7 citations · 36 across the 19 of their papers we have counts for
8 papers · 1 filter
DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models
Nikolaos Louloudakis, Perry Gibson, José Cano +1
Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Fai…
Fault Localization for Buggy Deep Learning Framework Conversions in Image Recognition
Nikolaos Louloudakis, Perry Gibson, José Cano +1
When deploying Deep Neural Networks (DNNs), developers often convert models from one deep learning framework to another (e.g., TensorFlow to PyTorch). However, this process is erro…
Exploring Robustness of Image Recognition Models on Hardware Accelerators
Nikolaos Louloudakis, Perry Gibson, José Cano +1
As the usage of Artificial Intelligence (AI) on resource-intensive and safety-critical tasks increases, a variety of Machine Learning (ML) compilers have been developed, enabling c…
Can We Trust Explainable AI Methods on ASR? An Evaluation on Phoneme Recognition
Xiaoliang Wu, Peter Bell, Ajitha Rajan
Explainable AI (XAI) techniques have been widely used to help explain and understand the output of deep learning models in fields such as image classification and Natural Language…
Vaxformer: Antigenicity-controlled Transformer for Vaccine Design Against SARS-CoV-2
Aryo Pradipta Gema, Michał Kobiela, Achille Fraisse +3
The SARS-CoV-2 pandemic has emphasised the importance of developing a universal vaccine that can protect against current and future variants of the virus. The present study propose…
Knowledge Graph Embeddings in the Biomedical Domain: Are They Useful? A Look at Link Prediction, Rule Learning, and Downstream Polypharmacy Tasks
Aryo Pradipta Gema, Dominik Grabarczyk, Wolf De Wulf +5
Knowledge graphs are powerful tools for representing and organising complex biomedical data. Several knowledge graph embedding algorithms have been proposed to learn from and compl…
BenchDirect: A Directed Language Model for Compiler Benchmarks
Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu +4
The exponential increase of hardware-software complexity has made it impossible for compiler engineers to find the right optimization heuristics manually. Predictive models have be…
Explanations for Automatic Speech Recognition
Xiaoliang Wu, Peter Bell, Ajitha Rajan
We address quality assessment for neural network based ASR by providing explanations that help increase our understanding of the system and ultimately help build trust in the syste…