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Found 8 papers, 1 papers with code
Improving Molecular Properties Prediction Through Latent Space Fusion
Pre-trained Language Models have emerged as promising tools for predicting molecular properties, yet their development is in its early stages, necessitating further research to enhance their efficacy and address challenges such as generalization and sample efficiency.
Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction
Our approach opens up promising avenues for future research in molecular property prediction by harnessing the synergistic potential of both chemical language and physicochemical features, leading to enhanced performance and advancements in the field.
Position Paper on Dataset Engineering to Accelerate Science
Moreover, the dataset will be used in most cases as a blueprint of an entity that at any moment can be stored as a table.
An Interpretable Deep Semantic Segmentation Method for Earth Observation
Earth observation is fundamental for a range of human activities including flood response as it offers vital information to decision makers.
Towards Deep Machine Reasoning: a Prototype-based Deep Neural Network with Decision Tree Inference
In summary, we propose a new approach specifically advantageous for imbalanced multi-class problems that achieved two world records on well known hard benchmark problems and the best result on another problem in terms of accuracy.
Towards Explainable Deep Neural Networks (xDNN)
In this paper, we propose an elegant solution that is directly addressing the bottlenecks of the traditional deep learning approaches and offers a clearly explainable internal architecture that can outperform the existing methods, requires very little computational resources (no need for GPUs) and short training times (in the order of seconds).
Novelty Detection and Learning from Extremely Weak Supervision
In this paper we offer a method and algorithm, which make possible fully autonomous (unsupervised) detection of new classes, and learning following a very parsimonious training priming (few labeled data samples only).
Fair-by-design explainable models for prediction of recidivism
Recidivism prediction provides decision makers with an assessment of the likelihood that a criminal defendant will reoffend that can be used in pre-trial decision-making.
