Zobrazeno 1 - 10
of 108 802
pro vyhledávání: '"Bartlett, A"'
Federated Learning (FL) has emerged as a groundbreaking paradigm in collaborative machine learning, emphasizing decentralized model training to address data privacy concerns. While significant progress has been made in optimizing federated learning,
Externí odkaz:
http://arxiv.org/abs/2410.20659
Autor:
Sun, Hanshi, Haider, Momin, Zhang, Ruiqi, Yang, Huitao, Qiu, Jiahao, Yin, Ming, Wang, Mengdi, Bartlett, Peter, Zanette, Andrea
The safe and effective deployment of Large Language Models (LLMs) involves a critical step called alignment, which ensures that the model's responses are in accordance with human preferences. Prevalent alignment techniques, such as DPO, PPO and their
Externí odkaz:
http://arxiv.org/abs/2410.20290
Autor:
Sui, Ce, Bartlett, Deaglan J., Pandey, Shivam, Desmond, Harry, Ferreira, Pedro G., Wandelt, Benjamin D.
Current and future large scale structure surveys aim to constrain the neutrino mass and the equation of state of dark energy. We aim to construct accurate and interpretable symbolic approximations to the linear and nonlinear matter power spectra as a
Externí odkaz:
http://arxiv.org/abs/2410.14623
We identify regimes where post-selection can be used scalably in quantum error correction (QEC) to improve performance. We use statistical mechanical models to analytically quantify the performance and thresholds of post-selected QEC, with a focus on
Externí odkaz:
http://arxiv.org/abs/2410.07598
Autor:
Agarwal, Naman, Chen, Xinyi, Dogariu, Evan, Feinberg, Vlad, Suo, Daniel, Bartlett, Peter, Hazan, Elad
We address the challenge of efficient auto-regressive generation in sequence prediction models by introducing FutureFill - a method for fast generation that applies to any sequence prediction algorithm based on convolutional operators. Our approach r
Externí odkaz:
http://arxiv.org/abs/2410.03766
Autor:
Bartlett, Tara
The DARPA Subterranean Challenge is leading the development of robots capable of mapping underground mines and tunnels up to 8km in length and identify objects and people. Developing these autonomous abilities paves the way for future planetary cave
Externí odkaz:
http://arxiv.org/abs/2409.09967
Autor:
Bartlett, Tara, Manchester, Ian R.
This paper presents an algorithm that finds a centroidal motion and footstep plan for a Spring-Loaded Inverted Pendulum (SLIP)-like bipedal robot model substantially faster than real-time. This is achieved with a novel representation of the dynamic f
Externí odkaz:
http://arxiv.org/abs/2409.09939
Autor:
Pandey, Shivam, Modi, Chirag, Wandelt, Benjamin D., Bartlett, Deaglan J., Bayer, Adrian E., Bryan, Greg L., Ho, Matthew, Lavaux, Guilhem, Makinen, T. Lucas, Villaescusa-Navarro, Francisco
To maximize the amount of information extracted from cosmological datasets, simulations that accurately represent these observations are necessary. However, traditional simulations that evolve particles under gravity by estimating particle-particle i
Externí odkaz:
http://arxiv.org/abs/2409.09124
Fault-tolerant implementation of non-Clifford gates is a major challenge for achieving universal fault-tolerant quantum computing with quantum error-correcting codes. Magic state distillation is the most well-studied method for this but requires sign
Externí odkaz:
http://arxiv.org/abs/2409.07707
$N$-body simulations are computationally expensive, so machine-learning (ML)-based emulation techniques have emerged as a way to increase their speed. Although fast, surrogate models have limited trustworthiness due to potentially substantial emulati
Externí odkaz:
http://arxiv.org/abs/2409.02154