The paper predicts edge weights in weighted directed networks using metric geometry.
problem Predicting edge weights in weighted directed networks.
method Introducing new types of weighted directed networks (AWDNs), constructing metrics, and proposing modified kNN and SVM methods.
result The proposed methods outperform traditional approaches in predicting edge weights.
Predicting neural network accuracy from weights without testing.
problem Predicting neural network performance based on weights alone.
method Used simple statistics of weights to rank neural networks' performance.
result Simple predictors can rank networks' performance with high accuracy (R2 score > 0.98).
A new method compresses deep neural networks by predicting and quantizing weights between layers.
problem Resource constraints in deep neural networks.
method Inter-Layer Weight Prediction (ILWP) and quantization based on Smoothly Varying Weight Hypothesis (SVWH).
result The method achieves higher weight compression rates at the same accuracy level.
Paper uses GNN and conformal prediction for accurate edge weight prediction.
problem Predicting edge weights on graphs for various applications.
method Graph Neural Network (GNN) with conformal prediction and error reweighting.
result Our method provides better coverage and efficiency than baselines.
Proposes a method to generate prediction intervals using weighted asymmetric loss functions.
problem Generating reliable prediction intervals for neural network models.
method Uses a weighted asymmetric loss function to estimate prediction intervals.
result The method produces reliable prediction intervals in complex machine learning scenarios.
Bio-inspired neural networks use predictive coding for efficient weight updates.
problem Training artificial neural networks efficiently and biologically plausibly.
method Predictive Coding (PC) updates weights locally using only local information.
result PC provides theoretical advantages like automatic gradient scaling.
Improved protein structure classification using weighted graphlets and deep neural networks.
problem Protein structure classification for function prediction.
method Developed a weighted network and graphlet-based measure, combined with a deep neural network.
result Significantly improved performance on 36 real datasets compared to existing methods.
Proposes a model to classify nodes in networks using weighted feedback relations.
problem Challenges in predicting node labels in sparse networks with implicit feedback.
method Weighted personalized two-stage matrix factorization model with Bayesian ranking loss.
result Significantly outperforms state-of-the-art models on various datasets.
Bayesian neural networks learn weights with closed-form updates.
problem Efficiently learning Bayesian neural networks with closed-form updates.
method Closed-form Bayesian inference for online learning of Gaussian-weighted BNNs.
result Closed-form expressions for sequential/online training of BNNs.
Airlines optimize fuel loading with better flight time predictions.
problem Flight time uncertainties and their impact on fuel consumption.
method Developed a spatial weighted recurrent neural network model.
result The model provides more accurate flight time predictions, reducing fuel consumption.
Community detection is an important task in network analysis, in which we aim to learn a network partition that groups together vertices with similar community-level connectivity patterns. By finding such groups of vertices with similar structural roles, we extract a compact representation of the network's large-scale …
We demonstrate that there is significant redundancy in the parameterization of several deep learning models. Given only a few weight values for each feature it is possible to accurately predict the remaining values. Moreover, we show that not only can the parameter values be predicted, but many of them need not be lear…
Enhances physics-informed neural networks with adaptive sampling and weighting.
problem Challenges in training physics-informed neural networks on complex problems.
method Hybrid adaptive sampling and weighting method.
result Consistently improves prediction accuracy and training efficiency.
MANA-Net improves market predictions by dynamically weighting news sentiments.
problem Aggregated Sentiment Homogenization in financial news data.
method Dynamic market-news attention mechanism to aggregate sentiments.
result MANA-Net outperforms recent market prediction methods by 1.1% Profit & Loss and 0.252 daily Sharpe ratio.
Under what conditions is an edge present in a social network at time t likely to decay or persist by some future time t + Delta(t)? Previous research addressing this issue suggests that the network range of the people involved in the edge, the extent to which the edge is embedded in a surrounding structure, and the age…
Study improves epidemic forecasting with a sparsified GSRNN.
problem Epidemic forecasting on real-world health data.
method Graph-structured recurrent neural network (GSRNN) with sparsification via transformed-ℓ1 penalty. result Maintained prediction accuracy with 70% of network weights being zero.
We show how well known rules of back propagation arise from a weighted combination of finite automata. By redefining a finite automata as a predictor we combine the set of all k-state finite automata using a weighted majority algorithm. This aggregated prediction algorithm can be simplified using symmetry, and we pro…
Convolutional networks struggle to learn Game of Life, even with lottery ticket weights.
problem Training convolutional networks to predict Conway's Game of Life is challenging.
method Examined small convolutional networks trained on Game of Life, focusing on weight initializations and network sizes.
result Minimal networks require significantly more parameters to converge, and their performance is sensitive to small changes in weights.
SGD and weight decay encourage neural networks to learn low-rank weight matrices.
problem The bias of SGD towards low-rank weight matrices in neural networks.
method The study investigates the effect of SGD and weight decay on the rank of weight matrices in neural networks, both theoretically and empirically.
result Training with SGD and weight decay induces a bias towards rank minimization in weight matrices, which becomes more pronounced with smaller batch sizes and stronger weight decay.
Random matrix analysis reveals that neural network weights are mostly random, with some indicating learned information.
problem Understanding how neural networks store information needed for tasks.
method Random matrix theory (RMT) applied to weight matrices of trained deep neural networks.
result Most singular values and eigenvectors of trained neural networks follow universal RMT predictions, suggesting they are random and do not contain system-specific information.
Deep neural networks improve chemical reactor control using MPC.
problem Improving control of chemical reactors with neural networks.
method Training neural networks on model predictive control (MPC) for reactor control.
result Neural network can mimic MPC control inputs while maintaining constraints.
Adapts neural network neurons' activation functions for better predictions.
problem Training neural networks with fixed activation functions limits their performance.
method Proposes training over a shape parameter, allowing neurons to adapt their own activation functions.
result Improves prediction accuracy by allowing neurons to tune their activation functions.
We develop a simple theoretical framework for the evolution of weighted networks that is consistent with a number of stylized features of real-world data. In our framework, the Barabasi-Albert model of network evolution is extended by assuming that link weights evolve according to a geometric Brownian motion. Our model…
LEAP predicts graph edges and weights from path aggregations.
problem Predicting edges and weights in graphs.
method Trainable framework based on path aggregations.
result LEAP outperforms state-of-the-art methods in link and rating prediction.
Paper develops a framework to optimize neural networks using weighted metrics.
problem Discrepancy between maximizing weighted classification scores and minimizing loss function.
method Formalizes weighted classification metrics and constructs corresponding losses.
result Framework includes well-established approaches like cost-sensitive learning and weighted cross entropy.
A new framework enhances IDW models for complex industrial datasets.
problem Low performance of IDW models in complex industrial datasets.
method Deep reinforcement learning network to enhance IDW models and learn hyperparameters.
result The proposed framework achieves differential spatial prediction and is more accurate than current IDW models.
The paper analyzes prediction error in nonstationary settings using weighted risk minimization.
problem Prediction under distribution drift and nonstationary conditions.
method General decomposition of excess risk into learning and drift terms, proving oracle inequalities under mixing conditions.
result Oracle inequalities for the learning error, providing bounds that hold uniformly over arbitrary weight classes.
Simplified Bayesian neural networks reduce model complexity and improve interpretability.
problem Over-parameterization and interpretability issues in deep learning models.
method Input-skip Latent Binary Bayesian Neural Networks (LBBNNs) that allow covariates to skip layers or be excluded.
result Significant reduction in model complexity (over 99%) with minimal loss in accuracy and uncertainty.
New method reduces neural network memorization of noisy labels.
problem Neural networks memorize label noise, leading to poor generalization.
method Proposes an auxiliary network that predicts gradients without labels.
result Reduces memorization of label-noise, improving generalization.
Proposes a novel network for CTR prediction by learning modality-specific and modality-invariant representations.
problem Learning good representation of items from multimodal features in E-commerce is challenging due to redundant information across modalities.
method Introduces a Multimodal Adversarial Representation Network (MARN) that calculates modality-specific weights and learns modality-invariant representations.
result Consistently achieves remarkable improvements over state-of-the-art methods in CTR prediction.
We introduce a novel scheme to train binary convolutional neural networks (CNNs) -- CNNs with weights and activations constrained to {-1,+1} at run-time. It has been known that using binary weights and activations drastically reduce memory size and accesses, and can replace arithmetic operations with more efficient bit…
AWARE improves graph prediction by aggregating walks with attention schemes.
problem Improving graph prediction accuracy using walk aggregation.
method Integrates attention schemes into walk-aggregating GNNs.
result AWARE outperforms existing methods in graph-level prediction tasks.
Bayesian Perceptron offers fully Bayesian neural networks without complex computations.
problem Lack of uncertainty quantification in neural networks.
method Bayesian inference framework for perceptron training and predictions in closed-form.
result Analytical expressions for perceptron's output and weight learning provided.
This paper introduces hierarchical Gaussian process priors for neural networks to capture weight correlations and inductive biases.
problem Capturing weight correlations and inductive biases in neural networks.
method Hierarchical Gaussian process priors with unit embeddings and input-dependent kernels.
result Hierarchical Gaussian process priors provide competitive predictive performance and desirable uncertainty estimates.
Study shows proper initialisation of binary weights is crucial for deep neural networks.
problem Training stochastic binary neural networks with continuous surrogates is challenging.
method Developed new surrogates based on Markov chain theory and mean field analysis.
result Critical initialisations are necessary for training deep networks with binary weights.
Proposes new methods for interpreting document classification models.
problem Interpretation fragility of attention-based neural networks.
method Corpus-level and concept-based explanation methods using attention weights.
result Extracts semantically meaningful keywords and concepts for model predictions.
This paper investigates whether the gravity model (GM) can explain the statistical properties of the International Trade Network (ITN). We fit data on international-trade flows with a GM specification using alternative fitting techniques and we employ GM estimates to build a weighted predicted ITN, whose topological pr…
Convex dual network improves neural network reconstruction for medical imaging.
problem Non-convex nature of neural networks hinders their use in sensitive applications.
method Introduces a convex duality framework for a two-layer fully-convolutional ReLU denoising network.
result Training neural networks with weight decay regularization induces path sparsity and piecewise linear filtering.
Adaptive weights improve physics-informed neural networks and deep operator networks.
problem Training physics-informed neural networks and deep operator networks can be challenging, leading to unsatisfactory accuracy and efficiency.
method Proposes a pointwise adaptive weighting method that balances the residual decay rate across different training points.
result Our proposed approach of balanced residual decay rates offers advantages including bounded weights, high prediction accuracy, fast convergence rate, low training uncertainty, low computational cost, and ease of hyperparameter tuning.
Random weights in GNNs match learned weights in performance.
problem Feature rank collapse in GNNs.
method Replacing learned weights with random weights.
result Random weights achieve comparable performance to learned weights, reducing training time and memory usage.
Paper compares deep learning models for financial series prediction.
problem Predicting financial time series with deep learning methods.
method Compares MLP, 1D CNN, stacked LSTM, attention networks, and weighted attention networks.
result Attention LSTM model produces a 0.76 hit ratio, superior to other methods.
Proposes learning invariances in neural networks using a weight-space approach.
problem Learning invariances from data in neural networks remains an open problem.
method Minimizes a lower bound on the marginal likelihood in weight space.
result Results in higher performing models with naturally learned invariances.
The paper tightens bounds on covering numbers for deep ReLU networks.
problem Characterizing the capacity and performance of deep ReLU networks.
method Derives tight lower and upper bounds on metric entropy of ReLU networks.
result Establishes optimality in nonparametric regression via deep networks.
Proposes a graph-based approach for better stock prediction.
problem Long-range dependencies and chaotic property in stock prediction.
method Transforms time series into graphs, extracting structural information to resolve issues.
result Obtains the best performance among state-of-the-art benchmarks and highest cumulative profits in trading simulations.
A framework for multilayer networks predicts links without shared structures.
problem Link prediction in multilayer networks without shared structures.
method Bi-level model averaging with K-fold cross-validation. result Framework outperforms existing methods in predictive accuracy and robustness.
A lightweight model predicts IT system KPIs from historical data.
problem Predicting future KPIs of interconnected IT systems is hard due to diverse and changing components.
method A weighted heterogeneous ensemble method combining neural network and mean predictor.
result Achieved R2 scores of 0.10 and 0.15 on test data. Enhances neural network regression performance by modeling weight and variance uncertainty.
problem Improving predictive performance of neural networks for regression tasks.
method Extended Blundell's framework to include variance uncertainty, using a full posterior distribution over variance parameters.
result Explicitly modeling variance uncertainty improves generalization of Bayesian neural networks.
Bayesian neural networks use temperature adjustments to improve predictive performance.
problem Lack of theoretical generalization guarantees for Bayesian neural networks.
method Temperature adjustments to balance likelihood and prior regularization.
result Improved predictive performance through temperature adjustments.