Search Results - learning with errors (problems OR (problemysys OR problemysys))

Learning from Errors by 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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Learning about structural errors in models of complex dynamical systems by Wu, Jin-Long, Levine, Matthew E., Schneider, Tapio, Stuart, Andrew

“… Building on such closure models and correcting them through learning the structural errors can be an effective way of fusing data with domain knowledge…”
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Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning by 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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Learning and correcting non-Gaussian model errors by Smyl, Danny, Tallman, Tyler N., Black, Jonathan A., Hauptmann, Andreas, Liu, Dong

“… In this work, we address this challenge by proposing a neural network approach capable of accurately approximating and compensating for such modeling errors in augmented direct and inverse problems…”
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Noise resilient quantum learning: Adaptive policy guided error mitigation in quantum reinforcement learning for the traveling salesman problem by 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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Post-Quantum Key Exchange for the TLS Protocol from the Ring Learning with Errors Problem by 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 by 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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Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems by 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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Using meta‐learning to predict performance metrics in machine learning problems by 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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Generalization of machine learning for problem reduction: a case study on travelling salesman problems by 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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Understanding error patterns in students' solutions to linear function problems to design learning interventions by 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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Learning to construct a solution for UAV path planning problem with positioning error correction by 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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Deep Learning for Integrated Origin-Destination Estimation and Traffic Sensor Location Problems by 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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Zero-Error Tracking Control Under Unified Quantized Iterative Learning Framework via Encoding-Decoding Method by Shen, Dong, Zhang, Chao

“…This article considers the zero-error tracking problem of quantized iterative learning control for a general networked structure where the data are quantized and transmitted through limited…”
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Solving a Higgs optimization problem with quantum annealing for machine learning by 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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Students' errors in solving probability problems viewed by learning style by Salido, A, Dasari, D

“… This study aims to describe students' errors in probability problems viewed by learning styles…”
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Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review by 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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Fostering Learning from Errors—Computer-Based Adaptivity at the Transition Between Problem Solving and Explicit Instruction by Boomgaarden, Antje, Loibl, Katharina, Leuders, Timo

“…When learners acquire new content by working on a problem-solving task prior to explicit instruction, their attempts to solve the problem usually represent only partial steps on the way to the target concept…”
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Error control and loss functions for the deep learning inversion of borehole resistivity measurements by Shahriari, Mostafa, Pardo, David, Rivera, Jon A., Torres‐Verdín, Carlos, Picon, Artzai, Del Ser, Javier, Ossandón, Sebastian, Calo, Victor M.

“…Deep learning (DL) is a numerical method that approximates functions. Recently, its use has become attractive for the simulation and inversion of multiple…”
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NN‐mCRE: A modified constitutive relation error framework for unsupervised learning of nonlinear state laws with physics‐augmented neural networks by Benady, Antoine, Baranger, Emmanuel, Chamoin, Ludovic

“… The neural network is trained thanks to an unsupervised procedure in which the modified constitutive relation error (mCRE) is minimized…”
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