Publications (25)
Seizure Type Classification using EEG signals and Machine Learning: Setting a benchmark
Subhrajit Roy, Umar Asif, Jianbin Tang +1
Accurate classification of seizure types plays a crucial role in the treatment and disease management of epileptic patients. Epileptic seizure types not only impact the choice of d…
Boosting the interpretability of clinical risk scores with intervention predictions
Eric Loreaux, Ke Yu, Jonas Kemp +8
Machine learning systems show significant promise for forecasting patient adverse events via risk scores. However, these risk scores implicitly encode assumptions about future inte…
Maximizing Savonius Turbine Performance using Kriging Surrogate Model and Grey Wolf-Driven Cylindrical Deflector Optimization
Paras Singh, Vishal Jaiswal, Subhrajit Roy +1
With the growing demand for power and the pressing need to shift towards renewable energy sources, wind power stands as a vital component of the energy transition. To optimize ener…
STUDY: Socially Aware Temporally Causal Decoder Recommender Systems
Eltayeb Ahmed, Diana Mincu, Lauren Harrell +2
Recommender systems are widely used to help people find items that are tailored to their interests. These interests are often influenced by social networks, making it important to…
Learning Spike time codes through Morphological Learning with Binary Synapses
Subhrajit Roy, Phyo Phyo San, Shaista Hussain +2
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. Since binary synapses are…
Healthsheet: Development of a Transparency Artifact for Health Datasets
Negar Rostamzadeh, Diana Mincu, Subhrajit Roy +7
Machine learning (ML) approaches have demonstrated promising results in a wide range of healthcare applications. Data plays a crucial role in developing ML-based healthcare systems…
Safety Alignment Should Be Made More Than Just a Few Tokens Deep
Xiangyu Qi, Ashwinee Panda, Kaifeng Lyu +5
The safety alignment of current Large Language Models (LLMs) is vulnerable. Relatively simple attacks, or even benign fine-tuning, can jailbreak aligned models. We argue that many…
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021
Fabian Falck, Yuyin Zhou, Emma Rocheteau +7
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021. This index is not complete, as some accepted abstracts chose to opt-out of inclusi…
Disability prediction in multiple sclerosis using performance outcome measures and demographic data
Subhrajit Roy, Diana Mincu, Lev Proleev +8
Literature on machine learning for multiple sclerosis has primarily focused on the use of neuroimaging data such as magnetic resonance imaging and clinical laboratory tests for dis…
Benchmarking Continuous Time Models for Predicting Multiple Sclerosis Progression
Alexander Norcliffe, Lev Proleev, Diana Mincu +3
Multiple sclerosis is a disease that affects the brain and spinal cord, it can lead to severe disability and has no known cure. The majority of prior work in machine learning for m…
Fast Efficient Object Detection Using Selective Attention
Shivanthan Yohanandan, Andy Song, Adrian G. Dyer +3
Retraction due to significant oversight
An Online Structural Plasticity Rule for Generating Better Reservoirs
Subhrajit Roy, Arindam Basu
In this article, a novel neuro-inspired low-resolution online unsupervised learning rule is proposed to train the reservoir or liquid of Liquid State Machine. The liquid is a spars…
ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification
Subhrajit Roy, Isabell Kiral-Kornek, Stefan Harrer
Brain-related disorders such as epilepsy can be diagnosed by analyzing electroencephalograms (EEG). However, manual analysis of EEG data requires highly trained clinicians, and is…
Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering
Han Zhou, Xingchen Wan, Lev Proleev +4
Prompting and in-context learning (ICL) have become efficient learning paradigms for large language models (LLMs). However, LLMs suffer from prompt brittleness and various bias fac…
Diagnosing failures of fairness transfer across distribution shift in real-world medical settings
Jessica Schrouff, Natalie Harris, Oluwasanmi Koyejo +14
Diagnosing and mitigating changes in model fairness under distribution shift is an important component of the safe deployment of machine learning in healthcare settings. Importantl…
Type-Driven Automated Learning with Lale
Martin Hirzel, Kiran Kate, Avraham Shinnar +2
Machine-learning automation tools, ranging from humble grid-search to hyperopt, auto-sklearn, and TPOT, help explore large search spaces of possible pipelines. Unfortunately, each…
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431
In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…
Gemini: A Family of Highly Capable Multimodal Models
Gemini Team, Rohan Anil, Sebastian Borgeaud +1340
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consist…
A semi-supervised deep learning algorithm for abnormal EEG identification
Subhrajit Roy, Kiran Kate, Martin Hirzel
Systems that can automatically analyze EEG signals can aid neurologists by reducing heavy workload and delays. However, such systems need to be first trained using a labeled datase…
Liquid State Machine with Dendritically Enhanced Readout for Low-power, Neuromorphic VLSI Implementations
Subhrajit Roy, Amitava Banerjee, Arindam Basu
In this paper, we describe a new neuro-inspired, hardware-friendly readout stage for the liquid state machine (LSM), a popular model for reservoir computing. Compared to the parall…
Machine Learning for removing EEG artifacts: Setting the benchmark
Subhrajit Roy
Electroencephalograms (EEG) are often contaminated by artifacts which make interpreting them more challenging for clinicians. Hence, automated artifact recognition systems have the…
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification
Umar Asif, Subhrajit Roy, Jianbin Tang +1
Automatic classification of epileptic seizure types in electroencephalograms (EEGs) data can enable more precise diagnosis and efficient management of the disease. This task is cha…
Quantum-Based Salp Swarm Algorithm Driven Design Optimization of Savonius Wind Turbine-Cylindrical Deflector System
Paras Singh, Vishal Jaiswal, Subhrajit Roy +3
Savonius turbines, prominent in small-scale wind turbine applications operating under low-speed conditions, encounter limitations due to opposing torque on the returning blade, imp…
An Online Unsupervised Structural Plasticity Algorithm for Spiking Neural Networks
Subhrajit Roy, Arindam Basu
In this article, we propose a novel Winner-Take-All (WTA) architecture employing neurons with nonlinear dendrites and an online unsupervised structural plasticity rule for training…
ML4H Abstract Track 2020
Emily Alsentzer, Matthew B. A. McDermott, Fabian Falck +3
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2020. This index is not complete, as some accepted abstracts chose to opt-out…