bothcharacterizationanddiscriminationarebasedondatageneralizationandsummarization Datageneralization aprocesswhichabstractsalargesetoftaskrelevantdatainadatabasefromarelativelylowconceptualleveltohigherconceptua
Generative AI, quantum ML, neuro-symbolic methods and reasoning, causal reasoning, non-IID learning, OOD generalization, representation learning, optimization methods Model explainability, XAI Learning Methods and Algorithms Clustering, classification, pattern mining and association rules discovery ...
The summarization system takes a Database (DB) table as input and produces a reduced version of this table through both a rewriting and a generalization process. The resulting table provides records with less precision than the original but it is very informative of the actual DB content. This...
generalization, representation learning, mathematical and statistical foundations, information theoretic approaches, optimization method Theoretical foundations for fairness, safety, model explainability, and XAI Learning Methods and Algorithms Clustering, classification, pattern mining and association rules discovery ...
Generalization:replacing specific values with broader ones Suppression:removing certain values altogether when generalization is not enough Data Masking Data masking is a popular technique for protecting confidential information by replacing it with fake, but realistic-looking values. This approach is useful...
Once the round of unsupervised learning is complete, the model undergoes supervised fine-tuning, also called SFT. Here, the LLM is trained on labeled data for specific tasks. The process is designed to refine the model’s ability to perform particular functions, such astranslationor summarization...
Additionally, the neural networks model should converge to the desired solution much faster, leading to an overall better training efficiency and generalization capability (Lin et al., 1996). A two-step combined algorithm based on NARX neural network were compared by (Buevich et al., 2021) ...
Understanding the causal mechanisms behind workflows (Cheney 2010) and the generalization conditions behind data transportability (Pearl and Bareinboim 2011) are examples of theoretical models that can impact data curation, guiding users towards the generation and represen- tation of data that can be ...
When concept drift is detected, the next step is adaptation, in which model generalization and adaptability are improved by integrating different natures of base classifiers dynamically and selectively. Hen et al. [22] proposed the Bilevel Online Deep Learning Framework (BODL). When concept drift ...
DoGE: Domain Reweighting with Generalization Estimation Simin Fan, Matteo Pagliardini, Martin Jaggi. ICML 2024. [ pdf ]DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining Sang Michael Xie, Hieu Pham, Xuanyi Dong, Nan Du, Hanxiao Liu, Yifeng Lu, Percy Liang, Quoc V. Le, ...