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Found 9 papers, 5 papers with code
Uncertainty Estimation of Transformer Predictions for Misclassification Detection
Uncertainty estimation (UE) of model predictions is a crucial step for a variety of tasks such as active learning, misclassification detection, adversarial attack detection, out-of-distribution detection, etc.
LM-Polygraph: Uncertainty Estimation for Language Models
Recent advancements in the capabilities of large language models (LLMs) have paved the way for a myriad of groundbreaking applications in various fields.
One-Step Distributional Reinforcement Learning
Reinforcement learning (RL) allows an agent interacting sequentially with an environment to maximize its long-term expected return.
Distributional Reinforcement Learning
reinforcement-learning
+1
ScaleFace: Uncertainty-aware Deep Metric Learning
The performance of modern deep learning-based systems dramatically depends on the quality of input objects.
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network
This paper proposes a fast and scalable method for uncertainty quantification of machine learning models' predictions.
How Certain is Your Transformer?
In this work, we consider the problem of uncertainty estimation for Transformer-based models.
EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data
In this paper, we discuss how modern deep learning approaches can be applied to the credit scoring of bank clients.
Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampling
Uncertainty estimation for machine learning models is of high importance in many scenarios such as constructing the confidence intervals for model predictions and detection of out-of-distribution or adversarially generated points.
Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data
Financial institutions obtain enormous amounts of data about user transactions and money transfers, which can be considered as a large graph dynamically changing in time.
