Search Results for author:
Found 6 papers, 4 papers with code
CAESAR: Enhancing Federated RL in Heterogeneous MDPs through Convergence-Aware Sampling with Screening
CAESAR is an aggregation strategy used by the server that combines convergence-aware sampling with a screening mechanism.
RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions
Depth estimation from monocular images is pivotal for real-world visual perception systems.
Prompting a Large Language Model to Generate Diverse Motivational Messages: A Comparison with Human-Written Messages
We then used this same pipeline to generate messages using GPT-4, and compared the collective diversity of messages from: (1) crowd-writers, (2) GPT-4 using the pipeline, and (3 & 4) two baseline GPT-4 prompts.
SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning
More recently, with the rapid development of language models (LMs), researchers have focused on leveraging LMs to facilitate the learning of TGs, either by jointly training them in a computationally intensive framework (merging the two stages), or designing complex self-supervised training tasks for feature extraction (enhancing the first stage).
Ranked #1 on
Node Property Prediction
on ogbn-arxiv
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
Shape-CD: Change-Point Detection in Time-Series Data with Shapes and Neurons
We found that existing approaches become less accurate when the underlying process is complex and generates large varieties of patterns in the time series.
