Zobrazeno 1 - 10
of 2 612
pro vyhledávání: '"BARBER, DAVID"'
Training generative models to sample from unnormalized density functions is an important and challenging task in machine learning. Traditional training methods often rely on the reverse Kullback-Leibler (KL) divergence due to its tractability. Howeve
Externí odkaz:
http://arxiv.org/abs/2410.12456
Linear recurrent neural networks, such as State Space Models (SSMs) and Linear Recurrent Units (LRUs), have recently shown state-of-the-art performance on long sequence modelling benchmarks. Despite their success, their empirical performance is not w
Externí odkaz:
http://arxiv.org/abs/2407.07239
The probabilistic diffusion model has become highly effective across various domains. Typically, sampling from a diffusion model involves using a denoising distribution characterized by a Gaussian with a learned mean and either fixed or learned covar
Externí odkaz:
http://arxiv.org/abs/2406.10808
The time complexity of the standard attention mechanism in transformers scales quadratically with sequence length. We propose a probabilistic framework for attention, enabling us to derive a novel low-rank linear re-parameterisation of both bidirecti
Externí odkaz:
http://arxiv.org/abs/2402.17512
Retrieval Augmented Generation (RAG) has emerged as an effective solution for mitigating hallucinations in Large Language Models (LLMs). The retrieval stage in RAG typically involves a pre-trained embedding model, which converts queries and passages
Externí odkaz:
http://arxiv.org/abs/2402.12177
As large language models (LLMs) become more capable, fine-tuning techniques for aligning with human intent are increasingly important. A key consideration for aligning these models is how to most effectively use human resources, or model resources in
Externí odkaz:
http://arxiv.org/abs/2402.08114
The inadequate mixing of conventional Markov Chain Monte Carlo (MCMC) methods for multi-modal distributions presents a significant challenge in practical applications such as Bayesian inference and molecular dynamics. Addressing this, we propose Diff
Externí odkaz:
http://arxiv.org/abs/2402.03008
Autor:
Shahin, Ahmed H., Zhao, An, Whitehead, Alexander C., Alexander, Daniel C., Jacob, Joseph, Barber, David
Survival analysis is a valuable tool for estimating the time until specific events, such as death or cancer recurrence, based on baseline observations. This is particularly useful in healthcare to prognostically predict clinically important events ba
Externí odkaz:
http://arxiv.org/abs/2309.03851
Energy-Based Models (EBMs) offer a versatile framework for modeling complex data distributions. However, training and sampling from EBMs continue to pose significant challenges. The widely-used Denoising Score Matching (DSM) method for scalable EBM t
Externí odkaz:
http://arxiv.org/abs/2305.11650