Search Results for author:
Found 18 papers, 6 papers with code
A Generative AI Assistant to Accelerate Cloud Migration
We present a tool that leverages generative AI to accelerate the migration of on-premises applications to the cloud.
Using AI/ML to Find and Remediate Enterprise Secrets in Code & Document Sharing Platforms
We introduce a new challenge to the software development community: 1) leveraging AI to accurately detect and flag up secrets in code and on popular document sharing platforms that frequently used by developers, such as Confluence and 2) automatically remediating the detections (e. g. by suggesting password vault functionality).
DeepClean: Machine Unlearning on the Cheap by Resetting Privacy Sensitive Weights using the Fisher Diagonal
By leveraging the FIM diagonal, our approach provides an interpretable, lightweight, and efficient solution for machine unlearning with practical privacy benefits.
Estimating Class Separability of Datasets Using Persistent Homology with Application to LLM Fine-Tuning
This paper proposes a method to estimate the class separability of an unlabeled text dataset by inspecting the topological characteristics of sentence-transformer embeddings of the text.
Spam-T5: Benchmarking Large Language Models for Few-Shot Email Spam Detection
Our results demonstrate that Spam-T5 surpasses baseline models and other LLMs in the majority of scenarios, particularly when there are a limited number of training samples available.
A Benchmark Generative Probabilistic Model for Weak Supervised Learning
Finding relevant and high-quality datasets to train machine learning models is a major bottleneck for practitioners.
Learning a Consensus Sub-Network with Polarization Regularization and One Pass Training
Our method enables pruning and training simultaneously, which saves energy in both the training and inference phases and avoids extra computational overhead from gating modules at inference time.
API-Miner: an API-to-API Specification Recommendation Engine
Overall, API-Miner will allow developers to retrieve relevant OpenAPI specification components from a public or internal database in the early stages of the API development cycle, so that they can learn from existing established examples and potentially identify redundancies in their work.
Code Librarian: A Software Package Recommendation System
The use of packaged libraries can significantly shorten the software development cycle by improving the quality and readability of code.
Topical: Learning Repository Embeddings from Source Code using Attention
This paper presents Topical, a novel deep neural network for repository level embeddings.
ST-FL: Style Transfer Preprocessing in Federated Learning for COVID-19 Segmentation
We demonstrate that the widely varying data quality on FL client nodes leads to a sub-optimal centralised FL model for COVID-19 chest CT image segmentation.
Senatus -- A Fast and Accurate Code-to-Code Recommendation Engine
Senatus also outperforms standard MinHash LSH by 29. 2\% F1 and 51. 02\emph{x} faster query time.
Low Light Video Enhancement using Synthetic Data Produced with an Intermediate Domain Mapping
Advances in low-light video RAW-to-RGB translation are opening up the possibility of fast low-light imaging on commodity devices (e. g. smartphone cameras) without the need for a tripod.
Image and Video Processing
DeepLPF: Deep Local Parametric Filters for Image Enhancement
We introduce a deep neural network, dubbed Deep Local Parametric Filters (DeepLPF), which regresses the parameters of these spatially localized filters that are then automatically applied to enhance the image.
Ranked #8 on
Image Enhancement
on MIT-Adobe 5k
(SSIM on proRGB metric)
CURL: Neural Curve Layers for Global Image Enhancement
We present a novel approach to adjust global image properties such as colour, saturation, and luminance using human-interpretable image enhancement curves, inspired by the Photoshop curves tool.
Ranked #4 on
Photo Retouching
on MIT-Adobe 5k
NODE: Extreme Low Light Raw Image Denoising using a Noise Decomposition Network
In this paper, we propose a multi-task deep neural network called Noise Decomposition (NODE) that explicitly and separately estimates defective pixel noise, in conjunction with Gaussian and Poisson noise, to denoise an extreme low light image.
