8.8k citations · 10.6k across the 35 of their papers we have counts for
8 papers · 1 filter
Scaling Laws of Motion Forecasting and Planning -- Technical Report
Mustafa Baniodeh, Kratarth Goel, Scott Ettinger +14
We study the empirical scaling laws of a family of encoder-decoder autoregressive transformer models on the task of joint motion forecasting and planning in the autonomous driving…
Let Your Graph Do the Talking: Encoding Structured Data for LLMs
Bryan Perozzi, Bahare Fatemi, Dustin Zelle +4
How can we best encode structured data into sequential form for use in large language models (LLMs)? In this work, we introduce a parameter-efficient method to explicitly represent…
DDGK: Learning Graph Representations for Deep Divergence Graph Kernels
Rami Al-Rfou, Dustin Zelle, Bryan Perozzi
Can neural networks learn to compare graphs without feature engineering? In this paper, we show that it is possible to learn representations for graph similarity with neither domai…
Watch Your Step: Learning Node Embeddings via Graph Attention
Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou +1
Graph embedding methods represent nodes in a continuous vector space, preserving information from the graph (e.g. by sampling random walks). There are many hyper-parameters to thes…
Learning Edge Representations via Low-Rank Asymmetric Projections
Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou
We propose a new method for embedding graphs while preserving directed edge information. Learning such continuous-space vector representations (or embeddings) of nodes in a graph i…
Exploring the power of GPU's for training Polyglot language models
Vivek Kulkarni, Rami Al-Rfou', Bryan Perozzi +1
One of the major research trends currently is the evolution of heterogeneous parallel computing. GP-GPU computing is being widely used and several applications have been designed t…
Inducing Language Networks from Continuous Space Word Representations
Bryan Perozzi, Rami Al-Rfou, Vivek Kulkarni +1
Recent advancements in unsupervised feature learning have developed powerful latent representations of words. However, it is still not clear what makes one representation better th…
The Expressive Power of Word Embeddings
Yanqing Chen, Bryan Perozzi, Rami Al-Rfou +1
We seek to better understand the difference in quality of the several publicly released embeddings. We propose several tasks that help to distinguish the characteristics of differe…