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Found 10 papers, 2 papers with code
The Power of Noise: Redefining Retrieval for RAG Systems
Retrieval-Augmented Generation (RAG) has recently emerged as a method to extend beyond the pre-trained knowledge of Large Language Models by augmenting the original prompt with relevant passages or documents retrieved by an Information Retrieval (IR) system.
A graph neural network-based model with Out-of-Distribution Robustness for enhancing Antiretroviral Therapy Outcome Prediction for HIV-1
We evaluated these models' robustness against Out-of-Distribution drugs in the test set, with a specific focus on the GNN's role in handling such scenarios.
Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It
Fake news detection models are critical to countering disinformation but can be manipulated through adversarial attacks.
RRAML: Reinforced Retrieval Augmented Machine Learning
The emergence of large language models (LLMs) has revolutionized machine learning and related fields, showcasing remarkable abilities in comprehending, generating, and manipulating human language.
Investigating the Robustness of Sequential Recommender Systems Against Training Data Perturbations
However, their robustness in the face of perturbations in training data remains a largely understudied yet critical issue.
Integrating Item Relevance in Training Loss for Sequential Recommender Systems
Sequential Recommender Systems (SRSs) are a popular type of recommender system that learns from a user's history to predict the next item they are likely to interact with.
Sheaf4Rec: Sheaf Neural Networks for Graph-based Recommender Systems
GNNs have proven to be effective in addressing the challenges posed by recommendation systems by efficiently modeling graphs in which nodes represent users or items and edges denote preference relationships.
Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels
In practical settings, classification datasets are obtained through a labelling process that is usually done by humans.
Encoding Concepts in Graph Neural Networks
The opaque reasoning of Graph Neural Networks induces a lack of human trust.
NEWRON: A New Generalization of the Artificial Neuron to Enhance the Interpretability of Neural Networks
In this work, we formulate NEWRON: a generalization of the McCulloch-Pitts neuron structure.
