• Challenges of deep learning in medical image analysis🔍
• Project repository of the paper "Less Annotating🔍
• Neural Networks for Deep Learning🔍
• Making Natural Language Processing work for Little Training Data🔍
• Utilising physics|guided deep learning to overcome data scarcity🔍
• Protecting Our Future Food Supply with AI and Geospatial Analytics🔍
• Model and Data|centric Machine Learning Algorithms to Address ...🔍
• Data Labeling Challenges and Solutions🔍

Challenges of deep learning in medical image analysis - CentAUR

While these are some recent encouraging findings made by deep learning researchers to tackle the challenge of data scarcity and variance, ...

Project repository of the paper "Less Annotating, More Classifying ...

This is the replication code for the paper "Less Annotating, More Classifying: Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep ...

Neural Networks for Deep Learning: More Complex Stuff from Data

Picture a massive web of interconnected nodes. Each node takes in input, does some math operation on it, and then spits out the result to ...

Making Natural Language Processing work for Little Training Data

The Data Scarcity Problem. In recent years, deep learning has been developing steadily, showing great performance in solving machine learning tasks. · Distant ...

Utilising physics-guided deep learning to overcome data scarcity

Abstract. Deep learning (DL) relies heavily on data, and the quality of data influences its performance significantly. However, obtaining high-quality, ...

Protecting Our Future Food Supply with AI and Geospatial Analytics

Researchers at NCSU, in partnership with Lenovo, are tackling food scarcity issue using deep-learning algorithms and geospatial analytics.

Model and Data-centric Machine Learning Algorithms to Address ...

The uneven occurrence of certain types of failures in optical networks results in a scarcity of data for less frequent failures, leading to imbalanced ...

Data Labeling Challenges and Solutions - DATAVERSITY

Accurate data labeling and annotation are crucial for reliable machine learning systems, but applying complex ontologies is time-consuming.

What is Data Scarcity? - CastorDoc

Emerging technologies such as IoT (Internet of Things) devices, machine learning ... Open data initiatives also play a crucial role in tackling ...

Machine Learning @Carrefour: tackling promotional product shortage

Discover why data-driven forecasting can be complex and how Carrefour manages to address this issue to avoid product shortage.

Deep neural networks for food waste analysis and classification

Machine learning generally requires large amounts of data, however data ... however, the problems tackled, data shortage and change detection, ...

Tackling Data Scarcity with Transfer Learning: A Case Study of ...

Transfer learning (TL) increasingly becomes an important tool in handling data scarcity, especially when applying machine learning (ML) to novel ...

Leveraging safe synthetic data to overcome scarcity in AI & LLM ...

The scarcity of data is a critical bottleneck in building effective machine learning models. Models trained on limited data often suffer from ...

Data scarcity - Vocab, Definition, and Must Know Facts | Fiveable

Machine learning models rely heavily on large and diverse datasets to achieve high accuracy, so data scarcity directly impacts their effectiveness in optimizing ...

Mitigating Data Scarcity in Polymer Property Prediction via Multi-task ...

... machine learning are still open questions. To tackle these challenges, we have compiled a large dataset of polymers labeled with various ...

Using machine learning and uncertainty quantification to tackle data ...

Data scarcity makes it challenging to develop robust simulations, and – despite the rapidly growing application of remote sensing – certain ...

a case study of thickness characterization from optical spectra of ...

Transfer learning (TL) increasingly becomes an important tool in handling data scarcity, especially when applying machine learning (ML) to novel ...

Issues and Recent Advances in Machine Learning Techniques for ...

An imbalanced dataset also indirectly leads to data scarcity. In the case of DARPA and KDD'99, the imbalance of normal to abnormal data also ...

Generative, Predictive, and Reactive Models for Data Scarce ...

Transfer learning is thus a favorable strategy for facilitating high throughput characterization of low-data design spaces. Generative chemical models invert ...

Tackling Data Scarcity Challenge through Active Learning in ...

[13]. To address the issue of data scarcity in experiments, a meth- odological approach emerged as a potential solution. Traditional experiment ...