35 research outputs found
A Primer on the Signature Method in Machine Learning
In these notes, we wish to provide an introduction to the signature method,
focusing on its basic theoretical properties and recent numerical applications.
The notes are split into two parts. The first part focuses on the definition
and fundamental properties of the signature of a path, or the path signature.
We have aimed for a minimalistic approach, assuming only familiarity with
classical real analysis and integration theory, and supplementing theory with
straightforward examples. We have chosen to focus in detail on the principle
properties of the signature which we believe are fundamental to understanding
its role in applications. We also present an informal discussion on some of its
deeper properties and briefly mention the role of the signature in rough paths
theory, which we hope could serve as a light introduction to rough paths for
the interested reader.
The second part of these notes discusses practical applications of the path
signature to the area of machine learning. The signature approach represents a
non-parametric way for extraction of characteristic features from data. The
data are converted into a multi-dimensional path by means of various embedding
algorithms and then processed for computation of individual terms of the
signature which summarise certain information contained in the data. The
signature thus transforms raw data into a set of features which are used in
machine learning tasks. We will review current progress in applications of
signatures to machine learning problems.Comment: 45 pages, 25 figure
Named Entity Recognition in Electronic Health Records Using Transfer Learning Bootstrapped Neural Networks
Neural networks (NNs) have become the state of the art in many machine
learning applications, especially in image and sound processing [1]. The same,
although to a lesser extent [2,3], could be said in natural language processing
(NLP) tasks, such as named entity recognition. However, the success of NNs
remains dependent on the availability of large labelled datasets, which is a
significant hurdle in many important applications. One such case are electronic
health records (EHRs), which are arguably the largest source of medical data,
most of which lies hidden in natural text [4,5]. Data access is difficult due
to data privacy concerns, and therefore annotated datasets are scarce. With
scarce data, NNs will likely not be able to extract this hidden information
with practical accuracy. In our study, we develop an approach that solves these
problems for named entity recognition, obtaining 94.6 F1 score in I2B2 2009
Medical Extraction Challenge [6], 4.3 above the architecture that won the
competition. Beyond the official I2B2 challenge, we further achieve 82.4 F1 on
extracting relationships between medical terms. To reach this state-of-the-art
accuracy, our approach applies transfer learning to leverage on datasets
annotated for other I2B2 tasks, and designs and trains embeddings that
specially benefit from such transfer.Comment: 11 pages, 4 figures, 8 table
Analytic structure of the scattering amplitude in SYM theory at multi-Regge kinematics: Conformal Regge pole contribution
We investigate the analytic structure of the scattering amplitude in
the planar limit of SYM in multi-Regge kinematics in all
physical regions. We demonstrate the close connection between Regge pole and
Regge cut contributions: in a selected class of kinematic regions (Mandelstam
regions) the usual factorizing Regge pole formula develops unphysical
singularities which have to be absorbed and compensated by Regge cut
contributions. This leads, in the corrections to the BDS formula, to conformal
invariant 'renormalized' Regge pole expressions in the remainder function. We
compute these renormalized Regge poles for the scattering amplitude.Comment: 46 pages, references added, typos corrected, journal versio
Analytic structure of the scattering amplitude in SYM theory in multi-Regge kinematics: Conformal Regge cut contribution
In this second part of our investigation of the analytic structure of the
scattering amplitude in the planar limit of SYM in
multi-Regge kinematics we compute, in all kinematic regions, the Regge cut
contributions in leading order. The results are infrared finite and conformally
invariant.Comment: 44 pages, 14 figures, 2 table
Detecting early signs of depressive and manic episodes in patients with bipolar disorder using the signature-based model
Recurrent major mood episodes and subsyndromal mood instability cause
substantial disability in patients with bipolar disorder. Early identification
of mood episodes enabling timely mood stabilisation is an important clinical
goal. Recent technological advances allow the prospective reporting of mood in
real time enabling more accurate, efficient data capture. The complex nature of
these data streams in combination with challenge of deriving meaning from
missing data mean pose a significant analytic challenge. The signature method
is derived from stochastic analysis and has the ability to capture important
properties of complex ordered time series data. To explore whether the onset of
episodes of mania and depression can be identified using self-reported mood
data.Comment: 12 pages, 3 tables, 10 figure
Clinical prompt learning with frozen language models
When the first transformer-based language models were published in the late 2010s, pretraining with general text and then fine-tuning the model on a task-specific dataset often achieved the state-of-the-art performance. However, more recent work suggests that for some tasks, directly prompting the pretrained model matches or surpasses fine-tuning in performance with few or no model parameter updates required. The use of prompts with language models for natural language processing (NLP) tasks is known as prompt learning. We investigated the viability of prompt learning on clinically meaningful decision tasks and directly compared this with more traditional fine-tuning methods. Results show that prompt learning methods were able to match or surpass the performance of traditional fine-tuning with up to 1000 times fewer trainable parameters, less training time, less training data, and lower computation resource requirements. We argue that these characteristics make prompt learning a very desirable alternative to traditional fine-tuning for clinical tasks, where the computational resources of public health providers are limited, and where data can often not be made available or not be used for fine-tuning due to patient privacy concerns. The complementary code to reproduce the experiments presented in this work can be found at https://github.com/NtaylorOX/Public_Clinical_Prompt