When training data is sparse, more domain knowledge must be incorporated into the learning algorithm in order to reduce the effective size of the hypothesis space. This paper builds on previous work in which knowledge about qualitative monotonicities was formally represented and incorporated into learning algorithms (e…
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48 results for “knowledge incorporation”
Paper proposes a novel RRL framework that learns from images and incorporates expert knowledge.
problem Lack of effective methods to incorporate expert background knowledge and learn from non-relational data in RRL.
method Differentiable Inductive Logic Programming (ILP) for learning relational information from images and incorporating expert knowledge.
result Efficacy demonstrated on various environments and datasets, showing improved learning and generalization.
Survey of methods to incorporate external knowledge into stock price prediction.
problem Challenges in predicting stock prices due to market volatility and non-linearity.
method Survey of methods for acquiring and incorporating external knowledge into stock price prediction models.
result Systematic synthesis of previous studies on external knowledge types and their application in stock price prediction.
End-to-end framework learns precise disparity for activity recognition.
problem Precise portrayal of intraclass disparity in activity recognition.
method Knowledge-directed adversarial learning framework with two competitive encoding distributions.
result Robust and generalizable performance on HAR benchmark datasets.
This paper proposes a systematic framework to design a classification model that yields a classifier which optimizes a utility function based on prior knowledge. Specifically, as the data size grows, we prove that the produced classifier asymptotically converges to the optimal classifier, an extended version of the Bay…
Optimizes expensive experiments by incorporating expert knowledge.
problem Expensive experiments require minimizing the number of trials.
method Bayesian optimization with posterior sampling of expert knowledge.
result Demonstrates significant efficiency gains in experiments and hyperparameter tuning.
Improves transparency and incorporates prior knowledge in Gaussian Process models.
problem Challenges in understanding and expressing prior assumptions in complex Bayesian models.
method Introduces self-explaining variational posterior distributions for Gaussian Processes.
result Allows incorporation of both general and feature-specific prior knowledge.
In this paper we develop a novel neural network model for predicting implied volatility surface. Prior financial domain knowledge is taken into account. A new activation function that incorporates volatility smile is proposed, which is used for the hidden nodes that process the underlying asset price. In addition, fina…
Attention-based encoder-decoder neural network models have recently shown promising results in goal-oriented dialogue systems. However, these models struggle to reason over and incorporate state-full knowledge while preserving their end-to-end text generation functionality. Since such models can greatly benefit from us…
Machine learning improves planetary space physics by incorporating physical knowledge.
problem Improving performance and interpretability of machine learning models for planetary space physics.
method Building on a previous semi-supervised physics-based classification, the team used varying data and physical information to improve machine learning performance and interpretability.
result Incorporating physical knowledge improves machine learning performance and interpretability, essential for deriving scientific meaning.
A new method converts neural networks to function space for scalable sequential learning.
problem Challenges in gradient-based deep learning for sequential data.
method Dual parameterization of neural networks from weight to function space.
result Efficient scaling, knowledge retention, and new data incorporation.
There is a significant literature on methods for incorporating knowledge into multiple testing procedures so as to improve their power and precision. Some common forms of prior knowledge include (a) beliefs about which hypotheses are null, modeled by non-uniform prior weights; (b) differing importances of hypotheses, m…
Framework incorporates prior knowledge into Bayesian models for data streams.
problem Effective use of prior knowledge in learning Bayesian models from streaming data.
method Proposes a novel framework that subsumes existing models for time-series data.
result Framework outperforms existing methods with a large margin.
Bayesian neural networks incorporate domain knowledge through variational inference.
problem Specifying priors for Bayesian neural networks that capture domain knowledge is challenging.
method Proposes a framework for integrating domain knowledge into BNN priors through variational inference.
result BNNs with proposed domain knowledge priors outperform those with standard priors, achieving better predictive performance.
Prior domain knowledge can greatly help to learn generative models. However, it is often too costly to hard-code prior knowledge as a specific model architecture, so we often have to use general-purpose models. In this paper, we propose a method to incorporate prior knowledge of feature relations into the learning of g…
Incorporation of a new knowledge into neural networks with simultaneous preservation of the previous one is known to be a nontrivial problem. This problem becomes even more complex when new knowledge is contained not in new training examples, but inside the parameters (connection weights) of another neural network. Her…
Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentationcs.LG
Proposes a method to use causal graph knowledge for better predictive modeling.
problem Lack of effective ways to incorporate causal graph knowledge into predictive models.
method Model-agnostic data augmentation method exploiting CI relations encoded in causal graphs.
result Improves prediction accuracy, especially in small-data scenarios.
Recent advances in analysis of subband amplitude envelopes of natural sounds have resulted in convincing synthesis, showing subband amplitudes to be a crucial component of perception. Probabilistic latent variable analysis is particularly revealing, but existing approaches don't incorporate prior knowledge about the ph…
New LSTM scheme incorporates prior knowledge and measurement uncertainties.
problem Overfitting and insufficient data for accurate time-dependent solutions.
method Sparse Bayesian training algorithm for automatic connection determination.
result Less prone to overfitting, smaller data set required for satisfying accuracy.
Knowledge graphs, on top of entities and their relationships, contain other important elements: literals. Literals encode interesting properties (e.g. the height) of entities that are not captured by links between entities alone. Most of the existing work on embedding (or latent feature) based knowledge graph analysis …
Bayesian neural networks improve with summary information and Dirichlet process.
problem Lack of prior knowledge in BNNs for complex architectures.
method Incorporates external summary information about predicted probabilities using a Dirichlet process.
result Improves model accuracy, uncertainty calibration, and robustness.
Novel framework for Bayesian neural networks incorporating task-specific constraints.
problem Task-specific constraints in supervised model deployment.
method Introduces Output-Constrained BNN (OC-BNN) framework.
result OC-BNNs effectively incorporate prior expert knowledge and desiderata like safety and fairness.
PKB framework boosts genomic data analysis by integrating pathway knowledge.
problem Boosting discovery power and connecting new findings with biological mechanisms in genomic data.
method Pathway-based Kernel Boosting (PKB) framework integrating clinical and pathway information for prediction of various outcomes.
result PKB substantially outperforms other methods in predicting drug response and cancer survival.
Predicting the efficacy of a drug for a given individual, using high-dimensional genomic measurements, is at the core of precision medicine. However, identifying features on which to base the predictions remains a challenge, especially when the sample size is small. Incorporating expert knowledge offers a promising alt…
The promise of ANNs to automatically discover and extract useful features/patterns from data without dwelling on domain expertise although seems highly promising but comes at the cost of high reliance on large amount of accurately labeled data, which is often hard to acquire and formulate especially in time-series doma…
Method uses network biology to construct gene expression models for cancer.
problem Building models for cancer phenotypes using gene expression data.
method Unsupervised construction of computational graphs based on protein-protein networks.
result The method outperforms other models in cancer phenotype analysis.
New Gromov-Wasserstein metric controls rigidity and incorporates prior knowledge.
problem Inflexible Gromov-Wasserstein distance and lack of feature alignment.
method Augmented Gromov-Wasserstein distance with feature alignments and prior knowledge.
result Improved performance in single-cell multi-omic alignment and transfer learning.
Proposes SPCA to incorporate structural constraints in model identification.
problem Model identification with partial structural knowledge.
method Structural Principal Component Analysis (SPCA) that leverages structural information.
result Demonstrates improved model estimates using synthetic and industrial data.
Incorporating domain knowledge into the modeling process is an effective way to improve learning accuracy. However, as it is provided by humans, domain knowledge can only be specified with some degree of uncertainty. We propose to explicitly model such uncertainty through probabilistic constraints over the parameter sp…
New method simplifies causal inference with tiered background knowledge.
problem Large equivalence classes of DAGs limit causal information.
method Integrates tiered background knowledge to create 'tiered MPDAGs' with simplified structure.
result Tiered MPDAGs are chain graphs with chordal components, simplifying causal effect estimation.
Bayesian framework integrates prior and data knowledge for nonlinear dynamical systems.
problem Fusing diverse prior knowledge with data for accurate model learning.
method General-purpose Bayesian inference and learning framework combining explicit and implicit prior knowledge.
result Efficient parameter marginalization and closed-form densities for online and offline inference.
Bayesian framework for sequential learning tasks with low-rank approximations.
problem Balancing knowledge retention and adaptability in sequential neural networks.
method Bayesian framework with diagonal plus low-rank approximations of the precision matrix.
result Unlocking capabilities to encode task relationships and incorporate prior knowledge from later tasks.
Cluster-DAGs improve causal discovery with prior knowledge.
problem Finding cause-effect relationships from high-dimensional data.
method Cluster-DAGs as prior knowledge framework, modified constraint-based algorithms Cluster-PC and Cluster-FCI.
result Cluster-PC and Cluster-FCI outperform baselines without prior knowledge.
ColaBO accelerates optimization with user beliefs.
problem Optimizing expensive functions with limited prior knowledge.
method General Bayesian framework for incorporating user beliefs.
result Significant optimization acceleration with accurate prior information.
Extends Demographic Parity for fairer wage predictions with expert knowledge.
problem Inadequate fairness metrics limit user domain knowledge and ignore intersectional fairness.
method Develops a parametric method to incorporate expert knowledge in fair predictions.
result Offers a robust solution for real-life applications with limited data and spending constraints.
This paper enhances language models with knowledge awareness.
problem Understanding how much knowledge pretrained language models grasp.
method Inserting explicit knowledge layers into pretraining without changing transformer architecture.
result Significantly more knowledge packed into transformer parameters.
Unsupervised relation discovery aims to discover new relations from a given text corpus without annotated data. However, it does not consider existing human annotated knowledge bases even when they are relevant to the relations to be discovered. In this paper, we study the problem of how to use out-of-relation knowledg…
By elaborating on the notion of linear belief functions (Dempster 1990; Liu 1996), we propose an elementary approach to knowledge representation for expert systems using linear belief functions. We show how to use basic matrices to represent market information and financial knowledge, including complete ignorance, stat…
Semantic TrueLearn uses semantic graphs to improve educational recommendation systems.
problem Challenges in handling semantic and hierarchical structure in knowledge areas.
method Introduces a novel learner model that exploits semantic relatedness between knowledge components using a Wikipedia link graph.
result Achieves statistically significant improvements in predictive performance for educational engagement.
In this work, we propose a subspace-based algorithm for DOA estimation which iteratively reduces the disturbance factors of the estimated data covariance matrix and incorporates prior knowledge which is gradually obtained on line. An analysis of the MSE of the reshaped data covariance matrix is carried out along with c…
Pykg2vec is an open-source Python library for learning the representations of the entities and relations in knowledge graphs. Pykg2vec's flexible and modular software architecture currently implements 16 state-of-the-art knowledge graph embedding algorithms, and is designed to easily incorporate new algorithms. The goa…
We consider classification tasks in the regime of scarce labeled training data in high dimensional feature space, where specific expert knowledge is also available. We propose a new hybrid optimization algorithm that solves the elastic-net support vector machine (SVM) through an alternating direction method of multipli…
While generative models such as Latent Dirichlet Allocation (LDA) have proven fruitful in topic modeling, they often require detailed assumptions and careful specification of hyperparameters. Such model complexity issues only compound when trying to generalize generative models to incorporate human input. We introduce …
While physics conveys knowledge of nature built from an interplay between observations and theory, it has been considered less importantly in deep neural networks. Especially, there are few works leveraging physics behaviors when the knowledge is given less explicitly. In this work, we propose a novel architecture call…
The paper improves Gaussian processes by adding sum constraints, enhancing prediction accuracy.
problem Improving Gaussian process predictions with background knowledge constraints.
method Conditioning the prior distribution on sum constraints to ensure fulfillment of linear and nonlinear constraints.
result The approach fulfills constraints with high precision and improves prediction accuracy.
Paper proposes AI for stock market forecasting using external knowledge.
problem Forecasting stock prices influenced by external factors.
method Learning from historical data and external temporal knowledge graphs modeled as Hawkes processes.
result Dynamic representations effectively rank stocks based on returns.
RID framework quantifies and regularizes task-relevant knowledge in distillation.
problem Distilling irrelevant information can hinder student model performance.
method Partial Information Decomposition to quantify and regularize task-relevant knowledge.
result RID framework leads to more resilient distillation under nuisance teachers.
Introduces a rule-based Bayesian regression for better uncertainty quantification and expert knowledge integration.
problem Handling regression problems with uncertainty quantification and expert intuition.
method Combines Bayesian inference and rule-based systems for better model performance.
result Improves model performance with better uncertainty quantification and point predictions.