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pro vyhledávání: '"A Lindsten"'
A prominent self-supervised learning paradigm is to model the representations as clusters, or more generally as a mixture model. Learning to map the data samples to compact representations and fitting the mixture model simultaneously leads to the rep
Externí odkaz:
http://arxiv.org/abs/2410.14060
Weather forecasting has seen a shift in methods from numerical simulations to data-driven systems. While initial research in the area focused on deterministic forecasting, recent works have used diffusion models to produce skillful ensemble forecasts
Externí odkaz:
http://arxiv.org/abs/2410.05431
Variational Autoencoders (VAEs) are a popular framework for unsupervised learning and data generation. A plethora of methods have been proposed focusing on improving VAEs, with the incorporation of adversarial objectives and the integration of prior
Externí odkaz:
http://arxiv.org/abs/2408.13805
Autor:
Olmin, Amanda, Lindsten, Fredrik
Epoch-wise double descent is the phenomenon where generalisation performance improves beyond the point of overfitting, resulting in a generalisation curve exhibiting two descents under the course of learning. Understanding the mechanisms driving this
Externí odkaz:
http://arxiv.org/abs/2407.09845
In recent years, machine learning has established itself as a powerful tool for high-resolution weather forecasting. While most current machine learning models focus on deterministic forecasts, accurately capturing the uncertainty in the chaotic weat
Externí odkaz:
http://arxiv.org/abs/2406.04759
The three-dimensional structure of a protein plays a key role in determining its function. Methods like AlphaFold have revolutionized protein structure prediction based only on the amino-acid sequence. However, proteins often appear in multiple diffe
Externí odkaz:
http://arxiv.org/abs/2407.01574
Self-distillation methods using Siamese networks are popular for self-supervised pre-training. DINO is one such method based on a cross-entropy loss between $K$-dimensional probability vectors, obtained by applying a softmax function to the dot produ
Externí odkaz:
http://arxiv.org/abs/2405.10939
Noise-contrastive estimation (NCE) is a popular method for estimating unnormalised probabilistic models, such as energy-based models, which are effective for modelling complex data distributions. Unlike classical maximum likelihood (ML) estimation th
Externí odkaz:
http://arxiv.org/abs/2402.16688
We introduce discriminator guidance in the setting of Autoregressive Diffusion Models. The use of a discriminator to guide a diffusion process has previously been used for continuous diffusion models, and in this work we derive ways of using a discri
Externí odkaz:
http://arxiv.org/abs/2310.15817
The rise of accurate machine learning methods for weather forecasting is creating radical new possibilities for modeling the atmosphere. In the time of climate change, having access to high-resolution forecasts from models like these is also becoming
Externí odkaz:
http://arxiv.org/abs/2309.17370