This guide simplifies explainable deep learning for beginners.
problem Difficulty in understanding deep learning model decisions.
method Introduces three dimensions for explainable deep learning methods.
result Clarifies evaluations for model explanations.
This paper reviews methods to improve AI explainability in finance.
problem Lack of explainability in AI models, especially in finance.
method Categorizes methods to improve explainability of deep learning models.
result Provides a comparative survey of methods to enhance AI explainability.
The popularity of Deep Learning for real-world applications is ever-growing. With the introduction of high performance hardware, applications are no longer limited to image recognition. With the introduction of more complex problems comes more and more complex solutions, and the increasing need for explainable AI. Deep…
This review explores methods to explain deep neural networks and their applications.
problem Understanding the decision-making process of deep neural networks.
method Overview of interpretability methods, theoretical foundations, and comparative evaluations.
result Demonstrates the effectiveness of explainable AI in various applications.
New framework explains deep learning using signal processing techniques.
problem Lack of a rigorous mathematical theory explaining deep learning performance.
method Transform-domain sparse regularization, Radon transform, and approximation theory.
result Explains neural network properties and performance.
AI helps in drug discovery with understandable explanations.
problem Understanding the complex models behind AI-generated drugs.
method Explainable AI methods to interpret deep learning models.
result Improved interpretability of AI-generated drug properties.
XCM improves MTS classification with explainable deep learning.
problem Lack of explainable deep learning models for MTS classification.
method XCM is a compact CNN that extracts variable and timestamp information directly from input data.
result XCM outperforms state-of-the-art MTS classifiers on large and small datasets.
New proof shows norms can't explain deep learning's implicit regularization.
problem Understanding the implicit regularization in deep learning.
method Mathematical proof on matrix factorization problems.
result Implicit regularization drives norms towards infinity, suggesting rank minimization is key.
DF2M uses deep neural networks within a factor model for high-dimensional functional time series forecasting.
problem Forecasting high-dimensional functional time series with explainability and accuracy.
method Bayesian nonparametric model based on Indian Buffet Process and multi-task Gaussian Process, incorporating a deep kernel function.
result DF2M provides better explainability and superior predictive accuracy compared to conventional deep learning models.
Deep learning based knowledge tracing model has been shown to outperform traditional knowledge tracing model without the need for human-engineered features, yet its parameters and representations have long been criticized for not being explainable. In this paper, we propose Deep-IRT which is a synthesis of the item res…
Proposes a method to quantify and explain deep learning model uncertainties.
problem Deep learning model predictions are sensitive to perturbations and adversarial attacks.
method Gradient-based uncertainty attribution method to identify problematic regions and propose mitigation strategies.
result Proposed UA-Backprop method achieves competitive accuracy and efficiency compared to existing methods.
Paper introduces a method to explain deep learning models and identify good generalization.
problem Limited interpretability of neural networks hinders progress and real-world applications.
method Polytope interpolation method for local explainability and generalization assessment.
result Developed a method to identify deep learning models with good generalization properties.
Developed an explainable DRL model for financial portfolio management.
problem Inability of DRL agents to provide interpretable financial investment policies.
method Integrating PPO with feature importance techniques (SHAP, LIME) to enhance transparency.
result Ability to interpret DRL agent actions in prediction time.
Paper explains why small-loss criterion works for learning from noisy labels.
problem Learning from noisy labels in deep learning with limited labeled data.
method Theoretical analysis and reformulation of the small-loss criterion.
result Theoretical explanation and reformulation of the small-loss criterion.
Method trains deep models to explain predictions with fewer examples.
problem Difficulty in humans understanding deep model predictions.
method Simultaneously trains prediction and explanation models with sparse regularization.
result Improves faithfulness of explanations with fewer examples while maintaining predictive performance.
Deep RL strategy improves natural gas trading performance.
problem Improving natural gas trading performance using Deep RL.
method Domain-adapted Deep RL for natural gas futures trading.
result Deep RL strategy outperforms benchmarks and reduces transaction costs.
A theoretical framework for deep learning is proposed to explain its effectiveness.
problem Lack of a comprehensive theory explaining deep learning's effectiveness.
method Integrates three characteristics into a graphical model called neurashed.
result Explains common empirical patterns in deep learning and provides insights into regularization and elasticity.
CoxSE combines deep learning with self-explaining neural networks for survival analysis.
problem Improving predictive power of Cox Proportional Hazards model while maintaining explainability.
method Proposes CoxSE, a locally explainable Cox proportional hazards model using SENN, and CoxSENAM, a hybrid model with NAM.
result CoxSE provides more stable and consistent explanations while maintaining predictive power.
Survey of spectral, probabilistic, and deep metric learning methods.
problem Developing effective distance metrics for various machine learning tasks.
method Divided into spectral, probabilistic, and deep approaches, covering various techniques and their applications.
result Comprehensive overview of metric learning methods, including new developments and applications.
FCDD explains deep anomaly detection by mapping anomalies away and providing heatmap explanations.
problem Deep one-class classification's non-linear transformation makes it hard to interpret.
method FCDD learns a mapping that concentrates nominal samples, maps anomalies away, and provides heatmap explanations.
result FCDD sets a new state of the art in unsupervised anomaly detection on MVTec-AD.
MACQ method explains deep learning models by analyzing feature contributions across prediction levels.
problem Explaining deep learning model predictions.
method Global gradient-based, model-agnostic approach focusing on marginal attribution.
result MACQ separates feature contributions from interaction effects and visualizes 3-way relationships.
New deep learning model interprets tabular data with variable selection and explainability.
problem Deep learning models lack interpretability and variable selection.
method Proposes a new network architecture that combines deep learning with generalized linear models.
result The model provides superior predictive power and interpretable results.
Proposes Neural Complexity (NC) for predicting and explaining generalization in deep neural networks.
problem Challenges in specifying a suitable complexity measure for deep neural networks to predict and explain generalization.
method A meta-learning framework that learns a scalar complexity measure through interactions with many heterogeneous tasks.
result Trained NC model can be added to standard training loss to regularize any task learner.
HCBM improves deep learning explainability by non-linear concept aggregation.
problem Lack of explainable and accurate predictions in deep learning for high-stake decisions.
method Introduce Hoeffding Concept Bottleneck Models (HCBM) using Hoeffding functional decomposition of gradient-boosted trees for non-linear and sparse concept aggregation.
result HCBM outperforms standard linear CBM and is robust to interconcept leakage.
This paper analyzes generalization issues in deep reinforcement learning.
problem Understanding and improving generalization capabilities of deep reinforcement learning policies.
method Formalizing and categorizing solutions to address overfitting in deep reinforcement learning.
result A comprehensive analysis of generalization challenges and solutions in deep reinforcement learning.
Interactive framework improves understanding of deep neural networks.
problem False sense of comprehension from static explanation methods.
method Interactive framework allowing exhaustive inspection and testing of decisions.
result Interactive approach leads to better understanding of complex decision boundaries.
Deep neural networks are complex and opaque. As they enter application in a variety of important and safety critical domains, users seek methods to explain their output predictions. We develop an approach to explaining deep neural networks by constructing causal models on salient concepts contained in a CNN. We develop…
RSM provides insights into deep survival models' decision-making.
problem Ensuring trust in deep survival models' predictions for healthcare applications.
method Reverse survival model (RSM) framework that explains deep survival models' decisions.
result RSM extracts relevant features for deep survival models' predictions.
Self-explaining AI provides understandable explanations for AI decisions.
problem Difficulty in interpreting decisions made by deep neural networks, especially in critical applications.
method Introducing self-explaining AI that provides human-understandable explanations and confidence levels.
result Deep neural networks operate by interpolating between data points, making them hard to interpret.
Study introduces UEBA framework using Deep Autoencoders for anomaly detection.
problem Detecting security incidents in cybersecurity.
method Combines Deep Autoencoders with Doc2Vec for anomaly detection.
result Demonstrates effective detection of real and synthetic anomalies.
AGOP mechanism explains deep neural collapse in neural networks.
problem Explaining the rigid structure of data representations in deep neural networks.
method Introducing AGOP and Deep RFM to demonstrate DNC.
result AGOP mechanism causes deep neural collapse in neural networks.
The paper improves deep learning generalization bounds using PAC-Bayes compression.
problem Improving generalization bounds for deep neural networks.
method Quantizing neural network parameters in a linear subspace to develop tight generalization bounds.
result Large models can be compressed significantly, explaining Occam's razor.
The emergence of deep learning networks raises a need for explainable AI so that users and domain experts can be confident applying them to high-risk decisions. In this paper, we leverage data from the latent space induced by deep learning models to learn stereotypical representations or "prototypes" during training to…
SRHM explains deep learning's hierarchy and insensitivity to transformations.
problem Understanding how deep networks learn hierarchical and invariant representations.
method Introducing sparsity to generative hierarchical models of data.
result Hierarchical representations and insensitivity to transformations correlate strongly with deep network performance.
New RL algorithm explains why deep learning works in stochastic environments.
problem Why deep RL algorithms perform well in practice despite using random exploration.
method Introducing SQIRL, an iterative RL algorithm that separates exploration and learning.
result Effective horizon explains why deep RL works in stochastic environments.
New report on machine learning visualization techniques and trends.
problem Improving trust in machine learning models through visualization.
method Analysis of peer-reviewed articles on machine learning visualization techniques.
result Rapid growth in machine learning visualization techniques over the past three years.
Paper explains DRL strategies for portfolio management using linear models.
problem Difficulty in understanding DRL-based trading strategies.
method Empirical approach using linear models and integrated gradients.
result DRL agents show stronger multi-step prediction power than machine learning methods.
Method extracts time-localized clusters to explain deep learning models in ECG analysis.
problem Limited understanding of deep learning models in ECG analysis.
method Extracts time-localized clusters from model's internal representations.
result Enhances trust in AI-driven diagnostics and reveals clinically relevant patterns.
Self-supervised method improves CBIR of CT liver images.
problem Limited labeled data and lack of transparency in deep CBIR systems.
method Proposes a self-supervised learning framework with domain-knowledge integration.
result Improved performance and generalization across datasets.
Proposes HEX for human-in-the-loop explainability in ML models.
problem Ensuring accountability and reliability in ML models used for high-stakes decisions.
method Human-in-the-loop deep reinforcement learning approach to MLX.
result Synthesizes decision-specific policies from any classification model.
With the availability of large databases and recent improvements in deep learning methodology, the performance of AI systems is reaching or even exceeding the human level on an increasing number of complex tasks. Impressive examples of this development can be found in domains such as image classification, sentiment ana…
CONFETTI improves interpretability of deep learning models for MTS by providing counterfactual explanations.
problem Lack of transparency in deep learning models for multivariate time series classification.
method CONFETTI is a novel multi-objective counterfactual explanation method that balances prediction confidence, proximity, and sparsity.
result CONFETTI outperforms state-of-the-art methods in various metrics, improving interpretability and decision support.
The book explains deep learning theory and how networks learn nontrivial representations.
problem Understanding and optimizing deep neural networks.
method Developed RG flow to characterize signal propagation, solved layer-to-layer equations, and analyzed representation learning.
result Predictions of trained networks are nearly-Gaussian, with depth-to-width ratio controlling deviations.
RelEx explains relational models without gradient access.
problem Lack of explainability for relational models like GNNs and SRL.
method Model-agnostic explainer for relational models using only outputs.
result Comparable or better performance compared to GNN-Explainer.
Quant 4.0 uses AI to automate, explain, and incorporate knowledge in investment.
problem Limitations of deep learning in quant investment.
method Automated AI, Explainable AI, Knowledge-driven AI.
result Improves investment decision-making through automation, interpretability, and prior knowledge integration.
New AI technique explains neural net decisions over time.
problem Difficulty of explaining AI decisions in time series data.
method Proposes a novel XAI technique for deep learning methods.
result Preserves and exploits the natural time ordering of data.
This paper improves causal inference using deep neural networks for low-dimensional covariates.
problem Improving causal inference with deep learning for high-dimensional covariates.
method Doubly robust off-policy learning with deep neural networks on low-dimensional manifolds.
result Nonasymptotic regret bounds for finite- and continuous-action scenarios, converging at a fast rate depending on intrinsic manifold dimension.
Researchers use DL and XAI to evaluate climate downscaling models.
problem Evaluating complex DL models for climate downscaling.
method Intercompare DL models, expand standard evaluation methods with XAI.
result XAI techniques provide new evaluation dimensions and model insights.