Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Tutunov, Rasul"'
This paper delves into the capabilities of large language models (LLMs), specifically focusing on advancing the theoretical comprehension of chain-of-thought prompting. We investigate how LLMs can be effectively induced to generate a coherent chain o
Externí odkaz:
http://arxiv.org/abs/2310.13571
Autor:
Maraval, Alexandre, Zimmer, Matthieu, Grosnit, Antoine, Tutunov, Rasul, Wang, Jun, Ammar, Haitham Bou
Faced with problems of increasing complexity, recent research in Bayesian Optimisation (BO) has focused on adapting deep probabilistic models as flexible alternatives to Gaussian Processes (GPs). In a similar vein, this paper investigates the feasibi
Externí odkaz:
http://arxiv.org/abs/2205.13902
The Schr\"odinger equation is at the heart of modern quantum mechanics. Since exact solutions of the ground state are typically intractable, standard approaches approximate Schr\"odinger equation as forms of nonlinear generalized eigenvalue problems
Externí odkaz:
http://arxiv.org/abs/2202.01388
Autor:
Khan, Asif, Cowen-Rivers, Alexander I., Grosnit, Antoine, Deik, Derrick-Goh-Xin, Robert, Philippe A., Greiff, Victor, Smorodina, Eva, Rawat, Puneet, Dreczkowski, Kamil, Akbar, Rahmad, Tutunov, Rasul, Bou-Ammar, Dany, Wang, Jun, Storkey, Amos, Bou-Ammar, Haitham
Antibodies are canonically Y-shaped multimeric proteins capable of highly specific molecular recognition. The CDRH3 region located at the tip of variable chains of an antibody dominates antigen-binding specificity. Therefore, it is a priority to desi
Externí odkaz:
http://arxiv.org/abs/2201.12570
Autor:
Grosnit, Antoine, Malherbe, Cedric, Tutunov, Rasul, Wan, Xingchen, Wang, Jun, Ammar, Haitham Bou
Optimising the quality-of-results (QoR) of circuits during logic synthesis is a formidable challenge necessitating the exploration of exponentially sized search spaces. While expert-designed operations aid in uncovering effective sequences, the incre
Externí odkaz:
http://arxiv.org/abs/2111.06178
Autor:
Grosnit, Antoine, Tutunov, Rasul, Maraval, Alexandre Max, Griffiths, Ryan-Rhys, Cowen-Rivers, Alexander I., Yang, Lin, Zhu, Lin, Lyu, Wenlong, Chen, Zhitang, Wang, Jun, Peters, Jan, Bou-Ammar, Haitham
We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces. By adapting ideas from deep metric learning, we use label guidance from
Externí odkaz:
http://arxiv.org/abs/2106.03609
In this paper, we propose CI-VI an efficient and scalable solver for semi-implicit variational inference (SIVI). Our method, first, maps SIVI's evidence lower bound (ELBO) to a form involving a nonlinear functional nesting of expected values and then
Externí odkaz:
http://arxiv.org/abs/2101.06070
Autor:
Grosnit, Antoine, Cowen-Rivers, Alexander I., Tutunov, Rasul, Griffiths, Ryan-Rhys, Wang, Jun, Bou-Ammar, Haitham
Bayesian optimisation presents a sample-efficient methodology for global optimisation. Within this framework, a crucial performance-determining subroutine is the maximisation of the acquisition function, a task complicated by the fact that acquisitio
Externí odkaz:
http://arxiv.org/abs/2012.08240
Autor:
Cowen-Rivers, Alexander I., Lyu, Wenlong, Tutunov, Rasul, Wang, Zhi, Grosnit, Antoine, Griffiths, Ryan Rhys, Maraval, Alexandre Max, Jianye, Hao, Wang, Jun, Peters, Jan, Ammar, Haitham Bou
In this work we rigorously analyse assumptions inherent to black-box optimisation hyper-parameter tuning tasks. Our results on the Bayesmark benchmark indicate that heteroscedasticity and non-stationarity pose significant challenges for black-box opt
Externí odkaz:
http://arxiv.org/abs/2012.03826
In this paper, we present C-ADAM, the first adaptive solver for compositional problems involving a non-linear functional nesting of expected values. We proof that C-ADAM converges to a stationary point in $\mathcal{O}(\delta^{-2.25})$ with $\delta$ b
Externí odkaz:
http://arxiv.org/abs/2002.03755