A review of some techniques for inclusion of domain-knowledge into deep neural networks

Dash, Tirtharaj; Chitlangia, Sharad; Ahuja, Aditya; Srinivasan, Ashwin

doi:10.1038/s41598-021-04590-0

Cited by 196Neural Networks and ApplicationsDomain Adaptation and Few-Shot LearningModel Reduction and Neural Networks

A review of some techniques for inclusion of domain-knowledge into deep neural networks

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Dash, Tirtharaj, Chitlangia, Sharad, Ahuja, Aditya, Srinivasan, Ashwin·2022·Scientific Reports

DOI 10.1038/s41598-021-04590-0PMC PMC8776800

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Abstract

We present a survey of ways in which existing scientific knowledge are included when constructing models with neural networks. The inclusion of domain-knowledge is of special interest not just to constructing scientific assistants, but also, many other areas that involve understanding data using human-machine collaboration. In many such instances, machine-based model construction may benefit significantly from being provided with human-knowledge of the domain encoded in a sufficiently precise form. This paper examines the inclusion of domain-knowledge by means of changes to: the input, the loss-function, and the architecture of deep networks. The categorisation is for ease of exposition: in practice we expect a combination of such changes will be employed. In each category, we describe techniques that have been shown to yield significant changes in the performance of deep neural networks.

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Integrating external domain knowledge into emotion recognition models is usually a complex and time-consuming process.
“However, integrating external domain knowledge into emotion recognition models is usually a complex and timeconsuming process [19].”
From Emotion Classification With Visibility Graphs (2025) · cites this paper as [19]
The Introduction (‘Focus of the paper’; page not provided) describes increasing complexity in ways of incorporating domain knowledge, and ‘Challenges and concluding remarks’ identifies technical difficulties. The passages do not establish that integration is usually time-consuming or address emotion recognition models specifically. (LLM, conf=0.84)
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Method
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BackgroundSelf-citation
DreamCoder: growing generalizable, interpretable knowledge with wake–sleep Bayesian program learning
Ellis, K., Wong, L., Nye, M., Sablé-Meyer, M., Cary, L., Anaya Pozo, L., Hewitt, L., Solar-Lezama, A., & Tenenbaum, J. B. (2023) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2022.0050 · Cited by 125 · cited 2× · see citing works
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From Statistical Relational to Neuro-Symbolic Artificial Intelligence
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Method
Improving the Interpretability of Deep Neural Networks with Knowledge Distillation
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Integrating Prior Knowledge into Deep Learning
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Commonsense Knowledge in Machine Intelligence
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Background
TensorLog: A Probabilistic Database Implemented Using Deep-Learning Infrastructure
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Riegel, R., Gray, A., Luus, F., Khan, N., Makondo, N., Akhalwaya, I. Y., Qian, H., Fagin, R., Barahona, F., Sharma, U., et al. (2020) · arXiv · doi:10.48550/arxiv.2006.13155 · Cited by 95 · cited 1× · see citing works
“Template-based construction of deep network structure can be also seen in Logical Neural Networks (LNNs: [102]).”
Domain Knowledge Guided Deep Learning with Electronic Health Records
Yin, C., Zhao, R., Qian, B., Lv, X., & Zhang, P. (2019) · Proceedings · doi:10.1109/icdm.2019.00084 · Cited by 83 · cited 2× · see citing works
“…Approach Work (Reference) Type of Learner Transforming Data DRM [24; 25] MLP CILP++ [28] MLP R-GCN [46] GNN KGCN [48] GNN KBRD [49] GNN DG-RNN [44] RNN DreamCoder [32] DNN ∗ Gated-K-BERT [38] Transformer VEGNN [5] GNN BotGNN [6] GNN KRISP [45] GNN, Transformer Transforming Loss IPKFL [78] CNN…”
Knowledge Graphs and Knowledge Networks: The Story in Brief
Sheth, A., Padhee, S., & Gyrard, A. (2019) · IEEE Internet Computing · doi:10.1109/mic.2019.2928449 · Cited by 82 · cited 1× · see citing works
“Knowledge graphs have been used successfully in variety of problems arising in information processing domains such as search, recommendation, summarisation [37].”
Turning 30: New Ideas in Inductive Logic Programming
Cropper, A., Dumančić, S., & Muggleton, S. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/673 · Cited by 78 · cited 2× · see citing works
“Techniques for automatic construction of Boolean-valued features from relational domain-knowledge have a long history in the ﬁeld of ILP [ 15; 16; 17 ] , originating from the LINUS [ 7 ] .”
Background
Augmenting Neural Networks with First-order Logic
Li, T., & Srikumar, V. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1028 · Cited by 78 · see citing works
Metamathematics of fuzzy logic
Höhle, U. (2002) · Fuzzy Sets and Systems · doi:10.1016/s0165-0114(02)00356-1 · Cited by 74 · see citing works
Lifted Relational Neural Networks: Efficient Learning of Latent Relational Structures
Šír, G., Aschenbrenner, V., Železný, F., Schockaert, S., & Kuželka, O. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11203 · Cited by 73 · cited 1× · see citing works
“However, it requires the use of a language of Lifted Relational Neural Networks (LRNNs) [101].”
Method
DL2: Training and Querying Neural Networks with Logic.
Fischer, M., Balunović, M., Drachsler-Cohen, D., Gehr, T., Zhang, C., & Vechev, M. (2019) · International Conference on Machine Learning · Cited by 67 · see citing works
Shades of Knowledge-Infused Learning for Enhancing Deep Learning
Sheth, A., Gaur, M., Kursuncu, U., et al. (2019) · IEEE Internet Computing · doi:10.1109/mic.2019.2960071 · Cited by 65 · cited 1× · see citing works
“Incorporation of the information in a knowledge-graph into deep neural models–termed “knowledge-infused learning”–is described in [Kursuncu et al., 2019; Sheth et al., 2019].”
Low-Dimensional Embeddings of Logic
Rocktäschel, T., Bošnjak, M., Singh, S., & Riedel, S. (2014) · Annual Meeting of the Association for Computational Linguistics · doi:10.3115/v1/w14-2409 · Cited by 62 · cited 1× · see citing works
“[55] was perhaps the ﬁrst method of regularising embeddings from declarative knowledge encoded in ﬁrst-order logic.”
Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep LearningPreprint
Kursuncu, U., Gaur, M., & Sheth, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.00512 · Cited by 51 · cited 3× · see citing works
“Incorporation of the information in a knowledge-graph into deep neural models–termed “knowledge-infused learning”–is described in [40; 41].”
Influential
Embedding Symbolic Knowledge into Deep NetworksPreprint
Xie, Y., Xu, Z., Kankanhalli, M., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.01161 · Cited by 51 · cited 2× · see citing works
“KBANN [Towell and Shavlik, 1994] Propositional logic MLP Cascade-ARTMAP [Tan, 1997] Propositional logic ARTMAP CILP [Avila Garcez and Zaverucha, 1999] Propositional logic RNN LENSR [Xie et al., 2019] Canonical Normal Form Loss function GNN CILP++ [França et al.”
kLog: A language for logical and relational learning with kernels
Frasconi, P., Costa, F., Raedt, L. D., & Grave, K. D. (2014) · Artificial Intelligence · doi:10.1016/j.artint.2014.08.003 · Cited by 48 · cited 1× · see citing works
“For instance, in kLog [97] the transformed representation is an undirected bipartite graph in the form of ‘Probabilistic Entity-Relationship model’ [98] which allows the use of a graph-kernel [99] for data classiﬁcation purpose, where each data instance is represented as a logic program constructed…”
Method
DeepStochLog: Neural Stochastic Logic Programming
Winters, T., Marra, G., Manhaeve, R., & Raedt, L. D. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i9.21248 · Cited by 44 · cited 3× · see citing works
“A recent approach called DeepStochLog [94] is a framework that extends the idea of neural predicates in DeepProbLog to deﬁ-nite clause grammars [96].”
Incorporating Prior Knowledge on Features into Learning
Krupka, E., & Tishby, N. (2007) · Cited by 43 · see citing works
Generative Adversarial Network-Based Transfer Reinforcement Learning for Routing With Prior Knowledge
Dong, T., Qi, Q., Wang, J., Liu, A. X., Sun, H., Zhuang, Z., & Liao, J. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3077249 · Cited by 40 · cited 1× · see citing works
“In [35], for example, a feature-encoder ensures that the feature representation for the target domain that is the same as the one used for the source.”
MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks
Hoernle, N., Karampatsis, R.-M., Belle, V., & Gal, K. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i5.20512 · Cited by 35 · cited 3× · see citing works
“One challenge that is pointed out in a recent study [74] is that incorporating domain-knowledge in this manner (that is: adding a domain-based loss to the standard problem-speciﬁc loss) may not always be suitable while dealing with safety-critical domains where 100% constraint satisfaction is…”
Influential
Propositionalization and embeddings: two sides of the same coin
Nada Lavrač, A., Škrlj, B., & Robnik-Šikonja, M. (2020) · Machine Learning · doi:10.1007/s10994-020-05890-8 · Cited by 31 · cited 1× · see citing works
“There is a long history of propositionalisation: see [18] for a review of some of early use of this technique, and [19; 20] who examine the links between propositionalisation and modern-day use of embeddings in neural networks.”
Toward Greater Intelligence in Route Planning: A Graph-Aware Deep Learning Approach
Zhuang, Z., Wang, J., Qi, Q., Sun, H., & Liao, J. (2019) · IEEE Systems Journal · doi:10.1109/jsyst.2019.2922217 · Cited by 30 · see citing works
Combining Prior Symbolic Knowledge and Constructive Neural Network Learning
Fletcher, J., & OBRADOVICgrave, Z. (1993) · Connection Science · doi:10.1080/09540099308915705 · Cited by 28 · cited 1× · see citing works
“Some of the limitations described above could be overcome with the proposal of a hybrid system by Fletcher and Obradovic [ 88 ] .”
Background
Incorporating symbolic domain knowledge into graph neural networks
Dash, T., Srinivasan, A., & Vig, L. (2021) · Machine Learning · doi:10.1007/s10994-021-05966-z · Cited by 27 · cited 5× · see citing works
“The results reported in [5] show signiﬁcant improvements in the predictive accuracy of GNNs across a large number datasets.”
BackgroundMethodResultInfluentialSelf-citation
Augmenting Neural Networks with First-order LogicPreprint
Li, T., & Srikumar, V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.06298 · Cited by 27 · see citing works
Deep Relational Machines
Lodhi, H. M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-42042-9_27 · Cited by 22 · cited 2× · see citing works
“Secondly, it can provide concepts that are meaningful for use Principal Approach Work (Reference) Type of Learner Transforming Data DRM [24; 25] MLP CILP++ [28] MLP R-GCN [46] GNN KGCN [48] GNN KBRD [49] GNN DG-RNN [44] RNN DreamCoder [32] DNN ∗ Gated-K-BERT [38] Transformer VEGNN [5] GNN BotGNN…”
Large-Scale Assessment of Deep Relational Machines
Dash, T., Srinivasan, A., Vig, L., Orhobor, O. I., & King, R. D. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-99960-9_2 · Cited by 19 · cited 3× · see citing works
“Conceptually, there is no variable-sharing between two or more ﬁrst-order logic features [25].”
BackgroundMethodSelf-citation
Introduction to knowledge-based neural networks
Fu, L. (1995) · Knowledge-Based Systems · doi:10.1016/0950-7051(96)81914-9 · Cited by 19 · cited 1× · see citing works
“[Fu, 1995] was one of the earliest works in this domain, in which they proposed a strategy to establish constraints by designating each node in a Hopfield Net to represent a concept and edges to represent their relationships and learn these nets by finding the solution which maximises the greatest number of these cons…”
Knowledge Graphs to Empower Humanity-Inspired AI Systems
Purohit, H., Shalin, V. L., & Sheth, A. (2020) · IEEE Internet Computing · doi:10.1109/mic.2020.3013683 · Cited by 19 · cited 1× · see citing works
“A knowledge graph provides a structure representation for knowledge that is accessible to both humans and machines [36].”
Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment
Dash, T., Srinivasan, A., & Baskar, A. (2021) · Machine Learning · doi:10.1007/s10994-021-06090-8 · Cited by 18 · cited 3× · see citing works
“…CILP++ [28] MLP R-GCN [46] GNN KGCN [48] GNN KBRD [49] GNN DG-RNN [44] RNN DreamCoder [32] DNN ∗ Gated-K-BERT [38] Transformer VEGNN [5] GNN BotGNN [6] GNN KRISP [45] GNN, Transformer Transforming Loss IPKFL [78] CNN ILBKRME [30] MLP HDNNLR [64] CNN, RNN SBR [68] MLP SBR [69] CNN DL2 [ 66 ] CNN…”
BackgroundMethodInfluentialSelf-citation
Beyond graph neural networks with lifted relational neural networks
Šír, G., Železný, F., & Kuželka, O. (2021) · Machine Learning · doi:10.1007/s10994-021-06017-3 · Cited by 18 · cited 6× · see citing works
“The intuition is that the nodes (in a problem involving recommender systems, as in [48], a node represents an entity) in the knowledge-graph can be represented with an aggregated neighbourhood information with bias while adopting the central idea of aggregate-and-combine in GNNs.”
Influential
Logical Explanations for Deep Relational Machines Using Relevance InformationPreprint
Srinivasan, A., Vig, L., & Bain, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.00595 · Cited by 17 · see citing works
Self-citation
Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions?
Zhao, J., Khashabi, D., Khot, T., Sabharwal, A., & Chang, K.-W. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.364 · Cited by 16 · cited 1× · see citing works
“While the precision of these formal representations may continue to be needed for the construction of scientific assistants, the flexibility of natural language may be especially useful in communicating commonsense knowledge to day-to-day machine assistants that need to an informal knowledge of the world [110; 111].”
Background
“When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware attention framework for relationship extraction
Yadav, S., Lokala, U., Daniulaityte, R., Thirunarayan, K., Lamy, F. R., & Sheth, A. (2021) · PLoS ONE · doi:10.1371/journal.pone.0248299 · Cited by 12 · cited 3× · see citing works
“…Transforming Data DRM [24; 25] MLP CILP++ [28] MLP R-GCN [46] GNN KGCN [48] GNN KBRD [49] GNN DG-RNN [44] RNN DreamCoder [32] DNN ∗ Gated-K-BERT [38] Transformer VEGNN [5] GNN BotGNN [6] GNN KRISP [45] GNN, Transformer Transforming Loss IPKFL [78] CNN ILBKRME [30] MLP HDNNLR [64] CNN, RNN SBR…”
An Investigation into the Role of Domain-Knowledge on the Use of Embeddings
Vig, L., Srinivasan, A., Bain, M., & Verma, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-78090-0_12 · Cited by 9 · cited 1× · see citing works
“There is a long history of propositionalisation: see [ 18 ] for a review of some of early use of this technique, and [ 19; 20 ] who examine the links between propositionalisation and modern-day use of embed-dings in neural networks.”
BackgroundSelf-citation
Neuro-Symbolic Inductive Logic Programming with Logical Neural NetworksPreprint
Sen, P., Carvalho, B. W., Riegel, R., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.03324 · Cited by 9 · cited 2× · see citing works
“LNNs resemble a tree structure where the leaf nodes represent the facts in the data and background knowledge, the internal nodes implement logical connectives (and, or, implication, etc.) using t -norm operators derived from real-valued logic, and the outputs represent the rules [103].”
Analogical Reasoning With Deep Learning-Based Symbolic Processing
Honda, H., & Hagiwara, M. (2021) · IEEE Access · doi:10.1109/access.2021.3109443 · Cited by 8 · cited 2× · see citing works
“…CNN Semantic Loss [ 63 ] CNN LENSR [ 91 ] GNN DANN [ 67 ] MLP PC-LSTM [ 72 ] RNN DomiKnowS [ 73 ] DNN* MultiplexNet [ 74 ] MLP, CNN Analogy Model [ 75 ] RNN Transforming Model KBANN [ 86 ] MLP Cascade-ARTMAP [ 89 ] ARTMAP CIL 2 P [ 29 ] RNN DeepProbLog [ 93 ] CNN LRNN [ 101 ] MLP TensorLog [ 105…”
Using Domain-Knowledge to Assist Lead Discovery in Early-Stage Drug Design
Dash, T., Srinivasan, A., Vig, L., & Roy, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-97454-1_6 · Cited by 8 · see citing works
Self-citation
Discrete Stochastic Search and Its Application to Feature-Selection for Deep Relational Machines
Dash, T., Srinivasan, A., Joshi, R. S., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30484-3_3 · Cited by 8 · cited 1× · see citing works
“The sampling technique is reﬁned further in [26].”
Self-citation
DomiKnowS: A Library for Integration of Symbolic Domain Knowledge in Deep Learning
Faghihi, H. R., Guo, Q., Uszok, A., Nafar, A., & Kordjamshidi, P. (2021) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2021.emnlp-demo.27 · Cited by 8 · cited 2× · see citing works
“Alibraryfor integrating symbolic domain-knowledge with deep neural networks was introduced recently in [73].”
Domain Knowledge Embedding Regularization Neural Networks for Workload Prediction and Analysis in Cloud Computing
Li, L., Feng, M., Jin, L., et al. (2018) · Journal of Information Technology Research · doi:10.4018/jitr.2018100109 · Cited by 7 · cited 1× · see citing works
“The result could be thought of as learning weighted forms of the antecedents present in the rules.”
“Who can help me?”: Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit
Gaur, M., Roy, K., Sharma, A., Srivastava, B., & Sheth, A. (2021) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi52183.2021.00049 · Cited by 7 · see citing works
Injecting Domain Knowledge in Neural Networks: A Controlled Experiment on a Constrained Problem
Silvestri, M., Lombardi, M., & Milano, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-78230-6_17 · Cited by 5 · cited 2× · see citing works
“[ 70 ] inject domain knowledge at training time via an approach that combines semantic based regularisation and constraint programming [ 71 ] .”
Background
Special issue: Knowledge-based neural networks
Fu, L. (1995) · Knowledge-Based Systems · doi:10.1016/0950-7051(95)90000-4 · Cited by 3 · see citing works
Knowledge-Based Distant Regularization in Learning Probabilistic ModelsPreprint
Takeishi, N., & Akimoto, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.11332 · Cited by 3 · see citing works
NeuralLog: a Neural Logic LanguagePreprint
Guimarães, V., & Costa, V. S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.01442 · Cited by 3 · cited 2× · see citing works
“…[ 89 ] ARTMAP CIL 2 P [ 29 ] RNN DeepProbLog [ 93 ] CNN LRNN [ 101 ] MLP TensorLog [ 105 ] MLP Domain-Aware BERT [ 82 ] Transformer NeuralLog [ 106 ] MLP DeepStochLog [ 94 ] DNN* LNN [ 103 ] MLP Table 1: Some selected works, in no particular order, showing the principal approach of domain…”

Flagged first, then by citations

3 of the 112 references couldn’t be matched to a known paper.

Research works printed but not linked: 9

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Sorted by citations

A Survey on Graph Representation Learning Methods
Khoshraftar, S., & An, A. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3633518 · Cited by 247 · see citing works
“This knowledge can be included in the models by changing the input, loss function, and model architecture [49].”
Towards artificial intelligence-enabled extracellular vesicle precision drug delivery
Greenberg, Z., Graim, K. S., & He, M. (2023) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2023.114974 · Cited by 160 · see citing works
Advancements in remote sensing for active fire detection: A review of datasets and methods
Yang, S., Huang, Q., & Yu, M. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.173273 · Cited by 94 · see citing works
3D molecular generative framework for interaction-guided drug design
(2024) · doi:10.1038/s41467-024-47011-2 · Cited by 88 · see citing works
“From the reference ligands of their original complexes, we extracted initial core structures, which were azabicyclo[2.2.1]heptane, 2-(oxan-3-yloxy)oxane, and benzene, respectively.”
Influential
Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency
Montero, A. M. B., Bibal, A., Huet‐Dastarac, M., Draguet, C., Valdés, G., Nguyen, D. T. D., Willems, S., Vandewinckele, L., Holmström, M., Löfman, F., et al. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac678a · Cited by 87 · see citing works
“Early works of including domain knowledge into the loss function date from the mid nineties ( Fu 1995, Dash et al 2022 ) .”
Background
Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data
Chato, L., & Regentova, E. E. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13121703 · Cited by 71 · see citing works
Towards Data-And Knowledge-Driven AI: A Survey on Neuro-Symbolic Computing
Wang, W., Yang, Y., & Wu, F. Q. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3483273 · Cited by 92 · see citing works
“In a related vein, informed ma-chinelearning[216],[217]focusinparticularonhowtointegrate prior knowledge into DNNs.”
Evidence synthesis, digital scribes, and translational challenges for artificial intelligence in healthcare
Coiera, E. W., & Liu, S. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100860 · Cited by 52 · see citing works
“82 Pragmatically, many healthcare problems involve highly structured data, may have low dimensionality, and can yield to traditional statistical approaches such as linear regression, 83 or classic tree-building methods from machine learning such as xgboost.”
Background
Knowledge‐enhanced graph neural networks for construction material quantity estimation of reinforced concrete buildings
Fei, Y., Liao, W., Lu, X., & Guan, H. (2023) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13094 · Cited by 47 · see citing works
“Generally, for neural networks, prior knowledge is introduced by modifying the following three elements of the network (Dash et al., 2022): (1) input data (Marino et al., 2021; Yin et al., 2019), (2) loss function (Y. Du et al., 2022; Fei et al., 2022a; Lu et al., 2022), and (3) network…”
Method
Multi-omics based artificial intelligence for cancer research
Li, L., Sun, M., Wang, J., & Wan, S. (2024) · Advances in cancer research · doi:10.1016/bs.acr.2024.06.005 · Cited by 47 · see citing works
Mapping rapeseed in China during 2017-2021 using Sentinel data: an automated approach integrating rule-based sample generation and a one-class classifier (RSG-OC)
Zang, Y., Qiu, Y., Chen, X., et al. (2023) · GIScience & Remote Sensing · doi:10.1080/15481603.2022.2163576 · Cited by 45 · see citing works
“Effectively integrating the domain knowledge might help to reduce the relying on the large training samples for deep (Dash et al. 2022; Xie et al. 2021).”
Background
Deep Learning with Logical Constraints
Giunchiglia, E., Stoian, M. C., & Lukasiewicz, T. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/767 · Cited by 45 · see citing works
“For coarser surveys on how to include background knowledge expressed in different ways, see [von Rueden et al. , 2021; Dash et al. , 2022].”
Background
Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence
Son, A., Park, J., Kim, W., Yoon, Y., Lee, S., Park, Y., & Kim, H. (2024) · Molecules · doi:10.3390/molecules29194626 · Cited by 42 · see citing works
“Embedding domain knowledge into AI models can greatly enhance their interpretability and resilience, as demonstrated by recent research in diverse domains like health, engineering, and environmental science [47].”
Background
Machine learning based surrogate modelling for the prediction of maximum contact temperature in EHL line contacts
Singh, A., Wolf, M., Jacobs, G., & König, F. (2023) · Tribology International · doi:10.1016/j.triboint.2022.108166 · Cited by 38 · see citing works
AI in Context: Harnessing Domain Knowledge for Smarter Machine Learning
Miller, T. I., Durlik, I., Łobodzińska, A., Dorobczyński, L., & Jasionowski, R. (2024) · Applied Sciences · doi:10.3390/app142411612 · Cited by 40 · see citing works
Physics-guided neural networks with engineering domain knowledge for hybrid process modeling
Gallup, E. R., Gallup, T., & Powell, K. M. (2023) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2022.108111 · Cited by 36 · see citing works
EINNs: Epidemiologically-Informed Neural Networks
Rodríguez, A., Cui, J., Ramakrishnan, N., et al. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26690 · Cited by 34 · see citing works
Knowledge-Infused Learning: A Sweet Spot in Neuro-Symbolic AI
Gaur, M., Gunaratna, K., Bhatt, S., & Sheth, A. (2022) · IEEE Internet Computing · doi:10.1109/mic.2022.3179759 · Cited by 34 · see citing works
“A substantial growth in incorporating knowledgewith deep learning has pursued.(3) Humans are capable of handling information at various levels of abstraction, which corresponds to different levels of learning.”
Background
Towards trustworthy artificial intelligence for decision-making: A lifecycle perspective on knowledge- and data-driven artificial intelligence systems
Miedema, E., Waschull, S., & Emmanouilidis, C. (2025) · Computers in Industry · doi:10.1016/j.compind.2025.104409 · Cited by 38 · see citing works
Towards responsible AI for education: Hybrid human-AI to confront the Elephant in the room
Hooshyar, D., Šír, G., Yang, Y., Kikas, E., Hämäläinen, R., Kärkkäinen, T., Gašević, D., & Azevedo, R. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100524 · Cited by 49 · see citing works
“The proposed framework offers three distinct ways to embed symbolic knowledge into deep neural networks (Dash et al., 2022): modifying network inputs by representing raw features as embeddings that capture both inherent characteristics and domain-specific context, enforcing domain knowledge through…”
An Effective Framework for Deep-Learning-Enhanced Quantitative Microwave Imaging and Its Potential for Medical Applications
Ruiz, Á. Y., Cavagnaro, M., & Crocco, L. (2023) · Sensors · doi:10.3390/s23020643 · Cited by 26 · see citing works
“In these approaches, domain knowledge on wave scattering physics is incorporated into the input or in the internal architecture of the neural network, having a beneficial effect on its performance [15,16].”
Background
Unfolding Explainable AI for Brain Tumor Segmentation
Hassan, M. A., Fateh, A. A., Lin, J., Zhuang, Y., Lin, G., Xiong, H., You, Z., Qin, P., & Zeng, H. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128058 · Cited by 28 · see citing works
ArkDTA: attention regularization guided by non-covalent interactions for explainable drug–target binding affinity prediction
Gim, M., Choe, J., Baek, S., Park, J., Lee, C., Ju, M., Lee, S., & Kang, J. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad207 · Cited by 25 · see citing works
“Critical domain knowledge should be integrated to ensure that the model’s implicit assumptions are compatible with expert opinions (Dash et al. 2022).”
Background
Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data
Kampezidou, S. I., Ray, A. T., Bhat, A. P., Fischer, O. J. P., & Mavris, D. N. (2024) · Eng—Advances in Engineering · doi:10.3390/eng5010021 · Cited by 25 · see citing works
A Survey on Large Language Model-based Agents for Statistics and Data Science
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Parametric and Machine Learning-Based Analysis of the Seismic Vulnerability of Adobe Historical Buildings Damaged After the September 2017 Mexico Earthquakes
Eudave, R. R., Ferreira, T. M., Vicente, R., et al. (2023) · International Journal of Architectural Heritage · doi:10.1080/15583058.2023.2200739 · Cited by 23 · see citing works
“Given that we accept that the set of parameters is representative for conditioning the level of damage, it is possible to accept it as a Handcrafted Feature Extraction Technique (Dash et al. 2022).”
Background
Phenotyping Key Fruit Quality Traits in Olive Using RGB Images and Back Propagation Neural Networks
Montanaro, G., Petrozza, A., Rustioni, L., et al. (2023) · Plant Phenomics · doi:10.34133/plantphenomics.0061 · Cited by 21 · see citing works
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Background
From failure to fusion: A survey on learning from bad machine learning models
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Integrating fluid–solid coupling domain knowledge with deep learning models: An automatic and interpretable diagnostic system for the silting disease of drainage pipelines
Fang, H., Zhang, Z., Di, D., Zhang, J., Sun, B., Wang, N., & Li, B. (2023) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2023.105386 · Cited by 20 · see citing works
Visible neural networks for multi-omics integration: a critical review
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Semantic Loss
Arrotta, L., Civitarese, G., & Bettini, C. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3631407 · Cited by 19 · see citing works
Probabilistic Bayesian Neural Networks for olive phenology prediction in precision agriculture
Nappa, A., Quartulli, M., Azpiroz, I., Marchi, S., Guidotti, D., Staiano, M., & Siciliano, R. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102723 · Cited by 18 · see citing works
RISnet: A Domain-Knowledge Driven Neural Network Architecture for RIS Optimization With Mutual Coupling and Partial CSI
Peng, B., Besser, K.-L., Shen, S., Siegismund-Poschmann, F., Raghunath, R., Mittleman, D. M., Jamali, V., & Jorswieck, E. A. (2025) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2025.3536178 · Cited by 18 · see citing works
“According to [34], incorporating domain knowledge is considered as one of the three grand challenges in ML.”
Mixture of Experts for EEG-Based Seizure Subtype Classification
Du, Z., Peng, R., Liu, W., Li, W., & Wu, D. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3337802 · Cited by 18 · see citing works
“Restrictions apply. interesting to incorporate a priori expert knowledge into DNNs to reduce their labeled data requirement and enhance their performance [34]: 1) Use expert knowledge to augment or transform the inputs.”
BackgroundInfluential
Generating Novel Leads for Drug Discovery Using LLMs with Logical Feedback
Brahmavar, S. B., Srinivasan, A., Dash, T., Krishnan, S. R., Vig, L., Roy, A., & Aduri, R. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i1.27751 · Cited by 17 · see citing works
Self-citation
Prediction of Water Diffusion in Wide Varieties of Polymers with All-Atom Molecular Dynamics Simulations and Deep Generative Models
Kawada, R., Endo, K., Yasuoka, K., Kojima, H., & Matubayasi, N. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01316 · Cited by 16 · see citing works
Logic-Informed Graph Neural Networks for Structural Form-Finding
Bleker, L., Tam, K.-M. M., & D’Acunto, P. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102510 · Cited by 15 · see citing works
ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models
Arrotta, L., Bettini, C., Civitarese, G., & Fiori, M. (2024) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp61445.2024.00029 · Cited by 15 · see citing works
The utilization of different AI methods-based satellite communications: A survey
Abdulwahid, M. M., & Kurnaz, S. (2024) · AIP conference proceedings · doi:10.1063/5.0192068 · Cited by 14 · see citing works
Neuro-Symbolic AI + Agent Systems: A First Reflection on Trends, Opportunities and Challenges
Belle, V., Fisher, M., Russo, A., Komendantskaya, E., & Nottle, A. (2023) · AAMAS Workshops · doi:10.1007/978-3-031-56255-6_10 · Cited by 14 · see citing works
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Background
Comparison of deep learning models for milk production forecasting at national scale
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The interpretable deep learning-based coke quality prediction strategy aligned with domain knowledge for sustainable smart cokemaking applications
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A Multi-layered Collaborative Framework for Evidence-driven Data Requirements Engineering for Machine Learning-based Safety-critical Systems
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Involving logical clinical knowledge into deep neural networks to improve bladder tumor segmentation
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Precast components on-site construction planning and scheduling method based on a novel deep learning integrated multi-agent system
Jiang, S., Yang, B., & Liu, B. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.111907 · Cited by 12 · see citing works
Hyperthermia Treatment Monitoring via Deep Learning Enhanced Microwave Imaging: A Numerical Assessment
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“In the latter, DL architectures are combined with standard MWI approaches that supply the neural network with domain knowledge that either makes the training of the network easier or its predictions more efficient [17,18].”
Method
Safety Monitoring of Machine Learning Perception Functions: A Survey
Ferreira, R., Guerin, J. M., Delmas, K., Guiochet, J., & Waeselynck, H. (2025) · Computational Intelligence · doi:10.1111/coin.70032 · Cited by 18 · see citing works
LAMBDA: A Large Model Based Data Agent
Sun, M., Han, R., Jiang, B., Qi, H., Sun, D., Yuan, Y., & Huang, J. (2025) · Journal of the American Statistical Association · doi:10.1080/01621459.2025.2510000 · Cited by 15 · see citing works
“On the other hand, domain experts find it challenging to integrate their expertise into AI models to enhance their performance (Dash et al. 2022).”
Post-earthquake residual track irregularities prediction using wavelet-packet-transformation-powered domain adaptive neural network
Zhang, Y., Yan, W., Jiang, L., et al. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111695 · Cited by 9 · see citing works
Hybrid AI Systems for Tool Wear Monitoring in Manufacturing: A Systematic Review
Saatçı, B. T., Ulaş, M., & Gürgenç, T. (2025) · Applied Sciences · doi:10.3390/app16010208 · Cited by 10 · see citing works
Improving Pre-trained Language Models
Paaß, G., & Giesselbach, S. (2023) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-3-031-23190-2_3 · Cited by 9 · see citing works
Concurrent Multi-Label Prediction in Event Streams
Shou, X., Gao, T., Subramanian, D., Bhattacharjya, D., & Bennett, K. P. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i8.26172 · Cited by 9 · see citing works
“where λ trades off between negative log-likelihood loss and background knowledge loss (Dash et al. 2022).”
Background
Detection of Emotions in Artworks Using a Convolutional Neural Network Trained on Non-Artistic Images: A Methodology to Reduce the Cross-Depiction Problem
González-Martín, C., Carrasco, M., & Wielandt, T. G. W. (2023) · Empirical Studies of the Arts · doi:10.1177/02762374231163481 · Cited by 8 · see citing works
“This problem has seen more studies in recent years to facilitate learning about different domains (Dash, Chitlangia, Ahuja, & Srinivasan, 2022) and it supposes a limitation in the ﬁ eld of deep learning.”
Background
Towards a neuro-symbolic cycle for human-centered explainability
Mileo, A. (2024) · Neurosymbolic Artificial Intelligence · doi:10.3233/nai-240740 · Cited by 8 · see citing works
“Vector embeddings as well as modiﬁcation of the loss function have become popular approaches to inject background knowledge into DNNs with a focus on improving performance [5].”
DFDK-MSNet: A Deep Fusion Framework of Domain Knowledge and Multiscale 1DCNN for Specific Emitter Identification
Guo, S., Li, L., Li, X., & Zhu, J. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3502777 · Cited by 8 · see citing works
Challenges and opportunities in Machine learning for bioenergy crop yield Prediction: A review
Dayil, J. L., Akande, O., Mahmoud, A. E. D., Kimera, R., & Omole, O. (2024) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2024.104057 · Cited by 14 · see citing works
A Survey of Human Intelligence Augmented Artificial Intelligence: An Autonomous Driving Perspective
Li, S., Yuan, K., Tang, Z., Zhang, Y., Ji, P., Ge, Q., Du, S., Huang, Y., & Chen, H. (2025) · Automotive Innovation · doi:10.1007/s42154-025-00361-z · Cited by 8 · see citing works
Superpixel-based knowledge infusion in deep neural networks for image classification
Chhablani, G., Sharma, A., Pandey, H., & Dash, T. (2022) · ACM Southeast Regional Conference (ACMSE) · doi:10.1145/3476883.3520216 · Cited by 8 · see citing works
“where the amount of available data is low [6].”
BackgroundSelf-citation
Brain‐regulated learning for classifying on‐site hazards with small datasets
Zhou, X., & Liao, P. (2023) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13078 · Cited by 8 · see citing works
“…various methods such as demonstration learning (C.-J. Liang et al., 2020), active learning (Jinwoo Kim et al., 2020), structured domain knowledge (Dash et al., 2022), and discriminative feature selection (Deng et al., 2015), this study highlights the unique advantage of directly leveraging human…”
Method
Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace
Hong, J., Park, K. H., & Pavlic, T. P. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00481 · Cited by 8 · see citing works
“Since the introduction of deep-learning models with logical constraints [23], this dataset has been used as one of the benchmark datasets to assess the effectiveness of embedding the constraints into neural networks (in-depth surveys can be found in [15,28]).”
Method
ClinVec: Unified Embeddings of Clinical Codes Enable Knowledge-Grounded AI in MedicinePreprint
Johnson, R., Gottlieb, U., Shaham, G., Eisen, L., Waxman, J. G., Devons-Sberro, S., Ginder, C. R., Hong, P. J., Sayeed, R., Su, X., et al. (2024) · medRxiv · doi:10.1101/2024.12.03.24318322 · Cited by 8 · see citing works
AI-Driven Chemometrics for Multi-omics Data Integration: Advances, Challenges, and Future Directions
Polu, P. R. (2026) · Critical Reviews in Analytical Chemistry · doi:10.1080/10408347.2026.2657553 · Cited by 9 · see citing works
A Multi-Method Machine Learning Framework for Simulating Power Dynamics in an Iron Ore Cone Crusher Plant
Gharaei, R. S., Gholami, A., Khoshdast, H., & Biglari, H. (2024) · Mineral Processing and Extractive Metallurgy Review · doi:10.1080/08827508.2024.2395828 · Cited by 7 · see citing works
Boosting Deep Learning for Interpretable Brain MRI Lesion Detection through the Integration of Radiology Report Information
Dai, L., Lei, J., Ma, F., Sun, Z., Du, H., Zhang, H., Jiang, J., Wei, J., Wang, D., Tan, G., et al. (2024) · Radiology Artificial Intelligence · doi:10.1148/ryai.230520 · Cited by 7 · see citing works
Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI PerspectivePreprint
Baumgartner, P., Smith, D., Rana, M., Kapoor, R., Tartaglia, E., Schutt, A., Rahman, A., Taylor, J., & Dunstall, S. (2022) · Research Square · doi:10.21203/rs.3.rs-2136936/v1 · Cited by 8 · see citing works
An integrated deep learning framework for the interpretation of untargeted metabolomics data
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Explanatory Debiasing: Involving Domain Experts in the Data Generation Process to Mitigate Representation Bias in AI Systems
Bhattacharya, A., Stumpf, S., Croon, R. D., & Verbert, K. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713497 · Cited by 6 · see citing works
A domain-knowledge-driven deep learning framework for automatic recognition of train-induced ground-borne vibrations
Zou, C., Zhang, Y., Jiang, S., Li, X., Luo, W., Liao, Z., & Guo, W. (2026) · Measurement · doi:10.1016/j.measurement.2026.122225 · Cited by 6 · see citing works
High-Accuracy ncRNA Function Prediction via Deep Learning Using Global and Local Sequence Information
Orro, A., & Trombetti, G. (2023) · Biomedicines · doi:10.3390/biomedicines11061631 · Cited by 6 · see citing works
“Although advanced machine learning architectures should be capable of inferring knowledge from data on their own, the direct inclusion of additional domain knowledge into the training process is very appealing [31,32], not only for increased accuracy and computational speed, but also for obtaining simpler and more int…”
Background
Potentials and challenges in enhancing the gear transmission development with machine learning methods—a review
Sendlbeck, S., Maurer, M., Otto, M. K., & Stahl, K. (2023) · Forschung im Ingenieurwesen · doi:10.1007/s10010-023-00699-y · Cited by 7 · see citing works
“Combining data-driven machine learning with domain knowledge directly, as for example reviewed in [103] for deep neural networks and shown by Liu et al. in feature selection for materials property prediction [104], is promising for ﬂexible and high-quality designs, but is rarely addressed in…”
Method
Zero-Shot Modulation Recognition via Knowledge-Informed Waveform Description
Chen, Y., Wang, X., & Huang, Z. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3491013 · Cited by 6 · see citing works
“Researchers have found that domain-knowledge can give guidance on what data features might be helpful [17], [18], [19].”
An exploratory study on domain knowledge infusion in deep learning for automated threat defense
Khanzadeh, S., Neto, E. C. P., Iqbal, S., Alalfi, M. H., & Buffett, S. (2025) · International Journal of Information Security · doi:10.1007/s10207-025-00987-4 · Cited by 7 · see citing works
Consistent Scene Graph Generation by Constraint Optimization
Chen, B., Marussy, K., Pilarski, S., et al. (2022) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3551349.3560433 · Cited by 6 · see citing works
Human Expectations and Perceptions of Learning in Machine Teaching
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“Machine Teaching environments are generally customized to a specific task [24].”
Background
Is machine learning a necessity? A regression-based approach for stock return prediction
Cheng, T., Jiang, S., Zhao, A. B., et al. (2025) · Journal of Empirical Finance · doi:10.1016/j.jempfin.2025.101598 · Cited by 5 · see citing works
Artificial intelligence for sustainable architectural design
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Domain-enhanced dual-branch model for efficient and interpretable accident anticipation
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Review on artificial intelligence supported breast cancer diagnosis for effective treatment planning
Narayanan, M., Prabhu, N., & Rajinikanth, V. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-07920-3 · Cited by 6 · see citing works
“Combine domain expertise into model strategy to improve explain ability [92].”
Phases, Modalities, Spatial and Temporal Locality: Domain Specific ML Prefetcher for Accelerating Graph Analytics
Zhang, P., Kannan, R., & Prasanna, V. K. (2023) · 2012 International Conference for High Performance Computing, Networking, Storage and Analysis · doi:10.1145/3581784.3607043 · Cited by 5 · see citing works
Framework for learning and control in the classical and quantum domains
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The function of red and banded patterns in snakes: a review of hypotheses and evidence
Mouy, H. (2023) · Biological Journal of the Linnean Society · doi:10.1093/biolinnean/blad151 · Cited by 5 · see citing works
Prestack Seismic Inversion Driven by Priori Information Neural Network and Statistical Characteristic
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Method
Identifying type II quasars at intermediate redshift with few-shot learning photometric classification
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DKINet: Medication Recommendation via Domain Knowledge Informed Deep Learning
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GLUECons: A Generic Benchmark for Learning under Constraints
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Integrating Domain Knowledge into Deep Learning for Skin Lesion Risk Prioritization to Assist Teledermatology Referral
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Proceedings of the 5th workshop on Advanced tools, programming languages, and PLatforms for Implementing and Evaluating algorithms for Distributed systems
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Ensemble modelling based on transfer learning for enhancing crop mapping through synergistic integration of InSAR coherence and multispectral satellite data
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ConSequence: Synthesizing Logically Constrained Sequences for Electronic Health Record Generation
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HYPERTENSION PREDICTION USING DEEP LEARNING WITH TRANSFER LEARNING TECHNIQUES
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Development of a multi-feature-combined model: proof-of-concept with application to local failure prediction of post-SBRT or surgery early-stage NSCLC patients
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“Nevertheless, the deep radiomic features are highly data-dependent and are directly learned by the neural networks without human supervision; these make the extracted features incompatible with human experts’ interpretation within the existing knowledge domain (56).”
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High-performance perovskite quantum dot synthesis investigated through exploratory data analysis
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Authors: Dash, T., Chitlangia, S., Ahuja, A., & Srinivasan, A.
Affiliations: Birla Institute of Technology and Science, Pilani - Goa Campus (IN)
Corresponding author: Dash, Tirtharaj
ORCID: Dash, Tirtharaj
Srinivasan, Ashwin
Venue: Scientific Reports
Publisher: Springer Science and Business Media LLC
Published: 2022-01-20
Volume: 12
Issue: 1
Pages: 1040-1040
ISSN: 2045-2322 (ISSN-L)
DOI: 10.1038/s41598-021-04590-0
PMC: PMC8776800
OpenAlex: W4205511145
Semantic Scholar: 8f00fca03482af9c7aaf3c5ff60f8a0f69178935
Cited by: 196
References: 112
Citations by year: 2026: 30 · 2025: 52 · 2024: 52 · 2023: 49 · 2022: 11 · 2021: 2
FWCI: 13.49
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Scientific Reports (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
arXiv (Cornell University) (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
arXiv (Cornell University)
Funders: University of New South Wales
Birla Institute of Technology and Science, Pilani
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
Keywords: domain knowledge, deep neural network, loss functions, network architecture, input encoding, human-machine collaboration

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A review of some techniques for inclusion of domain-knowledge into deep neural networks

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