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Andrea Gesmundo

4 accepted papers

2020

Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function

ICASSP 2020accepted

We propose a spiking neural network model that encodes information in the relative timing of individual neuron spikes and performs classification using the first output neuron to spike. This temporal coding scheme allows the supervised training of the network with backpropagation, using locally exac…

Cited by 0SourceScholar
2019

Parameter-Efficient Transfer Learning for NLP

ICML 2019oral

Fine-tuning large pretrained models is an effective transfer mechanism in NLP. However, in the presence of many downstream tasks, fine-tuning is parameter inefficient: an entire new model is required for every task. As an alternative, we propose transfer with adapter modules. Adapter modules yield a…

2018

Ask the Right Questions: Active Question Reformulation with Reinforcement Learning

ICLR 2018oral

We frame Question Answering (QA) as a Reinforcement Learning task, an approach that we call Active Question Answering. We propose an agent that sits between the user and a black box QA system and learns to reformulate questions to elicit the best possible answers. The agent probes the system with,…

Cited by 190SourcePDFScholar