Suchergebnisse - learning with errors (problem OR (problems OR (problems OR problems)))

Generalization of machine learning for problem reduction: a case study on travelling salesman problems von Sun, Yuan, Ernst, Andreas, Li, Xiaodong, Weiner, Jake

“… In this paper, we examine the generalization capability of a machine learning model for problem reduction on the classic travelling salesman problems (TSP …”
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Prediction Policy Problems von Kleinberg, Jon, Ludwig, Jens, Mullainathan, Sendhil, Obermeyer, Ziad

“… these “prediction policy problems” requires more than simple regression techniques, since these are tuned to generating unbiased estimates of coefficients rather than minimizing prediction error …”
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Solving Inverse Problems With Deep Neural Networks - Robustness Included? von Genzel, Martin, Macdonald, Jan, Marz, Maximilian

“… In the past five years, deep learning methods have become state-of-the-art in solving various inverse problems …”
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Deep Learning for Integrated Origin-Destination Estimation and Traffic Sensor Location Problems von Owais, Mahmoud

“… This article provides a resilient deep learning (DL) architecture combined with a global sensitivity analysis tool to solve O-D estimation and sensor location problems simultaneously …”
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Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems von Cao, Lianghao, O'Leary-Roseberry, Thomas, Jha, Prashant K., Oden, J. Tinsley, Ghattas, Omar

“… We explore using neural operators, or neural network representations of nonlinear maps between function spaces, to accelerate infinite-dimensional Bayesian inverse problems (BIPs …”
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Solving a Higgs optimization problem with quantum annealing for machine learning von Mott, Alex, Job, Joshua, Vlimant, Jean-Roch, Lidar, Daniel, Spiropulu, Maria

“… Here, Alex Mott and colleagues implement a 'signal versus background' machine learning optimization problem that can be used in the search for Higgs bosons at the Large Hadron Collider at CERN and at future colliders …”
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A-PINN: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations von Yuan, Lei, Ni, Yi-Qing, Deng, Xiang-Yun, Hao, Shuo

“… Physics informed neural networks (PINNs) are a novel deep learning paradigm primed for solving forward and inverse problems of nonlinear partial differential equations (PDEs …”
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Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review von Chan, Jireh Yi-Le, Leow, Steven Mun Hong, Bea, Khean Thye, Cheng, Wai Khuen, Phoong, Seuk Wai, Hong, Zeng-Wei, Chen, Yen-Lin

“… Although this presents opportunities to better model the relationship between predictors and the response variables, this also causes serious problems during data analysis, one of which is the multicollinearity problem …”
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Using meta‐learning to predict performance metrics in machine learning problems von Carneiro, Davide, Guimarães, Miguel, Carvalho, Mariana, Novais, Paulo

“… Machine learning has been facing significant challenges over the last years, much of which stem from the new characteristics of machine learning problems, such as learning from streaming data …”
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Pushing the frontiers of density functionals by solving the fractional electron problem von Kirkpatrick, James, McMorrow, Brendan, Turban, David H P, Gaunt, Alexander L, Spencer, James S, Matthews, Alexander G D G, Obika, Annette, Thiry, Louis, Fortunato, Meire, Pfau, David, Castellanos, Lara Román, Petersen, Stig, Nelson, Alexander W R, Kohli, Pushmeet, Mori-Sánchez, Paula, Hassabis, Demis, Cohen, Aron J

“… Density functional theory describes matter at the quantum level, but all popular approximations suffer from systematic errors that arise from the violation of mathematical properties of the exact functional …”
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Learning from Errors von Metcalfe, Janet

“… Although error avoidance during learning appears to be the rule in American classrooms, laboratory studies suggest that it may be a counterproductive strategy, at least for neurologically typical students …”
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A unified approach to error bounds for structured convex optimization problems von Zhou, Zirui, So, Anthony Man-Cho

“… of a host of iterative methods for solving optimization problems. In this paper, we present a new framework for establishing error bounds for a class of structured …”
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Understanding error patterns in students' solutions to linear function problems to design learning interventions von Elagha, Noor, Pellegrino, James W.

“… There were two overarching aims of the reported studies. One was to assess students’ understanding of LF and discern the cognitive underpinnings of common errors they make in these types of problems …”
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Noise resilient quantum learning: Adaptive policy guided error mitigation in quantum reinforcement learning for the traveling salesman problem von Majid, Bisma, Sofi, Shabir Ahmed, Jabeen, Zamrooda

“… Quantum Reinforcement Learning (QRL) has emerged as a promising paradigm for solving combinatorial optimization problems by leveraging quantum parallelism and interference to enhance learning efficiency …”
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Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning von Allen, Kelsey R, Smith, Kevin A, Tenenbaum, Joshua B

“… But human beings remain distinctive in their capacity for flexible, creative tool use-using objects in new ways to act on the world, achieve a goal, or solve a problem …”
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Post-Quantum Key Exchange for the TLS Protocol from the Ring Learning with Errors Problem von Bos, Joppe W., Costello, Craig, Naehrig, Michael, Stebila, Douglas

“… ) protocol that provide key exchange based on the ring learning with errors (R-LWE) problem, we accompany these cipher suites with a rigorous proof of security …”
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Effects of self-explaining feedback on learning from problem-solving errors von Zhang, Qian, Fiorella, Logan

“… •Modifying the layout of the feedback did not affect learning. We tested two potential ways to help students learn from feedback on their problem-solving errors in physics …”
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Deep problems with neural network models of human vision von Bowers, Jeffrey S., Malhotra, Gaurav, Dujmović, Marin, Llera Montero, Milton, Tsvetkov, Christian, Biscione, Valerio, Puebla, Guillermo, Adolfi, Federico, Hummel, John E., Heaton, Rachel F., Evans, Benjamin D., Mitchell, Jeffrey, Blything, Ryan

“… ) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3 …”
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Strong rules for discarding predictors in lasso-type problems von Tibshirani, Robert, Bien, Jacob, Friedman, Jerome, Hastie, Trevor, Simon, Noah, Taylor, Jonathan, Tibshirani, Ryan J.

“… We consider rules for discarding predictors in lasso regression and related problems, for computational efficiency …”
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Learning to construct a solution for UAV path planning problem with positioning error correction von Chun, Jie, Chen, Ming, Liu, Xiaolu, Xiang, Shang, Du, Yonghao, Wu, Guohua, Xing, Lining

“… This study seeks to solve the UAV path planning problem with positioning error correction (UPEC) with an end-to-end method …”
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