Deep Learning
Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in Continuous Control
Robot embodiments vary widely in their morphological ( e.g., number and connectivity
The Benefits of Balance: From Information Projections to Variance Reduction
ERM combines two well-understood techniques: mini-batch sampling and gradient-based optimization using backpropagation.
An Improved Empirical Fisher Approximation for Natural Gradient Descent
Despite its ease of implementation, the EF approximation has its theoretical and practical limitations.
EDT: An Efficient Diffusion Transformer Framework Inspired by Human-like Sketching
Transformer-based Diffusion Probabilistic Models (DPMs) have shown more potential than CNN-based DPMs, yet their extensive computational requirements hinder widespread practical applications.
SAND: Smooth Imputation Of Sparse And Noisy Functional Data With Transformer Networks
The conventional approach to imputing longitudinal data is to employ a parametric model with mixed-effects, such as the linear mixed-effects model.
Detecting Bugs with Substantial Monetary Consequences by LLM and Rule-based Reasoning
According to a recent study, accounting bugs (i.e., incorrect implementations of domain-specific financial models) are the most prevalent type of vulnerability, and are one of the most difficult to find,
Exploring the trade-off between deep-learning and explainable models for brain-machine interfaces Luis H. Cubillos
BMI applications, ranging from speech production to finger control.
