Tag: transformers

A joint deep learning framework called J-SUP unifies aspect-based sentiment analysis and consumer preference profiling within a shared Transformer architecture ...

A narrative review traces how deep-learning architectures for surgical video evolved from recurrent networks to transformers while confronting latency, fusion ...

Researchers review the methodological evolution of policy agenda-setting studies from manual content analysis to transformer models and generative artificial intelligence.

A new inference-time framework reuses highly similar hidden token states in transformers, preserving sequence length without retraining while largely maintaining ...

A new multimodal AI system called HMTF-Net combines text, conversation context, user interaction graphs, images and metadata to detect cyberbullying, ...

A systematic experimental study in Artificial Intelligence Review finds detector improvements deliver more than 10 percent tracking gains while refined ...

A three-stage CNN and transformer pipeline identified satellite image types and detected oil with high reported accuracy, while a lightweight ...

Simulated distribution-grid case studies found that a serverless, committee-based federated-learning framework combined near-centralized false-data-injection detection with resistance to tested malicious ...

A single-university study found that jointly predicting semester course sets and grades reduced a Transformer’s mean absolute grade-prediction error by ...

A dual-stream AI recommender balances recent intentions with long-term preferences through a personalized gate that detects changing tastes.