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The dynamics of cells, organisms, and fluids are often modeled as probability distributions evolving over time. Reconstructing and extrapolating this evolution from unpaired snapshots requires assumptions about the underlying process. Wasserstein gradient flows are a common choice, but they cannot describe conservative...
Probabilistic Causal Impact (PCI) builds on actual causality and on Pearl's notions of probability of necessity and sufficiency, but recasts the question of explainability as an estimation problem on a probabilistic causal model that is easily approximated via Monte Carlo.
It is demonstrated that the stitching method achieves state-of-the-art performance across trajectory inference benchmarks, and unifies several existing methods and leads to a new particle-based method, stitching, that is simulation-free and robust to large gaps between observations.
This work introduces A 2 -Judger, a novel MLLM-based A gentic instantiation of A uto Judger equipped with semantic-aware retrieval and dynamic memory that significantly improves sample efficiency while maintaining reliable evaluation results.
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