217 citations · 651 across the 24 of their papers we have counts for
6 papers · 1 filter
End-to-end Differentiable Clustering with Associative Memories
Bishwajit Saha, Dmitry Krotov, Mohammed J. Zaki +1
Clustering is a widely used unsupervised learning technique involving an intensive discrete optimization problem. Associative Memory models or AMs are differentiable neural network…
The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles
Md Shamim Hussain, Mohammed J. Zaki, Dharmashankar Subramanian
Transformers use the dense self-attention mechanism which gives a lot of flexibility for long-range connectivity. Over multiple layers of a deep transformer, the number of possible…
Energy Transformer
Benjamin Hoover, Yuchen Liang, Bao Pham +5
Our work combines aspects of three promising paradigms in machine learning, namely, attention mechanism, energy-based models, and associative memory. Attention is the power-house d…
Global Self-Attention as a Replacement for Graph Convolution
Md Shamim Hussain, Mohammed J. Zaki, Dharmashankar Subramanian
We propose an extension to the transformer neural network architecture for general-purpose graph learning by adding a dedicated pathway for pairwise structural information, called…
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings
Yu Chen, Lingfei Wu, Mohammed J. Zaki
In this paper, we propose an end-to-end graph learning framework, namely Iterative Deep Graph Learning (IDGL), for jointly and iteratively learning graph structure and graph embedd…
Deep Iterative and Adaptive Learning for Graph Neural Networks
Yu Chen, Lingfei Wu, Mohammed J. Zaki
In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph s…