Skip to content

Latest commit

 

History

History
50 lines (50 loc) · 9.36 KB

Holland.md

File metadata and controls

50 lines (50 loc) · 9.36 KB
  • Biswas, S., Chen, F., Chen, Z., Lu, C.T. and Ramakrishnan, N., 2023. Memetic algorithms for spatial partitioning problems. ACM Transactions on Spatial Algorithms and Systems, 9(1), pp.1-31.
  • Kaiser, T.K. and Hamann, H., 2022. Innate motivation for robot swarms by minimizing surprise: From simple simulations to real-world experiments. IEEE Transactions on Robotics, 38(6), pp.3582-3601.
  • Koçak, Ö., Levinthal, D.A. and Puranam, P., 2022. The dual challenge of search and coordination for organizational adaptation: How structures of influence matter. Organization Science.
  • Schlag, S., Heuer, T., Gottesbüren, L., Akhremtsev, Y., Schulz, C. and Sanders, P., 2022. High-quality hypergraph partitioning. ACM Journal of Experimental Algorithms.
  • Mallipeddi, R.R., Kumar, S., Sriskandarajah, C. and Zhu, Y., 2022. A framework for analyzing influencer marketing in social networks: Selection and scheduling of influencers. Management Science, 68(1), pp.75-104.
  • Schmidhuber, J., 2022. Annotated history of modern AI and deep learning. arXiv preprint arXiv:2212.11279.
  • Stripinis, L. and Paulavičius, R., 2022. DIRECTGO: A new DIRECT-type MATLAB toolbox for derivative-free global optimization. ACM Transactions on Mathematical Software, 48(4), pp.1-46.
  • Albert, D. and Siggelkow, N., 2022. Architectural search and innovation. Organization Science, 33(1), pp.275-292.
  • Gissurarson, M.P., Applis, L., Panichella, A., van Deursen, A. and Sands, D., 2022, May. PropR: property-based automatic program repair. In Proceedings of IEEE/ACM International Conference on Software Engineering (pp. 1768-1780).
  • Csaszar, F.A. and Steinberger, T., 2022. Organizations as artificial intelligences: The use of artificial intelligence analogies in organization theory. Academy of Management Annals, 16(1), pp.1-37.
  • Mueller, C.M. and Schatz, G.C., 2022. An algorithmic approach based on data trees and genetic algorithms to understanding charged and neutral metal nanocluster growth. Journal of Physical Chemistry A, 126(34), pp.5864-5872.
  • Hart, E. and Le Goff, L.K., 2022. Artificial evolution of robot bodies and control: On the interaction between evolution, learning and culture. Philosophical Transactions of the Royal Society B, 377(1843), p.20210117.
  • Tandeitnik, D. and Guerreiro, T., 2022. Evolving quantum circuits. arXiv preprint arXiv:2210.05058.
  • Duan, J.C., Li, S. and Xu, Y., 2022. Sequential Monte Carlo optimization and statistical inference. Wiley Interdisciplinary Reviews: Computational Statistics, p.e1598.
  • Fornasier, M., Huang, H., Pareschi, L. and Sünnen, P., 2021. Consensus-based optimization on the sphere: Convergence to global minimizers and machine learning. Journal of Machine Learning Research, 22(1), pp.10722-10776.
  • Dijkstra, M. and Luijten, E., 2021. From predictive modelling to machine learning and reverse engineering of colloidal self-assembly. Nature Materials, 20(6), pp.762-773.
  • Demo, N., Tezzele, M. and Rozza, G., 2021. A supervised learning approach involving active subspaces for an efficient genetic algorithm in high-dimensional optimization problems. SIAM Journal on Scientific Computing, 43(3), pp.B831-B853.
  • Singh, R., Sharma, A., Singh, P., Balasubramanian, G. and Johnson, D.D., 2021. Accelerating computational modeling and design of high-entropy alloys. Nature Computational Science, 1(1), pp.54-61.
  • Aligholipour, R., Baharloo, M., Farzaneh, B., Abdollahi, M. and Khonsari, A., 2021. TAMA: Turn-aware mapping and architecture–a power-efficient network-on-chip approach. ACM Transactions on Embedded Computing Systems, 20(5), pp.1-24.
  • Wu, Z., Sun, J., Zhang, Y., Wei, Z. and Chanussot, J., 2021. Recent developments in parallel and distributed computing for remotely sensed big data processing. Proceedings of the IEEE, 109(8), pp.1282-1305.
  • Perez-Nieves, N., Yang, Y., Slumbers, O., Mguni, D.H., Wen, Y. and Wang, J., 2021, July. Modelling behavioural diversity for learning in open-ended games. In International Conference on Machine Learning (pp. 8514-8524). PMLR.
  • Miikkulainen, R. and Forrest, S., 2021. A biological perspective on evolutionary computation. Nature Machine Intelligence, 3(1), pp.9-15.
  • Bowskill, D.H., Sugden, I.J., Konstantinopoulos, S., Adjiman, C.S. and Pantelides, C.C., 2021. Crystal structure prediction methods for organic molecules: State of the art. Annual Review of Chemical and Biomolecular Engineering, 12, pp.593-623.
  • Zhang, J. and Glezakou, V.A., 2021. Global optimization of chemical cluster structures: Methods, applications, and challenges. International Journal of Quantum Chemistry, 121(7), p.e26553.
  • Wang, J.C., Ding, D., Wang, H., Christensen, C., Wang, Z., Chen, H. and Li, J., 2021, July. Polyjuice: High-performance transactions via learned concurrency control. In OSDI (pp. 198-216).
  • Li, M., Sutter, T. and Kuhn, D., 2021, July. Distributionally robust optimization with Markovian data. In International Conference on Machine Learning (pp. 6493-6503). PMLR.
  • Yuan, S. and Jing, W., 2021. Optimal shape adjustment of large high-precision cable network structures. AIAA Journal, 59(4), pp.1441-1456.
  • Wang, Y., Xue, P., Cao, M., Yu, T., Lane, S.T. and Zhao, H., 2021. Directed evolution: Methodologies and applications. Chemical Reviews, 121(20), pp.12384-12444.
  • Brunton, S.L., Noack, B.R. and Koumoutsakos, P., 2020. Machine learning for fluid mechanics. Annual Review of Fluid Mechanics, 52, pp.477-508.
  • Hegde, K., Tsai, P.A., Huang, S., Chandra, V., Parashar, A. and Fletcher, C.W., 2021, April. Mind mappings: enabling efficient algorithm-accelerator mapping space search. In Proceedings of ACM International Conference on Architectural Support for Programming Languages and Operating Systems (pp. 943-958).
  • Real, E., Liang, C., So, D. and Le, Q., 2020, November. Automl-zero: Evolving machine learning algorithms from scratch. In International Conference on Machine Learning (pp. 8007-8019). PMLR.
  • Hasson, U., Nastase, S.A. and Goldstein, A., 2020. Direct fit to nature: An evolutionary perspective on biological and artificial neural networks. Neuron, 105(3), pp.416-434.
  • Li, S., Ke, L., Pratama, K., Tai, Y.W., Tang, C.K. and Cheng, K.T., 2020. Cascaded deep monocular 3d human pose estimation with evolutionary training data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6173-6183).
  • Cesca, S., Letort, J., Razafindrakoto, H.N., Heimann, S., Rivalta, E., Isken, M.P., Nikkhoo, M., Passarelli, L., Petersen, G.M., Cotton, F. and Dahm, T., 2020. Drainage of a deep magma reservoir near Mayotte inferred from seismicity and deformation. Nature Geoscience, 13(1), pp.87-93.
  • Bernhardt, J.R., O'Connor, M.I., Sunday, J.M. and Gonzalez, A., 2020. Life in fluctuating environments. Philosophical Transactions of the Royal Society B, 375(1814), p.20190454.
  • Gussow, A.B., Park, A.E., Borges, A.L., Shmakov, S.A., Makarova, K.S., Wolf, Y.I., Bondy-Denomy, J. and Koonin, E.V., 2020. Machine-learning approach expands the repertoire of anti-CRISPR protein families. Nature Communications, 11(1), p.3784.
  • Huang, C.C., Jin, G. and Li, J., 2020, March. Swapadvisor: Pushing deep learning beyond the gpu memory limit via smart swapping. In Proceedings of ACM International Conference on Architectural Support for Programming Languages and Operating Systems (pp. 1341-1355).
  • Sheil, B.B., Suryasentana, S.K., Mooney, M.A. and Zhu, H., 2020. Machine learning to inform tunnelling operations: Recent advances and future trends. Proceedings of Institution of Civil Engineers-Smart Infrastructure and Construction, 173(4), pp.74-95.
  • Wang, J., Clark, S.C., Liu, E. and Frazier, P.I., 2020. Parallel Bayesian global optimization of expensive functions. Operations Research, 68(6), pp.1850-1865.
  • Domingos, P., 2020. Every model learned by gradient descent is approximately a kernel machine. arXiv preprint arXiv:2012.00152.
  • Iliadis, C. and Coc, A., 2020. Thermonuclear reaction rates and primordial nucleosynthesis. Astrophysical Journal, 901(2), p.127.
  • Hassanzadeh, T., Essam, D. and Sarker, R., 2020. 2D to 3D evolutionary deep convolutional neural networks for medical image segmentation. IEEE Transactions on Medical Imaging, 40(2), pp.712-721.
  • O’Neill, M. and Spector, L., 2020. Automatic programming: The open issue?. Genetic Programming and Evolvable Machines, 21, pp.251-262.
  • Cole, A., Schachner, A. and Shiu, G., 2019. Searching the landscape of flux vacua with genetic algorithms. Journal of High Energy Physics, 2019(11), pp.1-39.
  • Berto, P., Philippet, L., Osmond, J., Liu, C.F., Afridi, A., Montagut Marques, M., Molero Agudo, B., Tessier, G. and Quidant, R., 2019. Tunable and free-form planar optics. Nature Photonics, 13(9), pp.649-656.
  • Elkelesh, A., Ebada, M., Cammerer, S. and Ten Brink, S., 2019. Decoder-tailored polar code design using the genetic algorithm. IEEE Transactions on Communications, 67(7), pp.4521-4534.
  • Schmidhuber, J., 2019. Reinforcement learning upside down: Don't predict rewards--Just map them to actions. arXiv preprint arXiv:1912.02875.
  • De Moraes, R.S. and De Freitas, E.P., 2019. Multi-UAV based crowd monitoring system. IEEE Transactions on Aerospace and Electronic Systems, 56(2), pp.1332-1345.
  • Berahas, A.S., Byrd, R.H. and Nocedal, J., 2019. Derivative-free optimization of noisy functions via quasi-Newton methods. SIAM Journal on Optimization, 29(2), pp.965-993.
  • Freeman, D., Ha, D. and Metz, L., 2019. Learning to predict without looking ahead: World models without forward prediction. Advances in Neural Information Processing Systems, 32.