Search Results for author:
Found 9 papers, 4 papers with code
Revisiting Heterophily For Graph Neural Networks
ACM is more powerful than the commonly used uni-channel framework for node classification tasks on heterophilic graphs and is easy to be implemented in baseline GNN layers.
Inductive Bias
Node Classification on Non-Homophilic (Heterophilic) Graphs
N-Grammer: Augmenting Transformers with latent n-grams
Transformer models have recently emerged as one of the foundational models in natural language processing, and as a byproduct, there is significant recent interest and investment in scaling these models.
Ranked #5 on
Language Modelling
on C4
Is Heterophily A Real Nightmare For Graph Neural Networks on Performing Node Classification?
In this paper, we first show that not all cases of heterophily are harmful for GNNs with aggregation operation.
Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?
In this paper, we first show that not all cases of heterophily are harmful for GNNs with aggregation operation.
Ranked #1 on
Node Classification
on Pubmed
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning
We present an end-to-end, model-based deep reinforcement learning agent which dynamically attends to relevant parts of its state during planning.
Model-based Reinforcement Learning
Out-of-Distribution Generalization
+2
Is Heterophily A Real Nightmare For Graph Neural Networks Performing Node Classification?
In this paper, we first show that not all cases of heterophily are harmful for GNNs with aggregation operation.
A comparison of end-to-end models for long-form speech recognition
In this paper, we both investigate and improve the performance of end-to-end models on long-form transcription.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Gmail Smart Compose: Real-Time Assisted Writing
In this paper, we present Smart Compose, a novel system for generating interactive, real-time suggestions in Gmail that assists users in writing mails by reducing repetitive typing.
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
