Artikler med mandater om offentlig tilgang - Martin TakáčLes mer
Ikke tilgjengelige noe sted: 2
Classification-Aware Path Planning of Network of Robots
G Liu, A Amini, M Takáč, N Motee
Distributed Autonomous Robotic Systems: 15th International Symposium, 294-305, 2022
Mandater: US Department of Defense
Multi-LSTM-Based Framework for Ambient Intelligence
NS Gulgec, M Takáč, SN Pakzad
Dynamics of Civil Structures, Volume 2: Proceedings of the 39th IMAC, A …, 2022
Mandater: US National Science Foundation
Tilgjengelige et eller annet sted: 60
Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function
P Richtárik, M Takáč
Mathematical Programming 144 (1), 1-38, 2014
Mandater: UK Engineering and Physical Sciences Research Council
SARAH: A novel method for machine learning problems using stochastic recursive gradient
L Nguyen, J Liu, K Scheinberg, M Takáč
In 34th International Conference on Machine Learning, ICML 2017, 2017
Mandater: US National Science Foundation
Parallel coordinate descent methods for big data optimization
P Richtárik, M Takáč
Mathematical Programming, Series A, 1-52, 2015
Mandater: UK Engineering and Physical Sciences Research Council
Mini-batch semi-stochastic gradient descent in the proximal setting
J Konečný, J Liu, P Richtárik, M Takáč
IEEE Journal of Selected Topics in Signal Processing 10 (2), 242-255, 2015
Mandater: UK Engineering and Physical Sciences Research Council
CoCoA: A general framework for communication-efficient distributed optimization
V Smith, S Forte, C Ma, M Takáč, MI Jordan, M Jaggi
Journal of Machine Learning Research 18 (230), 1-49, 2018
Mandater: US National Science Foundation, Swiss National Science Foundation, US …
Distributed coordinate descent method for learning with big data
P Richtárik, M Takác
Journal of Machine Learning Research 17, 1-25, 2016
Mandater: UK Engineering and Physical Sciences Research Council
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
LM Nguyen, PH Nguyen, M van Dijk, P Richtárik, K Scheinberg, M Takáč
In 34th International Conference on Machine Learning, ICML 2018, 2018
Mandater: US National Science Foundation, US Department of Defense
A deep q-network for the beer game: Deep reinforcement learning for inventory optimization
A Oroojlooyjadid, MR Nazari, LV Snyder, M Takáč
Manufacturing & Service Operations Management 24 (1), 285-304, 2022
Mandater: US National Science Foundation
Applying deep learning to the newsvendor problem
A Oroojlooyjadid, LV Snyder, M Takáč
IISE Transactions 52 (4), 444-463, 2020
Mandater: US National Science Foundation
A Multi-Batch L-BFGS Method for Machine Learning
AS Berahas, J Nocedal, M Takáč
The Thirtieth Annual Conference on Neural Information Processing Systems (NIPS), 2016
Mandater: US National Science Foundation, US Department of Energy
On optimal probabilities in stochastic coordinate descent methods
P Richtárik, M Takáč
Optimization Letters, 2015, 1-11, 2015
Mandater: UK Engineering and Physical Sciences Research Council
SDNA: stochastic dual newton ascent for empirical risk minimization
Z Qu, P Richtárik, M Takáč, O Fercoq
In 33rd International Conference on Machine Learning, ICML 2016, 2016
Mandater: UK Engineering and Physical Sciences Research Council
Convolutional neural network approach for robust structural damage detection and localization
NS Gulgec, M Takáč, SN Pakzad
Journal of computing in civil engineering 33 (3), 04019005, 2019
Mandater: US National Science Foundation
Quasi-Newton methods for machine learning: forget the past, just sample
AS Berahas, M Jahani, P Richtárik, M Takáč
Optimization Methods and Software 37 (5), 1668-1704, 2022
Mandater: US National Science Foundation, US Department of Defense
Modal identification of bridges using mobile sensors with sparse vibration data
S Sadeghi Eshkevari, SN Pakzad, M Takáč, TJ Matarazzo
Journal of Engineering Mechanics 146 (4), 04020011, 2020
Mandater: US National Science Foundation, Fraunhofer-Gesellschaft, National Research …
Fast distributed coordinate descent for non-strongly convex losses
O Fercoq, Z Qu, P Richtárik, M Takáč
IEEE Workshop on Machine Learning for Signal Processing, 2014, 2014
Mandater: UK Engineering and Physical Sciences Research Council
Structural sensing with deep learning: Strain estimation from acceleration data for fatigue assessment
NS Gulgec, M Takáč, SN Pakzad
Computer‐Aided Civil and Infrastructure Engineering 35 (12), 1349-1364, 2020
Mandater: US National Science Foundation, US Department of Transportation
Structural damage detection using convolutional neural networks
NS Gulgec, M Takáč, SN Pakzad
Model Validation and Uncertainty Quantification, Volume 3: Proceedings of …, 2017
Mandater: US National Science Foundation
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