Zobrazeno 1 - 10
of 301
pro vyhledávání: '"LAWRENCE, NEIL D."'
Using Large Language Models (LLMs) to address critical societal problems requires adopting this novel technology into socio-technical systems. However, the complexity of such systems and the nature of LLMs challenge such a vision. It is unlikely that
Externí odkaz:
http://arxiv.org/abs/2411.09050
This paper, a technical summary of our preceding publication, introduces a robust machine learning framework for the detection of vocal activities of Coppery titi monkeys. Utilizing a combination of MFCC features and a bidirectional LSTM-based classi
Externí odkaz:
http://arxiv.org/abs/2407.01452
Autor:
Ravuri, Aditya, Lawrence, Neil D.
This paper shows that dimensionality reduction methods such as UMAP and t-SNE, can be approximately recast as MAP inference methods corresponding to a model introduced in ProbDR, that describes the graph Laplacian (an estimate for the precision/inver
Externí odkaz:
http://arxiv.org/abs/2405.17412
What if end users could own the software development lifecycle from conception to deployment using only requirements expressed in language, images, video or audio? We explore this idea, building on the capabilities that generative Artificial Intellig
Externí odkaz:
http://arxiv.org/abs/2405.13708
Dimensionality reduction is crucial for analyzing large-scale single-cell RNA-seq data. Gaussian Process Latent Variable Models (GPLVMs) offer an interpretable dimensionality reduction method, but current scalable models lack effectiveness in cluster
Externí odkaz:
http://arxiv.org/abs/2405.03879
Software systems impact society at different levels as they pervasively solve real-world problems. Modern software systems are often so sophisticated that their complexity exceeds the limits of human comprehension. These systems must respond to chang
Externí odkaz:
http://arxiv.org/abs/2401.11370
Autor:
Paleyes, Andrei, Lawrence, Neil D.
Dataflow computing was shown to bring significant benefits to multiple niches of systems engineering and has the potential to become a general-purpose paradigm of choice for data-driven application development. One of the characteristic features of d
Externí odkaz:
http://arxiv.org/abs/2304.11987
Dimensionality reduction (DR) algorithms compress high-dimensional data into a lower dimensional representation while preserving important features of the data. DR is a critical step in many analysis pipelines as it enables visualisation, noise reduc
Externí odkaz:
http://arxiv.org/abs/2304.07658
Component-based development is one of the core principles behind modern software engineering practices. Understanding of causal relationships between components of a software system can yield significant benefits to developers. Yet modern software de
Externí odkaz:
http://arxiv.org/abs/2303.09552
This report documents the programme and the outcomes of Dagstuhl Seminar 22382 "Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling". Today's scientific challenges are characterised by complexity. Interconnected natural, tech
Externí odkaz:
http://arxiv.org/abs/2303.04217