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Found 9 papers, 0 papers with code
AmbigDocs: Reasoning across Documents on Different Entities under the Same Name
and a set of documents discussing different people named Michael Jordan, can LMs distinguish entity mentions to generate a cohesive answer to the question?
Stochastic approach for elliptic problems in perforated domains
We propose a neural network-based mesh-free approach for perforated domain problems.
Crafting In-context Examples according to LMs' Parametric Knowledge
We perform analysis on three multi-answer question answering datasets, which allows us to further study answer set ordering strategies based on the LM's knowledge of each answer.
Learning In-between Imagery Dynamics via Physical Latent Spaces
We present a framework designed to learn the underlying dynamics between two images observed at consecutive time steps.
An analysis of the derivative-free loss method for solving PDEs
This study analyzes the derivative-free loss method to solve a certain class of elliptic PDEs using neural networks.
Adaptive Tracking of a Single-Rigid-Body Character in Various Environments
Using the centroidal dynamics model (CDM) to express the full-body character as a single rigid body (SRB) and training a policy to track a reference motion, we can obtain a policy that is capable of adapting to various unobserved environmental changes and controller transitions without requiring any additional learning.
A Neural Network Approach for Homogenization of Multiscale Problems
Compared with other network-based approaches for multiscale problems, the proposed method is free from the design of hand-crafted neural network architecture and the cell problem to calculate the homogenization coefficient.
Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks
In this work, we propose a hierarchical approach to improve the convergence rate and accuracy of the neural network solution to partial differential equations.
Understanding the Stability of Deep Control Policies for Biped Locomotion
The goal of this study is to answer these questions by evaluating the push-recovery stability of deep policies compared to human subjects and a previous feedback controller.
