The library. Every paper below is real and readable in minutes — abstract, plain words, honest results (including the negative ones), and what each one taught me.
Part-of-speech tagging for Bangla — a low-resource language whose main benchmark, Microsoft IL-POST, is severely class-imbalanced — using contextual embeddings from three Bangla BERT models. A decision tree proves less biased by the imbalance than a neural network, motivating an unusual distillation direction: treat the class counts in the tree's leaf nodes as a probability distribution and distill that "dark knowledge" from the tree into the neural student .
A comprehensive review of the state of the art in quantum machine learning, paired with hands-on classification experiments: two first-generation hybrid quantum-classical models — a quanvolutional neural network on a gate-based simulator and a continuous-variable quantum neural network on a photonic simulator — trained on MNIST and compared head-to-head against classical baselines on accuracy and convergence.
Fine-tuning a pretrained RetinaNet (ResNet backbone + feature pyramid network, focal loss) on the BCCD microscopy dataset to detect red blood cells, white blood cells, and platelets — 364 images, 4,888 annotations, three classes — reaching mAP 0.876 at IoU 0.5 and 55.25% at IoU 0.50:0.95 on the test split.
Signature-based intrusion detection can't see attacks it has no signature for. This work trains four classical supervised models on the CICIDS2017 network-traffic benchmark, selects the strongest (a decision tree) as a teacher, and distills its knowledge into a neural student intended to flag anomalous — potentially novel — traffic patterns without predefined signatures.