Gradually growing the depth of Transformers during training can not only reduce training cost but also lead to improved reasoning performance, as shown by MIDAS...
Understanding human personality is crucial for web applications such as personalized recommendation and mental health assessment. Existing studies on personalit...
As AI-based code generation becomes widespread, researchers are investigating the calibration of code LLMs - ensuring their confidence scores faithfully represe...
Despite advancements in machine learning for security, rule-based detection remains prevalent in Security Operations Centers due to the resource intensiveness a...
Foundation models pretrained on large data have demonstrated remarkable zero-shot generalization capabilities across domains. Building on the success of TabPFN ...
Document shadow removal is essential for enhancing the clarity of digitized documents. Preserving high-frequency details (e.g., text edges and lines) is critica...
This paper addresses the challenge of aligning large language models (LLMs) with diverse human preferences within federated learning (FL) environments, where st...
We propose a post-training method for lower-resource languages that preserves fluency of language models even when aligned by disfluent reward models. Preferenc...
In recent years, high-performance computer vision models have achieved remarkable success in medical imaging, with some skin lesion classification systems even ...
There is no shortage of AI benchmarks in the market today, with popular options like Humanity's Last Exam HLE, ARC-AGI-2 and GDPval, among numerous others. AI a...
Automatic Sign Language Recognition (ASLR) has emerged as a vital field for bridging the gap between deaf and hearing communities. However, the problem of sign-...
This paper introduces the first publicly available dataset for Automatic Essay Scoring (AES) and feedback generation in Basque, targeting the CEFR C1 proficienc...
