arXiv:2512.23853v1 Announce Type: new Abstract: Neural processes (NPs) are a class of models that learn stochastic processes directly from data and can be used for inference, sampling and conditional sampling. We introduce a new NP model based on flow matching,…
arXiv:2510.26745v2 Announce Type: replace Abstract: Deep sequence models are said to store atomic facts predominantly in the form of associative memory: a brute-force lookup of co-occurring entities. We identify a dramatically different form of storage of atomic facts that we…
arXiv:2307.07657v2 Announce Type: replace-cross Abstract: We consider the supervised learning problem of learning the price of an option or the implied volatility given appropriate input data (model parameters) and corresponding output data (option prices or implied volatilities). The majority of…
arXiv:2412.07454v3 Announce Type: replace Abstract: Federated learning enables decentralized model training without sharing raw data, preserving data privacy. However, its vulnerability towards critical security threats, such as gradient inversion and model poisoning by malicious clients, remain unresolved. Existing solutions often…
arXiv:2508.00706v2 Announce Type: replace Abstract: The application of message-passing Graph Neural Networks has been a breakthrough for important network science problems. However, the competitive performance often relies on using handcrafted structural features as inputs, which increases computational cost and introduces…
arXiv:2512.24794v1 Announce Type: cross Abstract: The Noise2Noise method allows for training machine learning-based denoisers with pairs of input and target images where both the input and target can be noisy. This removes the need for training with clean target images,…
arXiv:2010.11450v2 Announce Type: replace Abstract: A soft-max function has two main efficiency measures: (1) approximation – which corresponds to how well it approximates the maximum function, (2) smoothness – which shows how sensitive it is to changes of its input.…
arXiv:2512.23761v1 Announce Type: new Abstract: Advances in data acquisition and computational methods have accelerated the use of differential equation based modelling for complex systems. Such systems are often described by coupled (or more) variables, yet governing equation is typically available…
arXiv:2511.08579v2 Announce Type: replace-cross Abstract: Can language models (LMs) learn to faithfully describe their internal computations? Are they better able to describe themselves than other models? We study the extent to which LMs’ privileged access to their own internals can…
arXiv:2512.23753v1 Announce Type: new Abstract: Evidential deep learning (EDL) models, based on Subjective Logic, introduce a principled and computationally efficient way to make deterministic neural networks uncertainty-aware. The resulting evidential models can quantify fine-grained uncertainty using learned evidence. However, the…
