This document investigates the integration of adaptive distinguishing sequences into the process of active automata learning (AAL). A novel AAL algorithm "ADT" (adaptive discrimination tree) is developed and presented. Since the submission of the original thesis, the presented algorithm has been integrated into LearnLi…
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48 results for “Activation Integral”
Paper simplifies ANN structure into a functional form.
problem Current ANN structure is complex and difficult to analyze.
method Uses activation integral concept to represent ANN structure as a function.
result Simplified mathematical representation of ANN structure.
Establishes upper bounds on generalization error in active learning.
problem Improving query algorithms in active learning.
method Derives upper bounds on generalization error using informativeness and representativeness query strategies.
result Validates the use of regularization techniques to ensure bounds' validity.
Derives integral formula for ReLU networks with limited weights.
problem Finding optimal neural network weights with limited -norm.
method Derives integral representation formula for shallow ReLU networks under -norm constraint.
result Explicitly solves the least -norm neural network representation for a given function.
Neural networks can approximate any L^p functions on R^n.
problem Approximating functions on unbounded domains with neural networks.
method Monotone sigmoid, ReLU, ELU, Softplus, LeakyReLU activation functions.
result Shallow neural networks can arbitrarily well approximate L^p functions on R^n.
This paper provides an overview of activation functions in neural networks.
problem Confusion in activation function selection and properties in deep learning.
method Analytic review of popular activation functions.
result Clarification of activation function properties and selection.
This paper proposes new methods for ALR that consider informativeness, representativeness, and diversity.
problem Efficiently label samples for regression models with limited labeled data.
method Integrates informativeness, representativeness, and diversity in pool-based sequential active learning.
result Demonstrates effectiveness of new ALR approaches on 12 datasets.
New modifiers improve noisy RNN replay in hippocampal networks.
problem Improving noisy RNN replay in hippocampal networks.
method Three approaches: hidden state leakage, adaptation, and momentum.
result Hidden state leakage, adaptation, and momentum improve noisy RNN replay.
S2KAN integrates symbolic primitives into neural network activations for improved interpretability.
problem Training activations in KANs often lack symbolic fidelity, leading to unintelligible models.
method Softly Symbolified Kolmogorov-Arnold Networks (S2KAN) integrates symbolic primitives into training with learnable gates and a Minimum Description Length objective.
result S2KAN discovers interpretable forms when symbolic terms suffice, gracefully degrading to dense splines when necessary.
Generative AI predicts economic activity from corporate transcripts.
problem Predicting economic activity using existing measures like surveys.
method Extracted managerial expectations from transcripts using generative AI.
result AI Economy Score predicts economic activity up to 10 quarters ahead.
We design a new nonparametric method that allows one to estimate the matrix of integrated kernels of a multivariate Hawkes process. This matrix not only encodes the mutual influences of each nodes of the process, but also disentangles the causality relationships between them. Our approach is the first that leads to an …
modAL is a modular active learning framework for Python, aimed to make active learning research and practice simpler. Its distinguishing features are (i) clear and modular object oriented design (ii) full compatibility with scikit-learn models and workflows. These features make fast prototyping and easy extensibility p…
We present analytical exploration of novel activation functions as consequence of integration of several ideas leading to implementation and subsequent use in habitability classification of exoplanets. Neural networks, although a powerful engine in supervised methods, often require expensive tuning efforts for optimize…
ActiveCQ improves causal quantity estimation with active learning and Gaussian Processes.
problem Estimating causal quantities requires large datasets, which are costly.
method Unified framework using Gaussian Processes and conditional mean embeddings for distribution estimation. Derived principled acquisition strategies based on information gain and total variance reduction.
result Framework significantly outperforms baselines in sample efficiency across various causal quantities.
We propose a new active learning algorithm for parametric linear regression with random design. We provide finite sample convergence guarantees for general distributions in the misspecified model. This is the first active learner for this setting that provably can improve over passive learning. Unlike other learning se…
Numerical valuation of European options under two-asset infinite-activity exponential Lévy modelsmath.NA
Method extends option valuation for 2D Lévy models.
problem Valuation of European options under 2-asset infinite-activity Lévy models.
method Developed numerical method extending Wang et al. (2007) for 1D to 2D, using Fourier transform for integral term and semi-Lagrangian theta-method for temporal discretization.
result Favourable second-order convergence for Normal Tempered Stable dynamics.
Bayesian quadrature (BQ) is a sample-efficient probabilistic numerical method to solve integrals of expensive-to-evaluate black-box functions, yet so far,active BQ learning schemes focus merely on the integrand itself as information source, and do not allow for information transfer from cheaper, related functions. Here…
Research uses deep learning and copulas to predict multivariate survival data.
problem Handling right-censored and correlated multivariate survival data.
method Integrates deep learning, copula functions, and survival analysis. Uses copula-based activation functions to model nonlinear dependencies.
result Enhanced prediction accuracy for multivariate survival responses.
We address challenges of active learning under scarce informational resources in non-stationary environments. In real-world settings, data labeled and integrated into a predictive model may become invalid over time. However, the data can become informative again with switches in context and such changes may indicate un…
Study examines cryptoasset service providers in Austria, revealing global integration and distinct responses to market shocks.
problem Understanding cryptoasset integration and stress behavior in national economies.
method Directly identified on-chain addresses of Austrian crypto-asset service providers, reconstructing transaction activity across multiple cryptocurrencies.
result Austrian crypto-asset service providers are globally integrated, with distinct responses to market shocks.
IMPACT optimizes LLM compression by focusing on activation importance, reducing model size up to 55.4%.
problem Resource constraints in deploying large language models (LLMs).
method IMPACT integrates activation importance into low-rank compression, optimizing for both size and accuracy.
result IMPACT achieves up to 55.4% greater model size reduction while maintaining comparable or better accuracy.
The paper shows neural networks can approximate functions over non-compact domains with non-polynomial activation.
problem Approximating functions over non-compact domains using neural networks.
method Using single-hidden-layer feedforward neural networks with non-polynomial activation functions over non-compact subsets of Euclidean spaces.
result Neural networks can approximate functions in weighted -spaces and weighted Sobolev spaces over unbounded domains.
Active Kriging Monte Carlo simulation method with conformal certification for failure probability estimation
problem Failure probability estimation in structural reliability analysis
method Active learning framework with conformal prediction
result Improved uncertainty quantification and reliability of failure probability estimates
Adaptive quadrature improves Bayesian inference through active learning.
problem Efficiently estimating posterior densities in Bayesian inference.
method Sequential node selection using acquisition functions, combining interpolative surrogate models and quadrature rules.
result Positive estimation of marginal likelihood with improved accuracy.
Stock market integration in the Latin American markets: further evidence from nonlinear modelingq-fin.ST
This article studies the financial integration between the six main Latin American markets and the US market in a nonlinear framework. Using the threshold cointegration techniques of Hansen and Seo (2002), we show significant threshold stock market linkages between Mexico, Chile and the US. Thus, the dynamics of these …
Study on function sensitivity in random DNNs using large deviation theory.
problem Understanding function sensitivity in finite-size deep neural networks.
method Large deviation theory and path integral analysis applied to random DNNs with ReLU and sign activations.
result Random DNNs with ReLU activations are more robust to parameter perturbations.
Efficient Human-in-the-Loop Active Learning: A Novel Framework for Data Labeling in AI Systemsstat.ML
A new AI framework optimizes expert labeling of unlabeled data.
problem Efficiently labeling unlabeled data for AI systems.
method Human-in-the-loop active learning framework with innovative query schemes.
result Higher accuracy and lower loss compared to other methods.
DASNet improves neural network efficiency by dynamically pruning activations.
problem Reducing neural network size and computational complexity in embedded systems.
method Dynamic Activation Sparsity (DAS) using a winners-take-all (WTA) dropout technique.
result DASNet achieves better computation cost reduction compared to static feature map pruning methods.
We use neural networks as control variates with geometric integration techniques.
problem Analytic integration of neural network approximations for variance reduction.
method Integration domain subdivision using computational geometry for MLPs with continuous piecewise linear activation functions.
result Neural networks can be used as control variates with geometric integration methods.
Inspired by brain's modality fusion, this paper detects active speakers from audio and video.
problem Detecting active speakers in noisy environments.
method Inspired by brain's superior colliculus, combines audio and visual data through specialized neural networks and a novel fusion layer.
result Achieved results greatly surpassing initial expectations, confirming the effectiveness of the proposed method.
A new framework learns to select samples for active learning.
problem Lack of labeled data for training active learning strategies.
method Learning to Sample (LTS) framework with a sampling and boosting model.
result Significantly outperforms baselines in limited label budgets.
New method predicts brain activity using past states for more accurate source estimation.
problem Independent source estimates ignore temporal context of neuronal activity.
method Combines LSTM networks with Minimum-Norm Estimates (MNE) for context-dependent brain activity prediction.
result CMNE leads to more accurate source estimation compared to independent MNE.
Hexagon grid patterns emerge from conformal isometry in grid cell neural networks.
problem Understanding the algebraic, geometric, and topological properties of grid cells.
method Investigating recurrent neural network models of grid cells, focusing on Lie group and Lie algebra representations, conformal isometry, and hexagon periodic patterns.
result Conformal isometry leads to hexagon periodic patterns in grid cell responses and accurate path integration.
The paper proposes a new model to capture specialized brain regions using mixture of regression experts.
problem Learning a forward mapping that relates stimuli to brain activation assumes all regions respond similarly, ignoring brain specialization.
method Clustering brain regions, learning different linear regression models for each cluster, using a mixture of linear experts.
result The proposed model predicts brain activation more accurately than conventional models.
Improving Network Threat Detection by Knowledge Graph, Large Language Model, and Imbalanced Learningcs.LG
Enhanced network threat detection using KG, LLM, and imbalanced learning.
problem Challenges in network threat detection due to complex attack patterns and limited historical data.
method Integrated framework combining Knowledge Graph, Imbalanced Learning, and Large Language Model.
result Improved threat capture rate by 3%-4% and increased interpretability of risk predictions.
Adaptive batch sizes improve active learning efficiency and flexibility.
problem Fixed batch sizes in active learning are inefficient due to dynamic cost-speed trade-offs.
method Probabilistic Numerics framework that adaptively changes batch sizes based on integration error and precision objectives.
result Significant enhancement in learning efficiency and flexibility across various applications.
uGMM-NN integrates probabilistic reasoning into neural networks.
problem Capturing multimodality and uncertainty in neural network activations.
method Parameterizes activations as univariate Gaussian mixtures with learnable parameters.
result Competitive discriminative performance with probabilistic activations.
We present two Bayesian procedures to infer the interactions and external currents in an assembly of stochastic integrate-and-fire neurons from the recording of their spiking activity. The first procedure is based on the exact calculation of the most likely time courses of the neuron membrane potentials conditioned by …
Paper uses pre-trained models and active learning to analyze customer reviews quickly.
problem Automatic review analysis with limited labeled data and time.
method Pre-trained language representation and active learning framework.
result Fully automatic review analysis achieved at a faster pace.
DABS uses a policy network to select experiments in high-dimensional design spaces.
problem Adaptive factorial screening in high-dimensional discrete design spaces.
method DABS learns a policy network offline to sequentially select experiments, incorporating sparsity and interactions via a spike-and-slab prior.
result DABS achieves superior accuracy and scalability over classical and Bayesian baselines under tight experimental budgets.
A3RL combines online and offline RL with active sampling to improve policy learning.
problem Combining online and offline RL for sample efficiency and robustness.
method A3RL uses a confidence-aware Active Advantage Aligned (A3) sampling strategy to prioritize data from both online and offline sources.
result A3RL outperforms competing online RL techniques that use offline data.
Neural networks explained through geometric projections.
problem Understanding the geometric and mathematical underpinnings of neural networks.
method Exploiting connections between integration, Radon transforms, and neural networks.
result Distribution of neural network outputs can be interpreted as nonlinear projections along hypersurfaces.
Bayesian system helps identify and thwart terrorist plans.
problem Identifying and thwarting terrorist plans before they occur.
method Develops a Bayesian decision support system to integrate member and group activities.
result Estimates the combined threat posed by a terrorist group.
We propose a novel sampling framework for inference in probabilistic models: an active learning approach that converges more quickly (in wall-clock time) than Markov chain Monte Carlo (MCMC) benchmarks. The central challenge in probabilistic inference is numerical integration, to average over ensembles of models or unk…
A new neural network layer integrates graph learning into classification tasks.
problem Lack of relational information in standard deep learning architectures for label predictions.
method Derives backpropagation equations for a differentiable graph learning layer.
result Smooth label transitions, improved generalization, and robustness to adversarial attacks.
A new MARL framework for community-based cooperation with transfer and active exploration.
problem Flexible coordination patterns in multi-agent systems with community structures.
method Community-based multi-agent reinforcement learning with transfer and active exploration.
result Provably convergent actor-critic algorithms for structured information sharing and transfer learning.
Multiple query criteria active learning (MQCAL) methods have a higher potential performance than conventional active learning methods in which only one criterion is deployed for sample selection. A central issue related to MQCAL methods concerns the development of an integration criteria strategy (ICS) that makes full …
New activation improves deep learning accuracy and robustness.
problem Improving accuracy and robustness of deep neural nets with limited data.
method Replaces softmax with graph Laplacian-based interpolating function.
result Significantly improves natural and robust accuracy.