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Found 9 papers, 1 papers with code
Few-shot fine-tuning SOTA summarization models for medical dialogues
Abstractive summarization of medical dialogues presents a challenge for standard training approaches, given the paucity of suitable datasets.
Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification
To investigate the effect of domain-specific pre-training, we considered the current state-of-the-art multiple-instance learning models, 1) CLAM, an attention-based model, and 2) TransMIL, a self-attention-based model, and evaluated the models' confidence and predictive performance in detecting primary brain tumors - gliomas.
DDoD: Dual Denial of Decision Attacks on Human-AI Teams
Artificial Intelligence (AI) systems have been increasingly used to make decision-making processes faster, more accurate, and more efficient.
Automatic Speech Summarisation: A Scoping Review
We also discuss the strengths and weaknesses of these different methods and speech features.
Jointly Modeling Intra- and Inter-transaction Dependencies with Hierarchical Attentive Transaction Embeddings for Next-item Recommendation
Most existing TBRSs recommend next item by only modeling the intra-transaction dependency within the current transaction while ignoring inter-transaction dependency with recent transactions that may also affect the next item.
Empirical Analysis of Zipf's Law, Power Law, and Lognormal Distributions in Medical Discharge Reports
Bayesian modelling and statistical text analysis rely on informed probability priors to encourage good solutions.
Graph Based Recommendations: From Data Representation to Feature Extraction and Application
The proposed approach is domain-independent (demonstrated on data from movies, music, and business recommender systems), and is evaluated using several state-of-the-art machine learning methods, on different recommendation tasks, and using different evaluation metrics.
Does Weather Matter? Causal Analysis of TV Logs
To the best of our knowledge, this is the first large-scale causal study of the impact of weather on TV watching patterns.
DUM: Diversity-Weighted Utility Maximization for Recommendations
The need for diversification of recommendation lists manifests in a number of recommender systems use cases.
