25 citations · 76 across the 14 of their papers we have counts for
15 papers
BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery
Peter St. John, Dejun Lin, Polina Binder +89
Artificial Intelligence models encoding biology and chemistry are opening new routes to high-throughput and high-quality in-silico drug development. However, their training increas…
TSPP: A Unified Benchmarking Tool for Time-series Forecasting
Jan Bączek, Dmytro Zhylko, Gilberto Titericz +7
While machine learning has witnessed significant advancements, the emphasis has largely been on data acquisition and model creation. However, achieving a comprehensive assessment o…
Heterogenous Ensemble of Models for Molecular Property Prediction
Sajad Darabi, Shayan Fazeli, Jiwei Liu +4
Previous works have demonstrated the importance of considering different modalities on molecules, each of which provide a varied granularity of information for downstream property…
A Framework for Large Scale Synthetic Graph Dataset Generation
Sajad Darabi, Piotr Bigaj, Dawid Majchrowski +3
Recently there has been increasing interest in developing and deploying deep graph learning algorithms for many tasks, such as fraud detection and recommender systems. Albeit, ther…
Contrastive Mixup: Self- and Semi-Supervised learning for Tabular Domain
Sajad Darabi, Shayan Fazeli, Ali Pazoki +2
Recent literature in self-supervised has demonstrated significant progress in closing the gap between supervised and unsupervised methods in the image and text domains. These metho…
Synthesising Multi-Modal Minority Samples for Tabular Data
Sajad Darabi, Yotam Elor
Real-world binary classification tasks are in many cases imbalanced, where the minority class is much smaller than the majority class. This skewness is challenging for machine lear…
Group-Connected Multilayer Perceptron Networks
Mohammad Kachuee, Sajad Darabi, Shayan Fazeli +1
Despite the success of deep learning in domains such as image, voice, and graphs, there has been little progress in deep representation learning for domains without a known structu…
Unsupervised Representation for EHR Signals and Codes as Patient Status Vector
Sajad Darabi, Mohammad Kachuee, Majid Sarrafzadeh
Effective modeling of electronic health records presents many challenges as they contain large amounts of irregularity most of which are due to the varying procedures and diagnosis…
TAPER: Time-Aware Patient EHR Representation
Sajad Darabi, Mohammad Kachuee, Shayan Fazeli +1
Effective representation learning of electronic health records is a challenging task and is becoming more important as the availability of such data is becoming pervasive. The data…
Generative Imputation and Stochastic Prediction
Mohammad Kachuee, Kimmo Karkkainen, Orpaz Goldstein +2
In many machine learning applications, we are faced with incomplete datasets. In the literature, missing data imputation techniques have been mostly concerned with filling missing…
Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams
Mohammad Kachuee, Orpaz Goldstein, Kimmo Karkkainen +2
In many real-world learning scenarios, features are only acquirable at a cost constrained under a budget. In this paper, we propose a novel approach for cost-sensitive feature acqu…
Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks
Mouloud Belbahri, Eyyüb Sari, Sajad Darabi +1
Deep neural networks (DNNs) have demonstrated success for many supervised learning tasks, ranging from voice recognition, object detection, to image classification. However, their…
Regularized Binary Network Training
Sajad Darabi, Mouloud Belbahri, Matthieu Courbariaux +1
There is a significant performance gap between Binary Neural Networks (BNNs) and floating point Deep Neural Networks (DNNs). We propose to improve the binary training method, by in…
Dynamic Feature Acquisition Using Denoising Autoencoders
Mohammad Kachuee, Sajad Darabi, Babak Moatamed +1
In real-world scenarios, different features have different acquisition costs at test-time which necessitates cost-aware methods to optimize the cost and performance trade-off. This…
Complex Event Processing of Health Data in Real-time to Predict Heart Failure Risk and Stress
Sandeep Singh Sandha, Mohammad Kachuee, Sajad Darabi
In this paper, we develop a scalable system which can do real-time analytics for different health applications. The occurrence of different health conditions can be regarded as the…