Zobrazeno 1 - 10
of 447
pro vyhledávání: '"Neklyudov, P. A."'
The Cambrian explosion of easily accessible pre-trained diffusion models suggests a demand for methods that combine multiple different pre-trained diffusion models without incurring the significant computational burden of re-training a larger combine
Externí odkaz:
http://arxiv.org/abs/2412.17762
Autor:
Du, Yuanqi, Plainer, Michael, Brekelmans, Rob, Duan, Chenru, Noé, Frank, Gomes, Carla P., Aspuru-Guzik, Alán, Neklyudov, Kirill
Rare event sampling in dynamical systems is a fundamental problem arising in the natural sciences, which poses significant computational challenges due to an exponentially large space of trajectories. For settings where the dynamical system of intere
Externí odkaz:
http://arxiv.org/abs/2410.07974
Autor:
Atanackovic, Lazar, Zhang, Xi, Amos, Brandon, Blanchette, Mathieu, Lee, Leo J., Bengio, Yoshua, Tong, Alexander, Neklyudov, Kirill
Numerous biological and physical processes can be modeled as systems of interacting entities evolving continuously over time, e.g. the dynamics of communicating cells or physical particles. Learning the dynamics of such systems is essential for predi
Externí odkaz:
http://arxiv.org/abs/2408.14608
Autor:
Wang, Haorui, Skreta, Marta, Ser, Cher-Tian, Gao, Wenhao, Kong, Lingkai, Strieth-Kalthoff, Felix, Duan, Chenru, Zhuang, Yuchen, Yu, Yue, Zhu, Yanqiao, Du, Yuanqi, Aspuru-Guzik, Alán, Neklyudov, Kirill, Zhang, Chao
Molecular discovery, when formulated as an optimization problem, presents significant computational challenges because optimization objectives can be non-differentiable. Evolutionary Algorithms (EAs), often used to optimize black-box objectives in mo
Externí odkaz:
http://arxiv.org/abs/2406.16976
Autor:
Kong, Lingkai, Du, Yuanqi, Mu, Wenhao, Neklyudov, Kirill, De Bortoli, Valentin, Wu, Dongxia, Wang, Haorui, Ferber, Aaron, Ma, Yi-An, Gomes, Carla P., Zhang, Chao
Addressing real-world optimization problems becomes particularly challenging when analytic objective functions or constraints are unavailable. While numerous studies have addressed the issue of unknown objectives, limited research has focused on scen
Externí odkaz:
http://arxiv.org/abs/2402.18012
We construct two families of orthogonal polynomials associated with the universal central extensions of the superelliptic Lie algebras. These polynomials satisfy certain fourth order linear differential equations, and one of the families is a particu
Externí odkaz:
http://arxiv.org/abs/2402.02947
Autor:
Lin, Wu, Dangel, Felix, Eschenhagen, Runa, Neklyudov, Kirill, Kristiadi, Agustinus, Turner, Richard E., Makhzani, Alireza
Second-order methods such as KFAC can be useful for neural net training. However, they are often memory-inefficient since their preconditioning Kronecker factors are dense, and numerically unstable in low precision as they require matrix inversion or
Externí odkaz:
http://arxiv.org/abs/2312.05705
Autor:
Neklyudov, Kirill, Brekelmans, Rob, Tong, Alexander, Atanackovic, Lazar, Liu, Qiang, Makhzani, Alireza
The dynamical formulation of the optimal transport can be extended through various choices of the underlying geometry (kinetic energy), and the regularization of density paths (potential energy). These combinations yield different variational problem
Externí odkaz:
http://arxiv.org/abs/2310.10649
Autor:
Neklyudov, Kirill, Nys, Jannes, Thiede, Luca, Carrasquilla, Juan, Liu, Qiang, Welling, Max, Makhzani, Alireza
Solving the quantum many-body Schr\"odinger equation is a fundamental and challenging problem in the fields of quantum physics, quantum chemistry, and material sciences. One of the common computational approaches to this problem is Quantum Variationa
Externí odkaz:
http://arxiv.org/abs/2307.07050
In this paper we show variant of the spectral theorem using an algebraic Jordan-Schwinger map. The advantage of this approach is that we don't have restriction of normality on the class of operators we consider. On the other side, we have the restric
Externí odkaz:
http://arxiv.org/abs/2304.06778