Search Results - learning with errors (problem OR problems)

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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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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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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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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Prediction Policy Problems by 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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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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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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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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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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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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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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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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Solving Inverse Problems With Deep Neural Networks - Robustness Included? by 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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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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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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Pushing the frontiers of density functionals by solving the fractional electron problem by 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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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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A unified approach to error bounds for structured convex optimization problems by 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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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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