S. Grunbacher, R. Hasani, M. Lechner, J. Cyranka, S. A. Smolka, and R. Grosu, “On the verification of neural ODEs with stochastic guarantees,” in Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35, no. 13, pp. 11525–11535.

R. Hasani, M. Lechner, A. Amini, D. Rus, and R. Grosu, “Liquid time-constant networks,” in Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35, no. 9, pp. 7657–7666.

Z. Babaiee, R. Hasani, M. Lechner, D. Rus, and R. Grosu, “On-off center-surround receptive fields for accurate and robust image classification,” in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 478–489.

C. J. Vorbach, R. Hasani, A. Amini, M. Lechner, and D. Rus, “Causal navigation by continuous-time neural networks,” in 35th Conference on Neural Information Processing Systems, Virtual, 2021.

B. Kragl and S. Qadeer, “The Civl verifier,” in Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design, Virtual, 2021, vol. 2, pp. 143–152.

H. Polshyn et al., “Orbital Chern insulator states in twisted monolayer-bilayer graphene and electrical switching of topological and magnetic order,” in APS March Meeting 2021, Virtual, 2021, vol. 66, no. 1.

G. Avni, I. R. Jecker, and D. Zikelic, “Infinite-duration all-pay bidding games,” in Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms, Virtual, 2021, pp. 617–636.

P. Tomásek, K. Horák, A. Aradhye, B. Bosanský, and K. Chatterjee, “Solving partially observable stochastic shortest-path games,” in 30th International Joint Conference on Artificial Intelligence, Virtual, Online, 2021, pp. 4182–4189.

A. Fedorov, N. Koval, and D.-A. Alistarh, “A scalable concurrent algorithm for dynamic connectivity,” in Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures, Virtual, Online, 2021, pp. 208–220.

F. Lombardi, S. Pepic, O. Shriki, G. Tkadik, and D. De Martino, “Quantifying the coexistence of neuronal oscillations and avalanches.” arXiv.

G. S. Shadel et al., “The San Diego Nathan Shock Center: Tackling the heterogeneity of aging,” GeroScience, vol. 43, no. 5. Springer Nature, pp. 2139–2148, 2021.

S. Krishna, R. Arrojo e Drigo, J. S. Capitanio, R. Ramachandra, M. Ellisman, and M. Hetzer, “Identification of long-lived proteins in the mitochondria reveals increased stability of the electron transport chain,” Developmental Cell, vol. 56, no. 21. Elsevier, p. 52–2965.e9, 2021.

V. Kungurtsev, M. Egan, B. Chatterjee, and D.-A. Alistarh, “Asynchronous optimization methods for efficient training of deep neural networks with guarantees,” in 35th AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual, Online, 2021, vol. 35, no. 9B, pp. 8209–8216.

L. Braun and T. P. Vogels, “Online learning of neural computations from sparse temporal feedback,” in Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 20, pp. 16437–16450.

F. Alimisis, P. Davies, B. Vandereycken, and D.-A. Alistarh, “Distributed principal component analysis with limited communication,” in Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 4, pp. 2823–2834.

E. Frantar, E. Kurtic, and D.-A. Alistarh, “M-FAC: Efficient matrix-free approximations of second-order information,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 14873–14886.

D.-A. Alistarh and J. Korhonen, “Towards tight communication lower bounds for distributed optimisation,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 7254–7266.

K. B. Schmidt et al., “Recovery and analysis of rest-frame UV emission lines in 2052 galaxies observed with MUSE at 1.5 &; z &; 6.4,” Astronomy &; Astrophysics, vol. 654. EDP Sciences, 2021.

R. Bacon et al., “The MUSE Extremely Deep Field: The cosmic web in emission at high redshift,” Astronomy &; Astrophysics, vol. 647. EDP Sciences, 2021.

L. A. Boogaard et al., “Measuring the average molecular gas content of star-forming galaxies at z = 3–4,” The Astrophysical Journal, vol. 916, no. 1. IOP Publishing, 2021.