GCTM-OT extracts goal candidates with an LLM, then uses goal-prompted contrastive learning and optimal transport to discover topics that are more coherent, diverse, and aligned with human intent than prior methods on subreddit data.
Kingma and Max Welling
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
BVME uses variational Gaussian message encoding with KL regularization to maintain or improve multi-agent coordination performance while using 67-83% fewer message dimensions than naive compression on SMAC and MPE benchmarks.
UAU-Net improves facial action unit detection by modeling uncertainty at both representation learning via conditional VAE and evidential classification via asymmetric Beta network.
citing papers explorer
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Human-Centric Topic Modeling with Goal-Prompted Contrastive Learning and Optimal Transport
GCTM-OT extracts goal candidates with an LLM, then uses goal-prompted contrastive learning and optimal transport to discover topics that are more coherent, diverse, and aligned with human intent than prior methods on subreddit data.
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Bandwidth-constrained Variational Message Encoding for Cooperative Multi-agent Reinforcement Learning
BVME uses variational Gaussian message encoding with KL regularization to maintain or improve multi-agent coordination performance while using 67-83% fewer message dimensions than naive compression on SMAC and MPE benchmarks.
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UAU-Net: Uncertainty-aware Representation Learning and Evidential Classification for Facial Action Unit Detection
UAU-Net improves facial action unit detection by modeling uncertainty at both representation learning via conditional VAE and evidential classification via asymmetric Beta network.