no code implementations • 22 Dec 2023 • Nikolaos Louloudakis, Perry Gibson, José Cano, Ajitha Rajan
Converting deep learning models between frameworks is a common step to maximize model compatibility across devices and leverage optimization features that may be exclusively provided in one deep learning framework.
no code implementations • 4 Sep 2023 • Chao Peng, Zhengwei Lv, Jiarong Fu, Jiayuan Liang, Zhao Zhang, Ajitha Rajan, Ping Yang
We find that Hawkeye is able to generate GUI event sequences targeting changed functions more reliably than FastBot2 and ARES for the open source Apps and the large commercial App.
no code implementations • 10 Jun 2023 • Nikolaos Louloudakis, Perry Gibson, José Cano, Ajitha Rajan
To mitigate such errors, we present a novel approach towards fault localization and repair of buggy deep learning framework conversions, focusing on pre-trained image recognition models.
1 code implementation • 5 Jun 2023 • Nikolaos Louloudakis, Perry Gibson, José Cano, Ajitha Rajan
Owing to the increased use of image recognition tasks in safety-critical applications like autonomous driving and medical imaging, it is imperative to assess their robustness to changes in the computational environment, as the impact of parameters like deep learning frameworks, compiler optimizations, and hardware devices on model performance and correctness is not yet well understood.
1 code implementation • 2 Jun 2023 • Nikolaos Louloudakis, Perry Gibson, José Cano, Ajitha Rajan
On top of that, AI methods such as Deep Neural Networks (DNNs) are utilized to perform demanding, resource-intensive and even safety-critical tasks, and in order to effectively increase the performance of the DNN models deployed, a variety of Machine Learning (ML) compilers have been developed, allowing compatibility of DNNs with a variety of hardware acceleration devices, such as GPUs and TPUs.
1 code implementation • 31 May 2023 • Aryo Pradipta Gema, Dominik Grabarczyk, Wolf De Wulf, Piyush Borole, Javier Antonio Alfaro, Pasquale Minervini, Antonio Vergari, Ajitha Rajan
We achieve a three-fold improvement in terms of performance based on the HITS@10 score over previous work on the same biomedical knowledge graph.
no code implementations • 29 May 2023 • 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 Processing.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
1 code implementation • 18 May 2023 • Aryo Pradipta Gema, Michał Kobiela, Achille Fraisse, Ajitha Rajan, Diego A. Oyarzún, Javier Antonio Alfaro
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.
no code implementations • 2 Mar 2023 • Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu, Chris Cummins, Kim Hazelwood, Ajitha Rajan, Hugh Leather
We improve this with BenchDirect which utilizes a directed LM that infills programs by jointly observing source code context and the compiler features that are targeted.
no code implementations • 27 Feb 2023 • 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 system.
Automatic Speech Recognition Explainable Artificial Intelligence (XAI) +4
no code implementations • 1 Nov 2022 • Nikolaos Louloudakis, Perry Gibson, José Cano, Ajitha Rajan
On the other hand, model inference time was affected by all environment parameters with changes in hardware device having the most effect.
1 code implementation • 13 Aug 2022 • Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu, Chris Cummins, Kim Hazelwood, Ajitha Rajan, Hugh Leather
We develop BenchPress, the first ML benchmark generator for compilers that is steerable within feature space representations of source code.
no code implementations • 27 Jan 2022 • Xin Du, Benedicte Legastelois, Bhargavi Ganesh, Ajitha Rajan, Hana Chockler, Vaishak Belle, Stuart Anderson, Subramanian Ramamoorthy
Robustness evaluations like our checklist will be crucial in future safety evaluations of visual perception modules, and be useful for a wide range of stakeholders including designers, deployers, and regulators involved in the certification of these systems.
no code implementations • 3 Dec 2021 • Xiaoliang Wu, Ajitha Rajan
We evaluate portability and effectiveness of our techniques using three popular ASRs and two input audio datasets using the metrics - Word Error Rate (WER) of output transcription, Similarity to original audio, attack Success Rate on different ASRs and Detection score by a defense system.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
1 code implementation • 13 Nov 2021 • Hans-Christof Gasser, Georges Bedran, Bo Ren, David Goodlett, Javier Alfaro, Ajitha Rajan
In particular, we find that amino acids close to the peptides' N- and C-terminals are highly relevant.
no code implementations • 27 Aug 2020 • Bruce Collie, Philip Ginsbach, Jackson Woodruff, Ajitha Rajan, Michael O'Boyle
Our approach is integrated with standard compiler tooling, and we use this integration to evaluate migration opportunities in 9 existing C/C++ applications with over 1MLoC.
Software Engineering
no code implementations • 8 Jan 2020 • Foivos Tsimpourlas, Ajitha Rajan, Miltiadis Allamanis
We use the labelled traces to train a neural network (NN) model to learn to distinguish runtime patterns for passing versus failing executions for a given program.