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Found 16 papers, 6 papers with code
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction
Here we propose a novel framework called Active Semi-supervised Graph Neural Network (ASGN) by incorporating both labeled and unlabeled molecules.
Heterogeneous Risk Minimization
In this paper, we propose Heterogeneous Risk Minimization (HRM) framework to achieve joint learning of latent heterogeneity among the data and invariant relationship, which leads to stable prediction despite distributional shifts.
When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?
Specifically, for general multi-layer PINNs and XPINNs, we first provide a prior generalization bound via the complexity of the target functions in the PDE problem, and a posterior generalization bound via the posterior matrix norms of the networks after optimization.
Kernelized Heterogeneous Risk Minimization
The ability to generalize under distributional shifts is essential to reliable machine learning, while models optimized with empirical risk minimization usually fail on non-$i. i. d$ testing data.
Integrated Latent Heterogeneity and Invariance Learning in Kernel Space
The ability to generalize under distributional shifts is essential to reliable machine learning, while models optimized with empirical risk minimization usually fail on non-$i. i. d$ testing data.
Enhancing Collaborative Filtering Recommender with Prompt-Based Sentiment Analysis
We build the recommenders on the Amazon US Reviews dataset, and tune the pretrained BERT and RoBERTa with the traditional fine-tuned paradigm as well as the new prompt-based learning paradigm.
Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology
We also show cases where XPINN is already better than PINN, so APINN can still slightly improve XPINN.
Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance
In this paper, we describe a system for vision-based dexterous manipulation that provides a "programming-free" approach for users to define new tasks and enable robots with complex multi-fingered hands to learn to perform them through interaction.
D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory
Kohn-Sham Density Functional Theory (KS-DFT) has been traditionally solved by the Self-Consistent Field (SCF) method.
Tackling the Curse of Dimensionality with Physics-Informed Neural Networks
We demonstrate in various diverse tests that the proposed method can solve many notoriously hard high-dimensional PDEs, including the Hamilton-Jacobi-Bellman (HJB) and the Schr\"{o}dinger equations in tens of thousands of dimensions very fast on a single GPU using the PINNs mesh-free approach.
REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation
We demonstrate the benefits of reusing past data as replay buffer initialization for new tasks, for instance, the fast acquisition of intricate manipulation skills in the real world on a four-fingered robotic hand.
Bias-Variance Trade-off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs
To optimize the bias-variance trade-off, we combine the two approaches in a hybrid method that balances the rapid convergence of the biased version with the high accuracy of the unbiased version.
Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks
We further showcase HTE's convergence to the original PINN loss and its unbiased behavior under specific conditions.
SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning
We posit that a significant challenge to widespread adoption of robotic RL, as well as further development of robotic RL methods, is the comparative inaccessibility of such methods.
Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck Equations
The score function, defined as the gradient of the LL, plays a fundamental role in inferring LL and PDF and enables fast SDE sampling.
Yell At Your Robot: Improving On-the-Fly from Language Corrections
In this paper, we make the following observation: high-level policies that index into sufficiently rich and expressive low-level language-conditioned skills can be readily supervised with human feedback in the form of language corrections.
