Copertina

Pretraining with random noise for uncertainty calibration

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Cheon, Jeonghwan, Paik, Se-Bum
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence Neural and Evolutionary Computing
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Pretraining with Random Noise for Fast and Robust Learning without Weight Transport
di: Cheon, Jeonghwan, et al.
Pubblicazione: (2024)

Neuromimetic metaplasticity for adaptive continual learning
di: Cho, Suhee, et al.
Pubblicazione: (2024)

Paradoxical noise preference in RNNs
di: Eckstein, Noah, et al.
Pubblicazione: (2026)

Particle swarm optimization for online sparse streaming feature selection under uncertainty
di: Xu, Ruiyang
Pubblicazione: (2025)

CoEvo: Continual Evolution of Symbolic Solutions Using Large Language Models
di: Guo, Ping, et al.
Pubblicazione: (2024)

A Learn-to-Optimize Approach for Coordinate-Wise Step Sizes for Quasi-Newton Methods
di: Lin, Wei, et al.
Pubblicazione: (2024)

Toward End-to-End Bearing Fault Diagnosis for Industrial Scenarios with Spiking Neural Networks
di: Zuo, Lin, et al.
Pubblicazione: (2024)

Reconsidering the energy efficiency of spiking neural networks
di: Yan, Zhanglu, et al.
Pubblicazione: (2024)

Hilbert curves for efficient exploratory landscape analysis neighbourhood sampling
di: Pienaar, Johannes J., et al.
Pubblicazione: (2024)

Towards evolution of Deep Neural Networks through contrastive Self-Supervised learning
di: Vinhas, Adriano, et al.
Pubblicazione: (2024)

MeGA: Merging Multiple Independently Trained Neural Networks Based on Genetic Algorithm
di: Yun, Daniel
Pubblicazione: (2024)

Space-Time Continuous PDE Forecasting using Equivariant Neural Fields
di: Knigge, David M., et al.
Pubblicazione: (2024)

Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations
di: Csordás, Róbert, et al.
Pubblicazione: (2024)

Large-scale Multi-objective Feature Selection: A Multi-phase Search Space Shrinking Approach
di: Bidgoli, Azam Asilian, et al.
Pubblicazione: (2024)

Enhancing Neural Network Representations with Prior Knowledge-Based Normalization
di: Faye, Bilal, et al.
Pubblicazione: (2024)

BayesNAM: Leveraging Inconsistency for Reliable Explanations
di: Kim, Hoki, et al.
Pubblicazione: (2024)

Learning Sequence Attractors in Recurrent Networks with Hidden Neurons
di: Lu, Yao, et al.
Pubblicazione: (2024)

Restricted Bayesian Neural Network
di: Ganguly, Sourav, et al.
Pubblicazione: (2024)

Task and Explanation Network
di: Sipper, Moshe
Pubblicazione: (2024)

REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning
di: Altmann, Philipp, et al.
Pubblicazione: (2024)

Half-Space Feature Learning in Neural Networks
di: Yadav, Mahesh Lorik, et al.
Pubblicazione: (2024)

Towards a theory of model distillation
di: Boix-Adsera, Enric
Pubblicazione: (2024)

Generalized Population-Based Training for Hyperparameter Optimization in Reinforcement Learning
di: Bai, Hui, et al.
Pubblicazione: (2024)

Large Language Models as In-context AI Generators for Quality-Diversity
di: Lim, Bryan, et al.
Pubblicazione: (2024)

Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances
di: Hart, Emma, et al.
Pubblicazione: (2024)

Position: Leverage Foundational Models for Black-Box Optimization
di: Song, Xingyou, et al.
Pubblicazione: (2024)

A Resource Model For Neural Scaling Law
di: Song, Jinyeop, et al.
Pubblicazione: (2024)

Structurally Flexible Neural Networks: Evolving the Building Blocks for General Agents
di: Pedersen, Joachim Winther, et al.
Pubblicazione: (2024)

Greener GRASS: Enhancing GNNs with Encoding, Rewiring, and Attention
di: Liao, Tongzhou, et al.
Pubblicazione: (2024)

SemlaFlow -- Efficient 3D Molecular Generation with Latent Attention and Equivariant Flow Matching
di: Irwin, Ross, et al.
Pubblicazione: (2024)

A Theory of Synaptic Neural Balance: From Local to Global Order
di: Baldi, Pierre, et al.
Pubblicazione: (2024)

SpiKernel: A Kernel Size Exploration Methodology for Improving Accuracy of the Embedded Spiking Neural Network Systems
di: Putra, Rachmad Vidya Wicaksana, et al.
Pubblicazione: (2024)

Bayes-CATSI: A variational Bayesian deep learning framework for medical time series data imputation
di: Kulkarni, Omkar, et al.
Pubblicazione: (2024)

More Consideration for the Perceptron
di: Larabi, Slimane
Pubblicazione: (2024)

TX-Gen: Multi-Objective Optimization for Sparse Counterfactual Explanations for Time-Series Classification
di: Huang, Qi, et al.
Pubblicazione: (2024)

DEBUG-HD: Debugging TinyML models on-device using Hyper-Dimensional computing
di: Ghanathe, Nikhil P, et al.
Pubblicazione: (2024)

An Inverse Modeling Constrained Multi-Objective Evolutionary Algorithm Based on Decomposition
di: Farias, Lucas R. C., et al.
Pubblicazione: (2024)

On the Improvement of Generalization and Stability of Forward-Only Learning via Neural Polarization
di: Terres-Escudero, Erik B., et al.
Pubblicazione: (2024)

COSCO: A Sharpness-Aware Training Framework for Few-shot Multivariate Time Series Classification
di: Barreda, Jesus, et al.
Pubblicazione: (2024)

Continuous Spiking Graph Neural Networks
di: Yin, Nan, et al.
Pubblicazione: (2024)