563 citations · 1.1k across the 17 of their papers we have counts for
6 papers · 1 filter
Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE
Qihuang Zhong, Liang Ding, Yibing Zhan +11
This technical report briefly describes our JDExplore d-team's Vega v2 submission on the SuperGLUE leaderboard. SuperGLUE is more challenging than the widely used general language…
Geodesic Graph Neural Network for Efficient Graph Representation Learning
Lecheng Kong, Yixin Chen, Muhan Zhang
Graph Neural Networks (GNNs) have recently been applied to graph learning tasks and achieved state-of-the-art (SOTA) results. However, many competitive methods run GNNs multiple ti…
How Powerful are K-hop Message Passing Graph Neural Networks
Jiarui Feng, Yixin Chen, Fuhai Li +2
The most popular design paradigm for Graph Neural Networks (GNNs) is 1-hop message passing -- aggregating information from 1-hop neighbors repeatedly. However, the expressive power…
Multi-Agent Path Planning with Asymmetric Interactions In Tight Spaces
Vismay Modi, Yixin Chen, Abhishek Madan +2
By starting with the assumption that motion is fundamentally a decision making problem, we use the world-line concept from Special Relativity as the inspiration for a novel multi-a…
SEA: Bridging the Gap Between One- and Two-stage Detector Distillation via SEmantic-aware Alignment
Yixin Chen, Zhuotao Tian, Pengguang Chen +2
We revisit the one- and two-stage detector distillation tasks and present a simple and efficient semantic-aware framework to fill the gap between them. We address the pixel-level i…
PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs
Zehao Dong, Muhan Zhang, Fuhai Li +1
Optimization of directed acyclic graph (DAG) structures has many applications, such as neural architecture search (NAS) and probabilistic graphical model learning. Encoding DAGs in…