Search Results for author:
Found 8 papers, 2 papers with code
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.
Topical: Learning Repository Embeddings from Source Code using Attention
This paper presents Topical, a novel deep neural network for repository level embeddings.
CV4Code: Sourcecode Understanding via Visual Code Representations
We present CV4Code, a compact and effective computer vision method for sourcecode understanding.
Enhancing Privacy against Inversion Attacks in Federated Learning by using Mixing Gradients Strategies
We show that otherwise it is possible to directly recover all vectors in a mini-batch without any numerical optimisation due to the de-mixing nature of the cross entropy loss.
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.
