510 citations · 998 across the 19 of their papers we have counts for
8 papers · 1 filter
Pruning at a Glance: Global Neural Pruning for Model Compression
Abdullah Salama, Oleksiy Ostapenko, Tassilo Klein +1
Deep Learning models have become the dominant approach in several areas due to their high performance. Unfortunately, the size and hence computational requirements of operating suc…
Multimodal Self-Supervised Learning for Medical Image Analysis
Aiham Taleb, Christoph Lippert, Tassilo Klein +1
Self-supervised learning approaches leverage unlabeled samples to acquire generic knowledge about different concepts, hence allowing for annotation-efficient downstream task learni…
Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds
Tassilo Klein, Moin Nabi
Automatic question generation aims at the generation of questions from a context, with the corresponding answers being sub-spans of the given passage. Whereas, most of the methods…
Uncertainty-Driven Semantic Segmentation through Human-Machine Collaborative Learning
Mahdyar Ravanbakhsh, Tassilo Klein, Kayhan Batmanghelich +1
Deep learning-based approaches achieve state-of-the-art performance in the majority of image segmentation benchmarks. However, training of such models requires a sizable amount of…
Attention Is (not) All You Need for Commonsense Reasoning
Tassilo Klein, Moin Nabi
The recently introduced BERT model exhibits strong performance on several language understanding benchmarks. In this paper, we describe a simple re-implementation of BERT for commo…
Budget-Aware Adapters for Multi-Domain Learning
Rodrigo Berriel, Stéphane Lathuilière, Moin Nabi +4
Multi-Domain Learning (MDL) refers to the problem of learning a set of models derived from a common deep architecture, each one specialized to perform a task in a certain domain (e…