10 citations · 26 across the 12 of their papers we have counts for
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cs.LG2023
A Unified Approach to Count-Based Weakly-Supervised Learning
Vinay Shukla, Zhe Zeng, Kareem Ahmed +1
High-quality labels are often very scarce, whereas unlabeled data with inferred weak labels occurs more naturally. In many cases, these weak labels dictate the frequency of each re…
cs.LG2023★ 4 cited
Probabilistically Rewired Message-Passing Neural Networks
Chendi Qian, Andrei Manolache, Kareem Ahmed +4
Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structured input. However, they operate on a fixed input graph structure, ignoring pote…
cs.LG2023★ 1 cited
Collapsed Inference for Bayesian Deep Learning
Zhe Zeng, Guy Van den Broeck
Bayesian neural networks (BNNs) provide a formalism to quantify and calibrate uncertainty in deep learning. Current inference approaches for BNNs often resort to few-sample estimat…