In a world where human time is expensive, learn how to use it only when really necessary The post Reducing Human Annotation with ML Active Learning appeared first on Towards Data Science .
arXiv:2606. 08718v1 Announce Type: cross Abstract: While Deep Active Learning (DAL) effectively reduces human annotation costs, its efficacy is constrained by human annotation errors.
arXiv:2403. 07008v3 Announce Type: replace-cross Abstract: The evaluation of machine learning models using human-labeled validation data can be expensive and time-consuming.
arXiv:2606. 07630v1 Announce Type: cross Abstract: Real-world datasets across image and text domains are often characterized by skewed class distributions and noisy annotations, which jointly degrade model performance, particularly on minority classes.
The downside of conference travel The post Last Month’s Machine Learning Lessons Learned appeared first on Towards Data Science .
Because the alternative is much too dangerous The post We Should Train AI to Betray Its Users appeared first on Towards Data Science .
How to smash through data / ML behavioural interviews The post How to Ace Data and ML Behavioural Interviews appeared first on Towards Data Science .
A primer about Semi-Supervised Learning, the approaches taken with different algorithms and the limitations of using unlabelled data. The post Introduction to Semi-Supervised Learning appeared first on Towards Data Science .
arXiv:2604. 13899v3 Announce Type: replace-cross Abstract: Instruction-tuned LLMs can annotate thousands of instances at low cost.
Enterprise Document Intelligence [Vol. 1 #3] - Why the ML toolkit (hyperparameter sweeps, train/test splits, explainability frameworks) solves the wrong problem, and what to use instead The post RAG Is Not Machine Learning, and the ML Toolkit Solves the Wrong Problem appeared first on Towards Data Science .
On the value of building on existing foundations The post Using Classical ML to Empower AI Agents appeared first on Towards Data Science .
arXiv:2604.13899v5 Announce Type: replace-cross Abstract: Annotating data remains a costly bottleneck for supervised NLP. Active learning (AL) reduces the number of human labels needed by selecting o...
arXiv:2512.12870v2 Announce Type: replace-cross Abstract: Active Learning (AL) is commonly used in applications where labeling data is expensive or time-consuming. In practice, however, labels are of...