A type of artificial intelligence that focuses on designing systems that can learn from and make decisions based on data.
The study presents a method of dynamically selecting layers in deep transformer networks to reduce the number of trainable parameters. This is achieved by employing Reinforcement Learning to decide whether…
Continue readingThis paper presents MAPPING, a model-agnostic framework for debiasing Graph Neural Networks (GNNs) to ensure fair node classification and reduce leakage of sensitive information. Despite the success of GNNs in…
Continue readingThis research aims to improve the prediction of travelers’ next destinations by using a novel model architecture based on Long Short-Term Memory (LSTM). This approach is particularly useful in the…
Continue readingThe article discusses Iterated Relevance Matrix Analysis (IRMA), a method for identifying a linear subspace that represents class-specific information in a data set by repeatedly retraining Generalized Matrix Learning Vector…
Continue readingThe research addresses the challenge of enforcing safety in Reinforcement Learning (RL), crucial for its application in real-world scenarios. The researchers propose a method of overcoming the non-contractiveness of safety…
Continue readingThis article introduces a new algorithm based on the damped Newton method for the H tracking control problem of unknown continuous-time nonlinear systems. The algorithm, named model-free Policy Iteration (PI),…
Continue readingThis article explores the problem of dataset selection for training large-scale models. The authors argue that traditional methods, which filter data based on human notions of quality, often do not…
Continue readingThis paper introduces pyAKI, an open-source tool designed to standardize the implementation of Kidney Disease Improving Global Outcomes (KDIGO) criteria to time series data for acute kidney injury diagnosis. It…
Continue readingThe paper ‘Bayesian Semi-structured Subspace Inference’ presents a new method to address the problem of epistemic uncertainty in semi-structured regression models. The researchers propose a Bayesian approximation using subspace inference…
Continue readingThis paper investigates the biases present in Bengali sentiment analysis tools, particularly those influenced by the impacts of colonialism. The authors conducted an audit of all such tools available on…
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