convolutions and soft attention.
result SNAIL achieves state-of-the-art performance on multiple tasks.
Forward Automatic Differentiation (AD) is a technique for augmenting programs to compute derivatives. The essence of Forward AD is to attach perturbations to each number, and propagate these through the computation. When derivatives are nested, the distinct derivative calculations, and their associated perturbations, m…
ECC compresses DNNs for energy-constrained devices like UAVs and smartphones.
problem Energy-constrained deep neural networks in vision applications.
method ECC uses a bilinear regression model to estimate DNN energy consumption and optimizes compression to meet energy constraints.
result ECC achieves higher accuracy under the same or lower energy budget compared to state-of-the-art techniques.