2 citations · 2 across the 6 of their papers we have counts for
6 papers
Text2Touch: Tactile In-Hand Manipulation with LLM-Designed Reward Functions
Harrison Field, Max Yang, Yijiong Lin +3
Large language models (LLMs) are beginning to automate reward design for dexterous manipulation. However, no prior work has considered tactile sensing, which is known to be critica…
Weight-Space Linear Recurrent Neural Networks
Roussel Desmond Nzoyem, Nawid Keshtmand, Enrique Crespo Fernandez +4
We introduce WARP (Weight-space Adaptive Recurrent Prediction), a simple yet powerful model that unifies weight-space learning with linear recurrence to redefine sequence modeling.…
Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts
Roussel Desmond Nzoyem, Grant Stevens, Amarpal Sahota +2
As foundational models reshape scientific discovery, a bottleneck persists in dynamical system reconstruction (DSR): the ability to learn across system hierarchies. Many meta-learn…
Reevaluating Meta-Learning Optimization Algorithms Through Contextual Self-Modulation
Roussel Desmond Nzoyem, David A. W. Barton, Tom Deakin
Contextual Self-Modulation (CSM) (Nzoyem et al., 2025) is a potent regularization mechanism for Neural Context Flows (NCFs) which demonstrates powerful meta-learning on physical sy…
Neural Context Flows for Meta-Learning of Dynamical Systems
Roussel Desmond Nzoyem, David A. W. Barton, Tom Deakin
Neural Ordinary Differential Equations (NODEs) often struggle to adapt to new dynamic behaviors caused by parameter changes in the underlying physical system, even when these dynam…
A Comparison of Mesh-Free Differentiable Programming and Data-Driven Strategies for Optimal Control under PDE Constraints
Roussel Desmond Nzoyem, David A. W. Barton, Tom Deakin
The field of Optimal Control under Partial Differential Equations (PDE) constraints is rapidly changing under the influence of Deep Learning and the accompanying automatic differen…