Copertina

Multi-task neural networks by learned contextual inputs

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Sandnes, Anders T., Grimstad, Bjarne, Kolbjørnsen, Odd
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Multi-task learning for virtual flow metering
di: Sandnes, Anders T., et al.
Pubblicazione: (2021)

Multi-task and few-shot learning in virtual flow metering
di: Løvland, Kristian, et al.
Pubblicazione: (2023)

The Unreasonable Effectiveness of Open Science in AI: A Replication Study
di: Gundersen, Odd Erik, et al.
Pubblicazione: (2024)

A deep latent variable model for semi-supervised multi-unit soft sensing in industrial processes
di: Grimstad, Bjarne, et al.
Pubblicazione: (2024)

A Spitting Image: Modular Superpixel Tokenization in Vision Transformers
di: Aasan, Marius, et al.
Pubblicazione: (2024)

Coding schemes in neural networks learning classification tasks
di: van Meegen, Alexander, et al.
Pubblicazione: (2024)

A lift for input-convex neural network training
di: Siahkoohi, Ali, et al.
Pubblicazione: (2026)

Training all-mechanical neural networks for task learning through in situ backpropagation
di: Li, Shuaifeng, et al.
Pubblicazione: (2024)

On weight and variance uncertainty in neural networks for regression tasks
di: Monemi, Moein, et al.
Pubblicazione: (2025)

Multi-task convolutional neural network for image aesthetic assessment
di: Soydaner, Derya, et al.
Pubblicazione: (2023)

On shallow feedforward neural networks with inputs from a topological space
di: Ismailov, Vugar
Pubblicazione: (2025)

Guiding the retraining of convolutional neural networks against adversarial inputs
di: López, Francisco Durán, et al.
Pubblicazione: (2022)

Evaluating alignment between humans and neural network representations in image-based learning tasks
di: Demircan, Can, et al.
Pubblicazione: (2023)

Central limit theorems for the outputs of fully convolutional neural networks with time series input
di: Betken, Annika, et al.
Pubblicazione: (2026)

Bayesian continual learning and forgetting in neural networks
di: Bonnet, Djohan, et al.
Pubblicazione: (2025)

Generating adversarial inputs for a graph neural network model of AC power flow
di: Parker, Robert
Pubblicazione: (2026)

Research on the application of graph data structure and graph neural network in node classification/clustering tasks
di: Wang, Yihan, et al.
Pubblicazione: (2025)

Gradient-based inference of abstract task representations for generalization in neural networks
di: Hummos, Ali, et al.
Pubblicazione: (2024)

Comparing concepts of quantum and classical neural network models for image classification task
di: Potempa, Rafal, et al.
Pubblicazione: (2021)

Statistical physics analysis of graph neural networks: Approaching optimality in the contextual stochastic block model
di: Duranthon, O., et al.
Pubblicazione: (2025)

The sampling complexity of learning invertible residual neural networks
di: Li, Yuanyuan, et al.
Pubblicazione: (2024)

Simmering: Sufficient is better than optimal for training neural networks
di: Babayan, Irina, et al.
Pubblicazione: (2024)

Effective backdoor attack on graph neural networks in link prediction tasks
di: Dai, Jiazhu, et al.
Pubblicazione: (2024)

The mathematics of adversarial attacks in AI -- Why deep learning is unstable despite the existence of stable neural networks
di: Bastounis, Alexander, et al.
Pubblicazione: (2021)

Kolmogorov-Arnold graph neural networks for chemically informed prediction tasks on inorganic nanomaterials
di: Volzhin, Nikita, et al.
Pubblicazione: (2025)

On the rates of convergence for learning with convolutional neural networks
di: Yang, Yunfei, et al.
Pubblicazione: (2024)

Early learning of the optimal constant solution in neural networks and humans
di: Rubruck, Jirko, et al.
Pubblicazione: (2024)

Emergence and scaling laws in SGD learning of shallow neural networks
di: Ren, Yunwei, et al.
Pubblicazione: (2025)

Optimal cross-learning for contextual bandits with unknown context distributions
di: Schneider, Jon, et al.
Pubblicazione: (2024)

Model selection for behavioral learning data and applications to contextual bandits
di: Aubert, Julien, et al.
Pubblicazione: (2025)

Inter-turbine Modelling of Wind-Farm Power using Multi-task Learning
di: Brealy, Simon M., et al.
Pubblicazione: (2025)

Feature learning is decoupled from generalization in high capacity neural networks
di: Göring, Niclas Alexander, et al.
Pubblicazione: (2025)

Progressive multi-fidelity learning with neural networks for physical system predictions
di: Conti, Paolo, et al.
Pubblicazione: (2025)

The impact of allocation strategies in subset learning on the expressive power of neural networks
di: Schlisselberg, Ofir, et al.
Pubblicazione: (2025)

ICNN-enhanced 2SP: Leveraging input convex neural networks for solving two-stage stochastic programming
di: Liu, Yu, et al.
Pubblicazione: (2025)

Convergence of gradient flow for learning convolutional neural networks
di: Diederen, Jona-Maria, et al.
Pubblicazione: (2026)

Automatic debiasing of neural networks via moment-constrained learning
di: Hines, Christian L., et al.
Pubblicazione: (2024)

Determining the severity of Parkinson's disease in patients using a multi task neural network
di: García-Ordás, María Teresa, et al.
Pubblicazione: (2024)

High-dimensional learning of narrow neural networks
di: Cui, Hugo
Pubblicazione: (2024)

One-step learning algorithm selection for classification via convolutional neural networks
di: Maldonado, Sebastian, et al.
Pubblicazione: (2023)