1 citations · 1 across the 8 of their papers we have counts for
9 papers
Rethinking Explanations: Formalizing Contrast in Description Logics
Yasir Mahmood, Arnab Sharma, Axel-Cyrille Ngonga Ngomo +1
There has been a growing interest in explaining entailments over description logic (DL) knowledge bases. The existing explanation formalisms focus on justifications to explain true…
Learning to Retrieve with Weakened Labels: Robust Training under Label Noise
Arnab Sharma
Neural Encoders are frequently used in the NLP domain to perform dense retrieval tasks, for instance, to generate the candidate documents for a given query in question-answering ta…
Parameter Averaging in Link Prediction
Rupesh Sapkota, Caglar Demir, Arnab Sharma +1
Ensemble methods are widely employed to improve generalization in machine learning. This has also prompted the adoption of ensemble learning for the knowledge graph embedding (KGE)…
Diving Deep: Forecasting Sea Surface Temperatures and Anomalies
Ding Ning, Varvara Vetrova, Karin R. Bryan +4
This overview paper details the findings from the Diving Deep: Forecasting Sea Surface Temperatures and Anomalies Challenge at the European Conference on Machine Learning and Princ…
Resilience in Knowledge Graph Embeddings
Arnab Sharma, N'Dah Jean Kouagou, Axel-Cyrille Ngonga Ngomo
In recent years, knowledge graphs have gained interest and witnessed widespread applications in various domains, such as information retrieval, question-answering, recommendation s…
Inference over Unseen Entities, Relations and Literals on Knowledge Graphs
Caglar Demir, N'Dah Jean Kouagou, Arnab Sharma +1
In recent years, knowledge graph embedding models have been successfully applied in the transductive setting to tackle various challenging tasks including link prediction, and quer…
Performance Evaluation of Knowledge Graph Embedding Approaches under Non-adversarial Attacks
Sourabh Kapoor, Arnab Sharma, Michael Röder +2
Knowledge Graph Embedding (KGE) transforms a discrete Knowledge Graph (KG) into a continuous vector space facilitating its use in various AI-driven applications like Semantic Searc…
Adaptive Stochastic Weight Averaging
Caglar Demir, Arnab Sharma, Axel-Cyrille Ngonga Ngomo
Ensemble models often improve generalization performances in challenging tasks. Yet, traditional techniques based on prediction averaging incur three well-known disadvantages: the…
EGNN-C+: Interpretable Evolving Granular Neural Network and Application in Classification of Weakly-Supervised EEG Data Streams
Daniel Leite, Alisson Silva, Gabriella Casalino +3
We introduce a modified incremental learning algorithm for evolving Granular Neural Network Classifiers (eGNN-C+). We use double-boundary hyper-boxes to represent granules, and cus…