Search Results for author:
Found 9 papers, 4 papers with code
Quantifying Manifolds: Do the manifolds learned by Generative Adversarial Networks converge to the real data manifold
This paper presents our experiments to quantify the manifolds learned by ML models (in our experiment, we use a GAN model) as they train.
Detecting out-of-distribution text using topological features of transformer-based language models
We attempt to detect out-of-distribution (OOD) text samples though applying Topological Data Analysis (TDA) to attention maps in transformer-based language models.
Garbage in, garbage out: Zero-shot detection of crime using Large Language Models
This paper proposes exploiting the common sense knowledge learned by large language models to perform zero-shot reasoning about crimes given textual descriptions of surveillance videos.
Green Runner: A tool for efficient model selection from model repositories
Deep learning models have become essential in software engineering, enabling intelligent features like image captioning and document generation.
Comparative analysis of real bugs in open-source Machine Learning projects -- A Registered Report
Objective: Our objective is to investigate whether there is a discrepancy in the distribution of resolution time between ML and non-ML issues and whether certain categories of ML issues require a longer time to resolve based on real issue reports in open-source applied ML projects.
SignalKG: Towards Reasoning about the Underlying Causes of Sensor Observations
This paper demonstrates our vision for knowledge graphs that assist machines to reason about the cause of signals observed by sensors.
Signal Knowledge Graph
This paper presents an knowledge graph to assist in reasoning over signals for intelligence purposes.
MLSmellHound: A Context-Aware Code Analysis Tool
In our approach we attempt to resolve this problem by exploring the use of context which includes i) purpose of the source code, ii) technical domain, iii) problem domain, iv) team norms, v) operational environment, and vi) development lifecycle stage to provide contextualised error reporting for code analysis.
A reliability measure for smart surveillance systems
We present a reliability measure for smart surveillance systems, taking into account the adversarial nature of intrusion.
