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Found 10 papers, 5 papers with code
$β$-calibration of Language Model Confidence Scores for Generative QA
To use generative question-and-answering (QA) systems for decision-making and in any critical application, these systems need to provide well-calibrated confidence scores that reflect the correctness of their answers.
Estimating Joint interventional distributions from marginal interventional data
In this paper we show how to exploit interventional data to acquire the joint conditional distribution of all the variables using the Maximum Entropy principle.
Score matching through the roof: linear, nonlinear, and latent variables causal discovery
Causal discovery from observational data holds great promise, but existing methods rely on strong assumptions about the underlying causal structure, often requiring full observability of all relevant variables.
The PetShop Dataset -- Finding Causes of Performance Issues across Microservices
Although the machine learning and systems research communities have proposed various techniques to tackle this problem, there is currently a lack of standardized datasets for quantitative benchmarking.
Beyond Single-Feature Importance with ICECREAM
Which set of features was responsible for a certain output of a machine learning model?
Causal Inference Through the Structural Causal Marginal Problem
We introduce an approach to counterfactual inference based on merging information from multiple datasets.
Obtaining Causal Information by Merging Datasets with MAXENT
Another similarly important and challenging task is to quantify the causal influence of a treatment on a target in the presence of confounders.
DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging
In order to use the data gained with calcium imaging, it is necessary to extract individual cells and their activity from the recordings.
LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos
Neuronal assemblies, loosely defined as subsets of neurons with reoccurring spatio-temporally coordinated activation patterns, or "motifs", are thought to be building blocks of neural representations and information processing.
Sparse convolutional coding for neuronal assembly detection
Cell assemblies, originally proposed by Donald Hebb (1949), are subsets of neurons firing in a temporally coordinated way that gives rise to repeated motifs supposed to underly neural representations and information processing.
