89 citations · 473 across the 107 of their papers we have counts for
23 papers · 1 filter
Semantic-Based Few-Shot Learning by Interactive Psychometric Testing
Lu Yin, Vlado Menkovski, Yulong Pei +1
Few-shot classification tasks aim to classify images in query sets based on only a few labeled examples in support sets. Most studies usually assume that each image in a task has a…
The Impact of Batch Learning in Stochastic Bandits
Danil Provodin, Pratik Gajane, Mykola Pechenizkiy +1
We consider a special case of bandit problems, namely batched bandits. Motivated by natural restrictions of recommender systems and e-commerce platforms, we assume that a learning…
Calibrated Adversarial Training
Tianjin Huang, Vlado Menkovski, Yulong Pei +1
Adversarial training is an approach of increasing the robustness of models to adversarial attacks by including adversarial examples in the training set. One major challenge of prod…
Avoiding Forgetting and Allowing Forward Transfer in Continual Learning via Sparse Networks
Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy
Using task-specific components within a neural network in continual learning (CL) is a compelling strategy to address the stability-plasticity dilemma in fixed-capacity models with…
The Banking Transactions Dataset and its Comparative Analysis with Scale-free Networks
Akrati Saxena, Yulong Pei, Jan Veldsink +3
We construct a network of 1.6 million nodes from banking transactions of users of Rabobank. We assign two weights on each edge, which are the aggregate transferred amount and the t…
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection
Iftitahu Ni'mah, Meng Fang, Vlado Menkovski +1
The ability to detect Out-of-Domain (OOD) inputs has been a critical requirement in many real-world NLP applications. For example, intent classification in dialogue systems. The re…