Education
GEPS: Boosting Generalization in Parametric PDE Neural Solvers through Adaptive Conditioning
We demonstrate the versatility of our approach for both fully data-driven and for physics-aware neural solvers.
WorldCoder,a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment
We give a model-based agent that builds a Python program representing its knowledge of the world based on its interactions with the environment.
Language models are weak learners
A central notion in practical and theoretical machine learning is that of a weak learner, classifiers that achieve better-than-random performance (on any given distribution over data), even by a small margin. Such weak learners form the practical basis for canonical machine learning methods such as boosting.
9f42f06a54ce3b709ad78d34c73e4363-Paper-Conference.pdf
Therefore, neuro-symbolic RL aims at creating policies that are interpretable in the first place. Unfortunately, interpretability is not explainability. To achieve both, we introduce Neurally gUided Differentiable loGic policiEs (NUDGE).
Continual Learning with Global Alignment
Specifically, we learn the data representation as a task-specific composition of pre-trained token representations shared across all tasks. Then the correlations between different tasks' data representations are grounded
Bowen Li
All the code and data are open-sourced at our website.