Learning, inference, and decision-making from perceived data.
Machine Learning is one of the core topic programs in the intelligence pillar of Neuroide. This topic covers probabilistic inference, optimization, representation learning, uncertainty, robustness, and evaluation as the methods that turn perceived data into internal models and decisions.
Bayesian approximations, variational methods, latent-variable models, and structured uncertainty.
Contrastive learning, self-supervision, multimodal embeddings, and geometry of learned spaces.
Calibration, robustness, distribution shift, error analysis, and benchmark design.
Loss design, regularization, scaling behavior, and the computational logic behind modern training.
Self-Supervised Representation Learning for Human Physiological Data
A stronger article on predictive, contrastive, masked, and multimodal pretraining for structured biosignal learning.
Multimodal Diffusion in Latent Space
Latent generative modeling, multimodal alignment, and architectural choices that matter for modern foundation systems.
Multimodal Biosignal Foundation Models
Reusable physiological representations, missing-modality robustness, and cross-modal pretraining at scale.