Search Results for author: Georgiana Neculae

Found 2 papers, 1 papers with code

Retrieve to Explain: Evidence-driven Predictions with Language Models

1 code implementation6 Feb 2024 Ravi Patel, Angus Brayne, Rogier Hintzen, Daniel Jaroslawicz, Georgiana Neculae, Dane Corneil

R2E is a retrieval-based language model that prioritizes amongst a pre-defined set of possible answers to a research question based on the evidence in a document corpus, using Shapley values to identify the relative importance of pieces of evidence to the final prediction.

Language Modelling Retrieval

Ensembles of Spiking Neural Networks

no code implementations15 Oct 2020 Georgiana Neculae, Oliver Rhodes, Gavin Brown

The work demonstrates how ensembling can overcome the challenges of producing individual SNN models which can compete with traditional deep neural networks, and creates systems with fewer trainable parameters and smaller memory footprints, opening the door to low-power edge applications, e. g. implemented on neuromorphic hardware.

Ensemble Learning

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