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Found 15 papers, 9 papers with code
TSPP: A Unified Benchmarking Tool for Time-series Forecasting
While machine learning has witnessed significant advancements, the emphasis has largely been on data acquisition and model creation.
Heterogenous Ensemble of Models for Molecular Property Prediction
Previous works have demonstrated the importance of considering different modalities on molecules, each of which provide a varied granularity of information for downstream property prediction tasks.
A Framework for Large Scale Synthetic Graph Dataset Generation
Recently there has been increasing interest in developing and deploying deep graph learning algorithms for many tasks, such as fraud detection and recommender systems.
Contrastive Mixup: Self- and Semi-Supervised learning for Tabular Domain
Recent literature in self-supervised has demonstrated significant progress in closing the gap between supervised and unsupervised methods in the image and text domains.
Synthesising Multi-Modal Minority Samples for Tabular Data
Furthermore, the superior synthetic data yields better prediction quality in downstream binary classification tasks, as was demonstrated in extensive experiments with 27 publicly available real-world datasets
AE-SMOTE: A Multi-Modal Minority Oversampling Framework
Real-world binary classification tasks are in many cases unbalanced i. e. the minority class is much smaller than the majority class.
Group-Connected Multilayer Perceptron Networks
GMLP is based on the idea of learning expressive feature combinations (groups) and exploiting them to reduce the network complexity by defining local group-wise operations.
Unsupervised Representation for EHR Signals and Codes as Patient Status Vector
In this work, we present a two-step unsupervised representation learning scheme to summarize the multi-modal clinical time series consisting of signals and medical codes into a patient status vector.
TAPER: Time-Aware Patient EHR Representation
The data contained in these records are irregular and contain multiple modalities such as notes, and medical codes.
Generative Imputation and Stochastic Prediction
In order to make imputations, we train a simple and effective generator network to generate imputations that a discriminator network is tasked to distinguish.
BNN+: Improved Binary Network Training
Binary neural networks (BNN) help to alleviate the prohibitive resource requirements of DNN, where both activations and weights are limited to 1-bit.
Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks
Using a quasiconvex base function in order to construct a binary quantizer helps training binary neural networks (BNNs) and adding noise to the input data or using a concrete regularization function helps to improve generalization error.
Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams
The suggested method acquires features incrementally based on a context-aware feature-value function.
Regularized Binary Network Training
We propose to improve the binary training method, by introducing a new regularization function that encourages training weights around binary values.
Dynamic Feature Acquisition Using Denoising Autoencoders
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.
