NSTL国家科技图书文献中心

1. Integrating kNN Retrieval with Inference on Graphical Models in Case-Based Reasoning NSTL国家科技图书文献中心

Luigi Portinale - 《Case-Based Reasoning Research and Development: 32nd International Conference, ICCBR 2024, Merida, Mexico, July 1-4, 2024, Proceedings》 - International Conference on Case-Based Reasoning - 2024, - 1～16 - 共16页

摘要：In Case-Based Reasoning, when the similarity | assumption does not hold, knowledge about the adaptability | of solutions has to be exploited, in order to | retrieve cases with adaptable solutions. We propose a | novel approach to address this issue, where kNN

关键词： Similarity assumption | Undirected graphical models | K-NN retrieval

2. Influence Maximization in Ising Models NSTL国家科技图书文献中心

Zongchen Chen | Elchanan Mossel - 《15th Innovations in Theoretical Computer Science Conference: ITCS 2024, Berkeley, California, USA, 30 January - 2 February 2024, Part 1 of 3》 - Innovations in Theoretical Computer Science Conference - 2024, - 30-1～30-14 - 共14页

摘要： example of undirected* graphical models and has wide* | maximization on sparse Ising models* under a bounded budget* | models which is also a critical point for other | Given a complex high-dimensional distribution | over {±1}~n, what is the best way to increase the

关键词： Influence maximization | Ising model | Phase transition | Correlation decay

3. Neural Graphical Models NSTL国家科技图书文献中心

Harsh Shrivastava | Urszula Chajewska - 《Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 17th European Conference, ECSQARU 2023, Arras, France, September 19-22, 2023, Proceedings》 - European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 2024, - 284～307 - 共24页

摘要：Probabilistic Graphical* Models are often used* | we introduce Neural Graphical* Models (NGMs) which* | graphical models, perform inference analysis of a lung | distribution. Theoretically these models can represent very | directed, undirected* and mixed-edge graphs as well as*

关键词： Probabilistic graphical models | Deep learning | Learning representations

4. False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening NSTL国家科技图书文献中心

Taulant Koka | Jasin Machkour ... - 《2024 32nd European Signal Processing Conference》 - European Signal Processing Conference - 2024, - 2482～2486 - 共5页

摘要：Gaussian graphical* models emerge in a wide* | graphical lasso or neighborhood selection, are known to be | the individual neighborhoods outputs an undirected | range of fields. They model the statistical | relationships between variables as a graph, where an edge

关键词： Graphical models | Image edge detection | Input variables | Process control | Medical services | Benchmark testing | Signal processing | Topology | Numerical models | Tuning

5. Neural Graph Revealers NSTL国家科技图书文献中心

Harsh Shrivastava | Urszula Chajewska - 《Machine Learning for Multimodal Healthcare Data: First International Workshop, ML4MHD 2023, Honolulu, Hawaii, USA, July 29, 2023, Proceedings》 - Workshop on Machine Learning for Multimodal Healthcare Data - 2024, - 7～25 - 共19页

摘要： hand, Probabilistic Graphical* Models (PGMs) learn an* | from Gaussian graphical* models and a multimodal* | norm that NGRs leverage to learn a graphical* model* | dependencies between features in the form of an undirected | Sparse graph recovery methods work well where

关键词： Sparse graph recovery | Probabilistic graphical models | Deep learning

6. ESTIMATING NORMALIZED GRAPH LAPLACIANS IN FINANCIAL MARKETS NSTL国家科技图书文献中心

Josi Vinicius de M. ... | Jiaxi Ying ... - 《2023 IEEE International Conference on Acoustics, Speech and Signal Processing: ICASSP 2023, Rhodes Island, Greece, 4-10 June 2023, [v.10]》 - IEEE International Conference on Acoustics, Speech and Signal Processing - 2023, - 7501～7505 - 共5页

摘要： graphical models, play an increasingly important role in | , graphical models. More precisely, we design an | problem of learning undirected, weighted, normalized | benchmark models, in a number of datasets involving | Gaussian Markov random fields, a class of

关键词： graphical models | normalized Laplacian | financial markets | time series | estimation theory

7. Aggregating the Gaussian Experts' Predictions via Undirected Graphical Models NSTL国家科技图书文献中心

Hamed Jalali | Gjergji Kasneci - 《2022 IEEE International Conference on Big Data and Smart Computing: IEEE International Conference on Big Data and Smart Computing (BigComp), 17-20 Jan. 2022, Daegu, South Korea》 - IEEE International Conference on Big Data and Smart Computing - 2022, - 23～26 - 共4页

摘要： graphical model (GGM) where the target aggregation is | Distributed Gaussian process (DGP) is a | popular approach to scale Gaussian processes to big data | which divides the training data into some subsets | , performs local inference for each partition, and

关键词： Graphical models | Aggregates | Training data | Gaussian processes | Big Data | Predictive models | Gaussian distribution

NSTL主题词： Graphical models | Gauss

8. Interpreting Potts and Transformer Protein Models Through the Lens of Simplified Attention NSTL国家科技图书文献中心

Nicholas Bhattachary... | Neil Thomas ... - 《Pacific Symposium on Biocomputing 2022 :》 - Pacific Symposium on Biocomputing - 2022, - 34～45 - 共12页

摘要： positions using undirected* graphical models. This approach | databases not captured by single-layer models. This raises | powerful structured models of protein family databases.* | The established approach to unsupervised | protein contact prediction estimates coevolving

关键词： Contact Prediction | Representation Learning | Language Modeling | Attention | Transformer | BERT | Markov Random Fields | Potts Models | Self-supervised learning

NSTL主题词： Lenses | Lens Plant | lens | through the lens | Transformer | Protein

9. Adaptive Noisy Data Augmentation for Regularized Construction of Undirected Graphical Models NSTL国家科技图书文献中心

Yinan Li | Fang Liu ... - 《2021 IEEE 8th International Conference on Data Science and Advanced Analytics: DSAA 2021, Porto, Portugal, 6-9 October 2021》 - IEEE International Conference on Data Science and Advanced Analytics - 2021, - 1～10 - 共10页

摘要： undirected graphical models. PANDA iteratively optimizes | practically interpretable and meaningful graphical* models.* | software that implements generalized linear models* and* | We develop the AdaPtive Noise Augmentation | (PANDA) technique to regularize the estimation of

关键词： Proteins | Adaptation models | Analytical models | Graphical models | Estimation | Lung cancer | Linear programming

NSTL主题词： noisy data | Graphical models | Self tuning

10. Learning Undirected Graphs in Financial Markets NSTL国家科技图书文献中心

José Vinícius de Mir... | Daniel P. Palomar - 《2020 54th Asilomar Conference on Signals, Systems, and Computers: 54th Asilomar Conference on Signals, Systems, and Computers, 1-4 Nov. 2020, Pacific Grove, CA, USA》 - Asilomar Conference on Signals, Systems, and Computers - 2021, - 741～745 - 共5页

摘要： undirected graphical models under Laplacian structural | learn undirected* graphs that account for stylized* | We investigate the problem of learning | constraints from the point of view of financial market data | . We show that Laplacian constraints have meaningful

摘要：In Case-Based Reasoning, when the similarity | assumption does not hold, knowledge about the adaptability | of solutions has to be exploited, in order to | retrieve cases with adaptable solutions. We propose a | novel approach to address this issue, where kNN

关键词： Similarity assumption | Undirected graphical models | K-NN retrieval

摘要： example of undirected* graphical models and has wide* | maximization on sparse Ising models* under a bounded budget* | models which is also a critical point for other | Given a complex high-dimensional distribution | over {±1}~n, what is the best way to increase the

关键词： Influence maximization | Ising model | Phase transition | Correlation decay

摘要：Probabilistic Graphical* Models are often used* | we introduce Neural Graphical* Models (NGMs) which* | graphical models, perform inference analysis of a lung | distribution. Theoretically these models can represent very | directed, undirected* and mixed-edge graphs as well as*

关键词： Probabilistic graphical models | Deep learning | Learning representations

摘要：Gaussian graphical* models emerge in a wide* | graphical lasso or neighborhood selection, are known to be | the individual neighborhoods outputs an undirected | range of fields. They model the statistical | relationships between variables as a graph, where an edge

关键词： Graphical models | Image edge detection | Input variables | Process control | Medical services | Benchmark testing | Signal processing | Topology | Numerical models | Tuning

摘要： hand, Probabilistic Graphical* Models (PGMs) learn an* | from Gaussian graphical* models and a multimodal* | norm that NGRs leverage to learn a graphical* model* | dependencies between features in the form of an undirected | Sparse graph recovery methods work well where

关键词： Sparse graph recovery | Probabilistic graphical models | Deep learning

摘要： graphical models, play an increasingly important role in | , graphical models. More precisely, we design an | problem of learning undirected, weighted, normalized | benchmark models, in a number of datasets involving | Gaussian Markov random fields, a class of

关键词： graphical models | normalized Laplacian | financial markets | time series | estimation theory

摘要： graphical model (GGM) where the target aggregation is | Distributed Gaussian process (DGP) is a | popular approach to scale Gaussian processes to big data | which divides the training data into some subsets | , performs local inference for each partition, and

关键词： Graphical models | Aggregates | Training data | Gaussian processes | Big Data | Predictive models | Gaussian distribution

NSTL主题词： Graphical models | Gauss

摘要： positions using undirected* graphical models. This approach | databases not captured by single-layer models. This raises | powerful structured models of protein family databases.* | The established approach to unsupervised | protein contact prediction estimates coevolving

关键词： Contact Prediction | Representation Learning | Language Modeling | Attention | Transformer | BERT | Markov Random Fields | Potts Models | Self-supervised learning

NSTL主题词： Lenses | Lens Plant | lens | through the lens | Transformer | Protein

摘要： undirected graphical models. PANDA iteratively optimizes | practically interpretable and meaningful graphical* models.* | software that implements generalized linear models* and* | We develop the AdaPtive Noise Augmentation | (PANDA) technique to regularize the estimation of

关键词： Proteins | Adaptation models | Analytical models | Graphical models | Estimation | Lung cancer | Linear programming

NSTL主题词： noisy data | Graphical models | Self tuning

摘要： undirected graphical models under Laplacian structural | learn undirected* graphs that account for stylized* | We investigate the problem of learning | constraints from the point of view of financial market data | . We show that Laplacian constraints have meaningful

关键词： Computers | Laplace equations | Graphical models | Correlation | Computational modeling | Clustering algorithms | Indexes

NSTL主题词： undirected graph | Stock exchanges | Markets | Learning

4008-161-200 800-990-8900

国家科技图书文献中心

摘要：In Case-Based Reasoning, when the similarity | assumption does not hold, knowledge about the adaptability | of solutions has to be exploited, in order to | retrieve cases with adaptable solutions. We propose a | novel approach to address this issue, where kNN

关键词： Similarity assumption | Undirected graphical models | K-NN retrieval

摘要： example of undirected graphical models and has wide | maximization on sparse Ising models under a bounded budget | models which is also a critical point for other | Given a complex high-dimensional distribution | over {±1}~n, what is the best way to increase the

关键词： Influence maximization | Ising model | Phase transition | Correlation decay

摘要：Probabilistic Graphical Models are often used | we introduce Neural Graphical Models (NGMs) which | graphical models, perform inference analysis of a lung | distribution. Theoretically these models can represent very | directed, undirected and mixed-edge graphs as well as

关键词： Probabilistic graphical models | Deep learning | Learning representations

摘要：Gaussian graphical models emerge in a wide | graphical lasso or neighborhood selection, are known to be | the individual neighborhoods outputs an undirected | range of fields. They model the statistical | relationships between variables as a graph, where an edge

关键词： Graphical models | Image edge detection | Input variables | Process control | Medical services | Benchmark testing | Signal processing | Topology | Numerical models | Tuning

摘要： hand, Probabilistic Graphical Models (PGMs) learn an | from Gaussian graphical models and a multimodal | norm that NGRs leverage to learn a graphical model | dependencies between features in the form of an undirected | Sparse graph recovery methods work well where

关键词： Sparse graph recovery | Probabilistic graphical models | Deep learning

摘要： graphical models, play an increasingly important role in | , graphical models. More precisely, we design an | problem of learning undirected, weighted, normalized | benchmark models, in a number of datasets involving | Gaussian Markov random fields, a class of

关键词： graphical models | normalized Laplacian | financial markets | time series | estimation theory

摘要： graphical model (GGM) where the target aggregation is | Distributed Gaussian process (DGP) is a | popular approach to scale Gaussian processes to big data | which divides the training data into some subsets | , performs local inference for each partition, and

关键词： Graphical models | Aggregates | Training data | Gaussian processes | Big Data | Predictive models | Gaussian distribution

NSTL主题词： Graphical models | Gauss

摘要： positions using undirected graphical models. This approach | databases not captured by single-layer models. This raises | powerful structured models of protein family databases. | The established approach to unsupervised | protein contact prediction estimates coevolving

关键词： Contact Prediction | Representation Learning | Language Modeling | Attention | Transformer | BERT | Markov Random Fields | Potts Models | Self-supervised learning

NSTL主题词： Lenses | Lens Plant | lens | through the lens | Transformer | Protein

摘要： undirected graphical models. PANDA iteratively optimizes | practically interpretable and meaningful graphical models. | software that implements generalized linear models and | We develop the AdaPtive Noise Augmentation | (PANDA) technique to regularize the estimation of

关键词： Proteins | Adaptation models | Analytical models | Graphical models | Estimation | Lung cancer | Linear programming

NSTL主题词： noisy data | Graphical models | Self tuning

摘要： undirected graphical models under Laplacian structural | learn undirected graphs that account for stylized | We investigate the problem of learning | constraints from the point of view of financial market data | . We show that Laplacian constraints have meaningful

关键词： Computers | Laplace equations | Graphical models | Correlation | Computational modeling | Clustering algorithms | Indexes

NSTL主题词： undirected graph | Stock exchanges | Markets | Learning

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： example of undirected* graphical models and has wide* | maximization on sparse Ising models* under a bounded budget* | models which is also a critical point for other | Given a complex high-dimensional distribution | over {±1}~n, what is the best way to increase the

摘要：Probabilistic Graphical* Models are often used* | we introduce Neural Graphical* Models (NGMs) which* | graphical models, perform inference analysis of a lung | distribution. Theoretically these models can represent very | directed, undirected* and mixed-edge graphs as well as*

摘要：Gaussian graphical* models emerge in a wide* | graphical lasso or neighborhood selection, are known to be | the individual neighborhoods outputs an undirected | range of fields. They model the statistical | relationships between variables as a graph, where an edge

摘要： hand, Probabilistic Graphical* Models (PGMs) learn an* | from Gaussian graphical* models and a multimodal* | norm that NGRs leverage to learn a graphical* model* | dependencies between features in the form of an undirected | Sparse graph recovery methods work well where

摘要： positions using undirected* graphical models. This approach | databases not captured by single-layer models. This raises | powerful structured models of protein family databases.* | The established approach to unsupervised | protein contact prediction estimates coevolving

摘要： undirected graphical models. PANDA iteratively optimizes | practically interpretable and meaningful graphical* models.* | software that implements generalized linear models* and* | We develop the AdaPtive Noise Augmentation | (PANDA) technique to regularize the estimation of

摘要： undirected graphical models under Laplacian structural | learn undirected* graphs that account for stylized* | We investigate the problem of learning | constraints from the point of view of financial market data | . We show that Laplacian constraints have meaningful