This paper presents a basic property of region dividing of ReLU (rectified linear unit) deep learning when new layers are successively added, by which two new perspectives of interpreting deep learning are given. The first is related to decision trees and forests; we construct a deep learning structure equivalent to a …
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48 results for “deep-learning”
The great success of deep learning shows that its technology contains profound truth, and understanding its internal mechanism not only has important implications for the development of its technology and effective application in various fields, but also provides meaningful insights into the understanding of human brai…
Improves deep learning robustness by considering task and model.
problem Adversarial attacks on deep learning systems.
method Binary and interval label encoding strategy to redefine classification tasks and design corresponding loss functions.
result Our method enhances robustness without sacrificing accuracy.
Deep learning is increasingly being used in high-stake decision making applications that affect individual lives. However, deep learning models might exhibit algorithmic discrimination behaviors with respect to protected groups, potentially posing negative impacts on individuals and society. Therefore, fairness in deep…
NeurIPS 2020 competition seeks to predict deep learning generalization.
problem Understanding and predicting generalization in deep learning models.
method Propose complexity measures to accurately predict generalization performance.
result A robust complexity measure could improve deep learning reliability.
Asset Pricing and Deep Learningq-fin.ST
Deep learning improves asset pricing and risk premium measurement.
problem Improving asset pricing and risk premium measurement using deep learning.
method Investigates various deep learning methods for asset pricing, especially for risk premia measurement.
result RNNs with memory mechanism and attention have the best performance in terms of predictivity.
Deep Learning Meets SAReess.IV
Deep learning applied to SAR data is explored in this paper.
problem Limited use of deep learning in SAR data processing.
method Introduction to relevant deep learning models, analysis of SAR data characteristics, review of state-of-the-art applications, and future research directions.
result Unlocking the potential of deep learning in SAR data processing.
Paper explores physics-informed deep learning for system reliability assessment.
problem Limited study on deep learning for system reliability assessment.
method Physics-informed deep learning approach for system reliability assessment.
result Physics-informed deep learning can alleviate computational challenges and combine measurement data and mathematical models.
Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence research. It is also one of the most popular scientific research trends now-a-days. Deep learning methods have brought revolutionary advances in computer vision and machine learning. Every now and then, new and new deep learning t…
Bayesian deep learning improves deep learning's capabilities across diverse settings.
problem Overlooked metrics, tasks, and data types in deep learning.
method Revisits strengths of Bayesian deep learning and addresses challenges.
result Bayesian deep learning can elevate deep learning's capabilities across diverse settings.
Deep learning methods are reviewed for preserving structure in neural networks.
problem Challenges in applying deep learning, especially in preserving structure.
method Review of existing deep learning methods and new algorithmic frameworks.
result Mathematical understanding and systematic design of deep learning methods to preserve structure.
Multimodal deep learning improves flaw detection in software programs.
problem Current flaw detection relies on single software representations.
method Adapted multimodal deep learning models for flaw detection.
result Multimodal models outperform traditional deep learning models.
Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results. Along with the success of deep learning in many other application domains, deep learning is also popularly used in sentiment analysi…
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.
Deep learning has arguably achieved tremendous success in recent years. In simple words, deep learning uses the composition of many nonlinear functions to model the complex dependency between input features and labels. While neural networks have a long history, recent advances have greatly improved their performance in…
Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have …
Quantile deep learning improves time series prediction accuracy and uncertainty quantification.
problem Uncertainty in multi-step time series prediction.
method Developed a novel quantile regression deep learning framework for multi-step time series prediction.
result Integrating quantile loss function with deep learning provides additional predictions for selected quantiles without loss in accuracy.
In this paper, a detailed study on crime classification and prediction using deep learning architectures is presented. We examine the effectiveness of deep learning algorithms on this domain and provide recommendations for designing and training deep learning systems for predicting crime areas, using open data from pol…
Study improves theoretical understanding of Bayesian deep learning for classification tasks.
problem Theoretical gap in understanding Bayesian approaches in deep learning for classification.
method PAC-Bayes bounds techniques and Spike-and-Slab priors for sparse deep learning.
result Established non-asymptotic results for prediction error, achieving minimax optimal rates.
Paper introduces privacy-preserving deep-learning service.
problem Privacy loss in deep-learning-as-a-service.
method Homomorphic Encryption tailored for CNNs.
result Effectiveness of proposed privacy-preserving architecture.
Statistical field theory aids in understanding deep learning complexities.
problem Complexity and lack of theoretical understanding in deep learning.
method Statistical field theory as a theoretical framework.
result Field theory provides insights into generalization, bias, and feature learning.
Book introduces deep learning methods with math, theory, and applications.
problem Understanding deep learning algorithms and their mathematical foundations.
method Reviews various ANN architectures and optimization methods, covers theoretical aspects.
result Provides a solid mathematical foundation for deep learning.
An overview of the applications of deep learning in ophthalmic diagnosis using retinal fundus images is presented. We also review various retinal image datasets that can be used for deep learning purposes. Applications of deep learning for segmentation of optic disk, blood vessels and retinal layer as well as detection…
Deep learning aids causal inference in complex settings.
problem Estimating heterogeneous treatment effects in non-linear, time-varying, and encoded confounders.
method Intuitive introduction to deep learning and causal inference, focusing on observational data.
result Maximizes accessibility to causal inference through deep learning.
Deep learning improves option pricing for a non-martingale asset model.
problem Computing call option prices for the Barndorff-Nielsen and Shephard model with infinite jumps.
method Developed a supervised deep-learning scheme using Monte Carlo teaching data and a Black-Scholes-derived variable.
result Significant improvement in accuracy of option pricing.
Deep learning models outperform classical methods in text classification.
problem Improving text classification accuracy using deep learning.
method Comprehensive review of deep learning models and datasets for text classification.
result Deep learning models outperform classical methods on various text classification tasks.
Deep learning models viewed through tame geometry for convergence guarantees.
problem Understanding convergence guarantees in deep learning models.
method Introducing tame geometry concepts and tools for nonsmooth nonconvex settings.
result Illustrates tame geometry as a natural framework for AI systems, especially deep learning.
Survey of deep learning methods for time series forecasting.
problem Improving accuracy in time series predictions across various domains.
method Analysis of common encoder and decoder designs, hybrid models, and decision support.
result Advancements in deep learning for time series forecasting.
Deep learning has sparked a network of mutual interactions between different disciplines and AI. Naturally, each discipline focuses and interprets the workings of deep learning in different ways. This diversity of perspectives on deep learning, from neuroscience to statistical physics, is a rich source of inspiration t…
Why and how that deep learning works well on different tasks remains a mystery from a theoretical perspective. In this paper we draw a geometric picture of the deep learning system by finding its analogies with two existing geometric structures, the geometry of quantum computations and the geometry of the diffeomorphic…
Geometric deep learning predicts knot invariants.
problem Predicting knot invariants from knot data.
method Constructing a functor from knots to graphs and using graph neural networks.
result High generalization capabilities demonstrated.
This paper surveys gradient-based multi-objective deep learning methods.
problem Balancing multiple conflicting objectives in deep learning models.
method Gradient-based techniques adapted from Multi-Objective Optimization.
result Comprehensive survey of gradient-based multi-objective deep learning algorithms.
In recent years, China, the United States and other countries, Google and other high-tech companies have increased investment in artificial intelligence. Deep learning is one of the current artificial intelligence research's key areas. This paper analyzes and summarizes the latest progress and future research direction…
Proposes model-based robust deep learning to handle natural variation in data.
problem Deep learning's fragility to natural variation in data.
method Develops model-based robust training algorithms using deep generative models to learn natural variation.
result Deep neural networks trained with model-based algorithms outperform standard and norm-bounded robust algorithms.
Develops a KL-divergence-based deep learning method for survival analysis with short data.
problem Challenges of training deep learning models with limited survival data.
method Kullback-Leibler (KL) divergence to integrate external and internal data.
result Proposed model achieves better performance and higher robustness.
Deep learning methods are widely regarded as indispensable when it comes to designing perception pipelines for autonomous agents such as robots, drones or automated vehicles. The main reasons, however, for deep learning not being used for autonomous agents at large scale already are safety concerns. Deep learning appro…
Deep Learning techniques have achieved remarkable results in many domains. Often, training deep learning models requires large datasets, which may require sensitive information to be uploaded to the cloud to accelerate training. To adequately protect sensitive information, we propose distributed layer-partitioned train…
In this article we review computational aspects of Deep Learning (DL). Deep learning uses network architectures consisting of hierarchical layers of latent variables to construct predictors for high-dimensional input-output models. Training a deep learning architecture is computationally intensive, and efficient linear…
This paper reviews methods for interpreting deep learning models with sequential data.
problem Limited interpretability of deep learning models in sequential data domains.
method Reviews and compares techniques for sequential interpretability.
result Current techniques have limitations and future research is needed.
New framework tackles deep learning issues like local traps and miscalibration.
problem Local traps and miscalibration in deep neural networks.
method Sparse deep learning framework with prior annealing algorithms.
result Proposed method successfully addresses local traps and miscalibration.
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.
Deep learning predicts breast cancer with high accuracy from patient data.
problem Early detection of breast cancer from patient data.
method Feature selection and k-fold Monte Carlo cross-validation using deep learning.
result Deep learning model effectively distinguishes between cancer and healthy patients.
Deep learning uncovers patterns between knot types.
problem Discovering connections between combinatorial and hyperbolic knot invariants.
method Statistical approach using linear regression and deep learning.
result Revealed empirical connections between knot types.
Review of priors in Bayesian deep learning models.
problem The importance of prior choices in Bayesian deep learning models.
method Overview of different priors and methods of learning priors from data.
result Motivate practitioners to think carefully about prior specification.
Introduces topological deep learning for neural network classification problems.
problem Classifying neural networks using minimal topological structures.
method Formalizes classification problems in a topological setting.
result Demonstrates conditions for the feasibility of classification problems in neural networks.
Deep learning methods improve subsurface flow modeling efficiency.
problem Efficiently modeling subsurface flow with uncertain parameters.
method Two categories of deep-learning based inverse modeling methods: surrogate-based and direct.
result Deep-learning methods significantly accelerate subsurface flow modeling.
Deep learning models improve financial price forecasting accuracy.
problem Accurately predicting financial time series prices.
method Review of recent advancements in deep learning models for price forecasting.
result Deep learning models outperform traditional methods in financial price forecasting.
Study shows simple vector quantization measures correlate with deep learning generalization.
problem Understanding and predicting generalization in deep learning models.
method Applying complexity measures from approximation and information theory to deep learning features.
result Simple vector quantization measures correlate well with generalization performance in deep learning.