66 citations · 67 across the 3 of their papers we have counts for
7 papers
Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization
Francesco Pelosin, Saurav Jha, Andrea Torsello +2
In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the k…
Learning Domain Specific Language Models for Automatic Speech Recognition through Machine Translation
Saurav Jha
Automatic Speech Recognition (ASR) systems have been gaining popularity in the recent years for their widespread usage in smart phones and speakers. Building ASR systems for task-s…
Continual Learning in Sensor-based Human Activity Recognition: an Empirical Benchmark Analysis
Saurav Jha, Martin Schiemer, Franco Zambonelli +1
Sensor-based human activity recognition (HAR), i.e., the ability to discover human daily activity patterns from wearable or embedded sensors, is a key enabler for many real-world a…
Continual Learning in Human Activity Recognition: an Empirical Analysis of Regularization
Saurav Jha, Martin Schiemer, Juan Ye
Given the growing trend of continual learning techniques for deep neural networks focusing on the domain of computer vision, there is a need to identify which of these generalizes…
Learning cross-lingual phonological and orthagraphic adaptations: a case study in improving neural machine translation between low-resource languages
Saurav Jha, Akhilesh Sudhakar, Anil Kumar Singh
Out-of-vocabulary (OOV) words can pose serious challenges for machine translation (MT) tasks, and in particular, for low-resource language (LRL) pairs, i.e., language pairs for whi…
Multi Task Deep Morphological Analyzer: Context Aware Joint Morphological Tagging and Lemma Prediction
Saurav Jha, Akhilesh Sudhakar, Anil Kumar Singh
The ambiguities introduced by the recombination of morphemes constructing several possible inflections for a word makes the prediction of syntactic traits in Morphologically Rich L…