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Found 8 papers, 3 papers with code
Quantum State Generation with Structure-Preserving Diffusion Model
This article considers the generative modeling of the states of quantum systems, and an approach based on denoising diffusion model is proposed.
Meaningful Causal Aggregation and Paradoxical Confounding
On the positive side, we show that cause-effect relations can be aggregated when the macro interventions are such that the distribution of micro states is the same as in the observational distribution; we term this natural macro interventions.
Deep Learning for Mean Field Optimal Transport
The first one is based on directly learning an optimal control.
Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach
We propose a kernel-based nonparametric estimator for the causal effect when the cause is corrupted by error.
Causal Effect Inference for Structured Treatments
We address the estimation of conditional average treatment effects (CATEs) for structured treatments (e. g., graphs, images, texts).
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction
In particular, we provide a unifying view of two-stage and moment restriction approaches for solving this problem in a nonlinear setting.
Model Rectification via Unknown Unknowns Extraction from Deployment Samples
Model deficiency that results from incomplete training data is a form of structural blindness that leads to costly errors, oftentimes with high confidence.
EQuANt (Enhanced Question Answer Network)
By training and evaluating EQuANt on SQuAD 2, we show that it is indeed possible to extend QANet to the unanswerable domain.
