Search Results for author: Subhrajit Roy

Found 17 papers, 4 papers with code

Boosting the interpretability of clinical risk scores with intervention predictions

no code implementations6 Jul 2022 Eric Loreaux, Ke Yu, Jonas Kemp, Martin Seneviratne, Christina Chen, Subhrajit Roy, Ivan Protsyuk, Natalie Harris, Alexander D'Amour, Steve Yadlowsky, Ming-Jun Chen

We propose a joint model of intervention policy and adverse event risk as a means to explicitly communicate the model's assumptions about future interventions.

Disability prediction in multiple sclerosis using performance outcome measures and demographic data

no code implementations8 Apr 2022 Subhrajit Roy, Diana Mincu, Lev Proleev, Negar Rostamzadeh, Chintan Ghate, Natalie Harris, Christina Chen, Jessica Schrouff, Nenad Tomasev, Fletcher Lee Hartsell, Katherine Heller

To the best of our knowledge, our results are the first to show that it is possible to predict disease progression using POMs and demographic data in the context of both clinical trials and smartphone-base studies by using two datasets.

BIG-bench Machine Learning

Healthsheet: Development of a Transparency Artifact for Health Datasets

no code implementations26 Feb 2022 Negar Rostamzadeh, Diana Mincu, Subhrajit Roy, Andrew Smart, Lauren Wilcox, Mahima Pushkarna, Jessica Schrouff, Razvan Amironesei, Nyalleng Moorosi, Katherine Heller

Our findings from the interviewee study and case studies show 1) that datasheets should be contextualized for healthcare, 2) that despite incentives to adopt accountability practices such as datasheets, there is a lack of consistency in the broader use of these practices 3) how the ML for health community views datasheets and particularly \textit{Healthsheets} as diagnostic tool to surface the limitations and strength of datasets and 4) the relative importance of different fields in the datasheet to healthcare concerns.

ML4H Abstract Track 2020

no code implementations19 Nov 2020 Emily Alsentzer, Matthew B. A. McDermott, Fabian Falck, Suproteem K. Sarkar, Subhrajit Roy, Stephanie L. Hyland

A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2020.

BIG-bench Machine Learning

Type-Driven Automated Learning with Lale

2 code implementations24 May 2019 Martin Hirzel, Kiran Kate, Avraham Shinnar, Subhrajit Roy, Parikshit Ram

Machine-learning automation tools, ranging from humble grid-search to hyperopt, auto-sklearn, and TPOT, help explore large search spaces of possible pipelines.

Time Series

Machine Learning for removing EEG artifacts: Setting the benchmark

no code implementations19 Mar 2019 Subhrajit Roy

Electroencephalograms (EEG) are often contaminated by artifacts which make interpreting them more challenging for clinicians.

BIG-bench Machine Learning EEG

A semi-supervised deep learning algorithm for abnormal EEG identification

no code implementations19 Mar 2019 Subhrajit Roy, Kiran Kate, Martin Hirzel

Systems that can automatically analyze EEG signals can aid neurologists by reducing heavy workload and delays.

EEG

SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification

3 code implementations8 Mar 2019 Umar Asif, Subhrajit Roy, Jianbin Tang, Stefan Harrer

Automatic classification of epileptic seizure types in electroencephalograms (EEGs) data can enable more precise diagnosis and efficient management of the disease.

Classification EEG +4

Seizure Type Classification using EEG signals and Machine Learning: Setting a benchmark

1 code implementation4 Feb 2019 Subhrajit Roy, Umar Asif, Jianbin Tang, Stefan Harrer

On that note, in this paper, we explore the application of machine learning algorithms for multi-class seizure type classification.

BIG-bench Machine Learning Classification +4

ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification

2 code implementations30 Jan 2018 Subhrajit Roy, Isabell Kiral-Kornek, Stefan Harrer

Brain-related disorders such as epilepsy can be diagnosed by analyzing electroencephalograms (EEG).

EEG Image Classification +1

An Online Structural Plasticity Rule for Generating Better Reservoirs

no code implementations19 Apr 2016 Subhrajit Roy, Arindam Basu

The proposed learning rule is inspired from structural plasticity and trains the liquid through formation and elimination of synaptic connections.

An Online Unsupervised Structural Plasticity Algorithm for Spiking Neural Networks

no code implementations4 Dec 2015 Subhrajit Roy, Arindam Basu

To demonstrate the performance of the proposed network and learning rule, we employ it to solve two, four and six class classification of random Poisson spike time inputs.

General Classification Specificity

Learning Spike time codes through Morphological Learning with Binary Synapses

no code implementations17 Jun 2015 Subhrajit Roy, Phyo Phyo San, Shaista Hussain, Lee Wang Wei, Arindam Basu

In this paper, a neuron with nonlinear dendrites (NNLD) and binary synapses that is able to learn temporal features of spike input patterns is considered.

Liquid State Machine with Dendritically Enhanced Readout for Low-power, Neuromorphic VLSI Implementations

no code implementations20 Nov 2014 Subhrajit Roy, Amitava Banerjee, Arindam Basu

Compared to the parallel perceptron architecture trained by the p-delta algorithm, which is the state of the art in terms of performance of readout stages, our readout architecture and learning algorithm can attain better performance with significantly less synaptic resources making it attractive for VLSI implementation.

Cannot find the paper you are looking for? You can Submit a new open access paper.