The Hasse principle for random Fano hypersurfaces
T.D. Browning, P.L. Boudec, W. Sawin, Annals of Mathematics 197 (2023) 1115–1203.
LibreCat: https://research-explorer.ista.ac.at/record/8682
DOI: 10.4007/annals.2023.197.3.3
WoS: 000966611000003
arXiv: 2006.02356

Empirical parameterisation and dynamical analysis of the allometric Rosenzweig-MacArthur equations
J.C. Mckerral, M. Kleshnina, V. Ejov, L. Bartle, J.G. Mitchell, J.A. Filar, PLoS One 18 (2023) e0279838.
LibreCat: https://research-explorer.ista.ac.at/record/12706
DOI: 10.1371/journal.pone.0279838
WoS: 000996122900022
PMID: 36848357

Nanoscale phosphoinositide distribution on cell membranes of mouse cerebellar neurons
K. Eguchi, E. Le Monnier, R. Shigemoto, The Journal of Neuroscience 43 (2023) 4197–4216.
LibreCat: https://research-explorer.ista.ac.at/record/13202
DOI: 10.1523/JNEUROSCI.1514-22.2023
WoS: 001020132100005
PMID: 37160366

Uniform bounds for rational points on hyperelliptic fibrations
D. Bonolis, T.D. Browning, Annali Della Scuola Normale Superiore Di Pisa - Classe Di Scienze 24 (2023) 173–204.
LibreCat: https://research-explorer.ista.ac.at/record/12916
DOI: 10.2422/2036-2145.202010_018
arXiv: 2007.14182

Synapseek: Meta-learning synaptic plasticity rules
B.J. Confavreux, Synapseek: Meta-Learning Synaptic Plasticity Rules, Institute of Science and Technology Austria, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14422
DOI: 10.15479/at:ista:14422

Boundary superconductivity in BCS theory
B. Roos, Boundary Superconductivity in BCS Theory, Institute of Science and Technology Austria, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14374
DOI: 10.15479/at:ista:14374

Boundary superconductivity in the BCS model
C. Hainzl, B. Roos, R. Seiringer, Journal of Spectral Theory 12 (2023) 1507–1540.
LibreCat: https://research-explorer.ista.ac.at/record/13207
DOI: 10.4171/JST/439
WoS: 000997933500008
arXiv: 2201.08090

The infinitesimal model with dominance
N.H. Barton, A.M. Etheridge, A. Véber, Genetics 225 (2023).
LibreCat: https://research-explorer.ista.ac.at/record/14452
DOI: 10.1093/genetics/iyad133
arXiv: 2211.03515

Quantized distributed training of large models with convergence guarantees
I. Markov, A. Vladu, Q. Guo, D.-A. Alistarh, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 24020–24044.
LibreCat: https://research-explorer.ista.ac.at/record/14461
arXiv: 2302.02390

Constant matters: Fine-grained error bound on differentially private continual observation
H. Fichtenberger, M.H. Henzinger, J. Upadhyay, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 10072–10092.
LibreCat: https://research-explorer.ista.ac.at/record/14462

Fundamental limits of two-layer autoencoders, and achieving them with gradient methods
A. Shevchenko, K. Kögler, H. Hassani, M. Mondelli, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 31151–31209.
LibreCat: https://research-explorer.ista.ac.at/record/14459
arXiv: 2212.13468

SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge
M. Nikdan, T. Pegolotti, E.B. Iofinova, E. Kurtic, D.-A. Alistarh, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 26215–26227.
LibreCat: https://research-explorer.ista.ac.at/record/14460
arXiv: 2302.04852

Stronger lower bounds for leakage-resilient secret sharing
C. Hoffmann, M. Simkin, in:, 8th International Conference on Cryptology and Information Security in Latin America, Springer Nature, 2023, pp. 215–228.
LibreCat: https://research-explorer.ista.ac.at/record/14457
DOI: 10.1007/978-3-031-44469-2_11

SparseGPT: Massive language models can be accurately pruned in one-shot
E. Frantar, D.-A. Alistarh, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 10323–10337.
LibreCat: https://research-explorer.ista.ac.at/record/14458
arXiv: 2301.00774

Multi-objective reward generalization: improving performance of Deep Reinforcement Learning for applications in single-asset trading
F. Cornalba, C. Disselkamp, D. Scassola, C. Helf, Neural Computing and Applications (2023).
LibreCat: https://research-explorer.ista.ac.at/record/14451
DOI: 10.1007/s00521-023-09033-7
arXiv: 2203.04579

Mixtures of self-propelled particles interacting with asymmetric obstacles
M.N. Rojas Vega, P. De Castro, R. Soto, The European Physical Journal E 46 (2023).
LibreCat: https://research-explorer.ista.ac.at/record/14442
DOI: 10.1140/epje/s10189-023-00354-y
PMID: 37819444

Substructures in Latin squares
M.A. Kwan, A. Sah, M. Sawhney, M. Simkin, Israel Journal of Mathematics 256 (2023) 363–416.
LibreCat: https://research-explorer.ista.ac.at/record/14444
DOI: 10.1007/s11856-023-2513-9
arXiv: 2202.05088

Monitoring algorithmic fairness under partial observations
T.A. Henzinger, K. Kueffner, K. Mallik, in:, 23rd International Conference on Runtime Verification, Springer Nature, 2023, pp. 291–311.
LibreCat: https://research-explorer.ista.ac.at/record/14454
DOI: 10.1007/978-3-031-44267-4_15
arXiv: 2308.00341

Against the flow of time with multi-output models
J. Jakubík, M. Phuong, M. Chvosteková, A. Krakovská, Measurement Science Review 23 (2023) 175–183.
LibreCat: https://research-explorer.ista.ac.at/record/14446
DOI: 10.2478/msr-2023-0023

Addressing global environmental challenges to mental health using population neuroscience: A review
G. Schumann, O.A. Andreassen, T. Banaschewski, V.D. Calhoun, N. Clinton, S. Desrivieres, R.E. Brandlistuen, J. Feng, S. Hese, E. Hitchen, P. Hoffmann, T. Jia, V. Jirsa, A.F. Marquand, F. Nees, M.M. Nöthen, G. Novarino, E. Polemiti, M. Ralser, M. Rapp, K. Schepanski, T. Schikowski, M. Slater, P. Sommer, B.C. Stahl, P.M. Thompson, S. Twardziok, D. Van Der Meer, H. Walter, L. Westlye, JAMA Psychiatry 80 (2023) 1066–1074.
LibreCat: https://research-explorer.ista.ac.at/record/14443
DOI: 10.1001/jamapsychiatry.2023.2996
PMID: 37610741