GitHub

ReSCORE: Label-free Iterative Retriever Training for Multi-hop Question Answering with Relevance-Consistency Supervision

Directly mapping complex problems ($x$) to their final solutions ($y$) poses a significant challenge, often requiring an intermediate reasoning step—a latent ...
The Lancet

Safety and efficacy of a dexamethasone-sparing regimen with daratumumab and lenalidomide in patients with frailty and newly diagnosed multiple myeloma (IFM2017-03): a phase 3 ...

bDepartment of Biostatistics and Epidemiology, University Hospital of Saint-Louis, Paris, France cDepartment of Hematology, University Hospital of Saint-Louis, Paris, France dDepartment of Hematology, ...
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Justice Dept. demotes Ed Martin, stripping Trump ally of most authority Latest batch of Epstein files has global impact Enormous freshwater reservoir discovered off the East Coast may be 20,000 years ...
IEEE

A Many-Objective Diversity-Guided Differential Evolution Algorithm for Multi-Label Feature Selection in High-Dimensional Datasets

Abstract: Multi-label classification (MLC) is crucial as it allows for a more nuanced and realistic representation of complex real-world scenarios, where instances may belong to multiple categories ...
Business Insider

MannKind Announces FDA Approval of Updated Afrezza® Label Providing Starting Dose Guidance when Switching from Multiple Daily Injections (MDI) or Insulin Pump Mealtime Therapy

DANBURY, Conn. and WESTLAKE VILLAGE, Calif., Jan. 26, 2026 (GLOBE NEWSWIRE) -- MannKind Corporation (Nasdaq: MNKD) , a biopharmaceutical company dedicated to transforming chronic disease care through ...
Ripple

Ozak AI’s ROI Matrix Reveals a Rare Multi-Bracket Target: 20× Short-Term, 80× Mid-Term, 300×+ Long-Term

There are multiple targets for Ozak AI to achieve. Based on the growth momentum demonstrated so far, there is a possibility that OZ may actually stand true to all three segments of the ROI matrix.
IEEE

Multi-Label Feature Selection by Maximum Affinity-Relevance, Maximum Affinity-Interaction and Minimum Affinity-Redundancy

Abstract: Multi-label feature selection solves the high-dimensional challenge problem in multi-label learning, and is widely used in pattern recognition, machine learning, and other related fields.