Mark Coates

Hybrid multi-Bernoulli and CPHD filters for superpositional sensors

2015 · IEEE Transactions on Aerospace and Electronic Systems

In this paper we present an approximate multi-Bernoulli filter and an approximate hybrid multi-Bernoulli cardinalized probability hypothesis density filter for superpositional sensors. The approximate-filter equations are derived by assuming that the predicted and posterior multitarget …

A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks

2020

Personalized recommender systems are playing an increasingly important role for online consumption platforms. Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize …

On the Design of Channel Coding Autoencoders With Arbitrary Rates for ISI Channels

2021 · IEEE Wireless Communications Letters

This letter presents an autoencoder-based channel coding scheme in the presence of inter-symbol interference (ISI) and additive white Gaussian noise (AWGN), supporting arbitrary coding rates. Both the transmitter and receiver of the proposed autoencoder employ …

Graph Inductive Biases in Transformers without Message Passing

2023 · arXiv (Cornell University)

Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial for Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings. However, Graph …

Plain Transformers Can be Powerful Graph Learners

2025 · arXiv (Cornell University)

Transformers have attained outstanding performance across various modalities, owing to their simple but powerful scaled-dot-product (SDP) attention mechanisms. Researchers have attempted to migrate Transformers to graph learning, but most advanced Graph Transformers (GTs) have strayed …