Zobrazeno 1 - 10
of 14 385
pro vyhledávání: '"A Moens"'
Through end-to-end training to predict the next token, LLMs have become valuable tools for various tasks. Enhancing their core training in language modeling can improve numerous downstream applications. A successful approach to enhance language model
Externí odkaz:
http://arxiv.org/abs/2410.12492
Modern language models predict the next token in the sequence by considering the past text through a powerful function such as attention. However, language models have no explicit mechanism that allows them to spend computation time for planning long
Externí odkaz:
http://arxiv.org/abs/2409.00070
To assist human fact-checkers, researchers have developed automated approaches for visual misinformation detection. These methods assign veracity scores by identifying inconsistencies between the image and its caption, or by detecting forgeries in th
Externí odkaz:
http://arxiv.org/abs/2408.09939
Parameter-efficient fine-tuning (PEFT) methods are increasingly used with pre-trained language models (PLMs) for continual learning (CL). These methods typically involve training a PEFT module for each new task and employing similarity-based selectio
Externí odkaz:
http://arxiv.org/abs/2408.09053
Link prediction models can benefit from incorporating textual descriptions of entities and relations, enabling fully inductive learning and flexibility in dynamic graphs. We address the challenge of also capturing rich structured information about th
Externí odkaz:
http://arxiv.org/abs/2408.06778
The ever-growing volume of biomedical publications creates a critical need for efficient knowledge discovery. In this context, we introduce an open-source end-to-end framework designed to construct knowledge around specific diseases directly from raw
Externí odkaz:
http://arxiv.org/abs/2407.13492
We present BricksRL, a platform designed to democratize access to robotics for reinforcement learning research and education. BricksRL facilitates the creation, design, and training of custom LEGO robots in the real world by interfacing them with the
Externí odkaz:
http://arxiv.org/abs/2406.17490
Autor:
Tian, Chang, Blaschko, Matthew B., Yin, Wenpeng, Xing, Mingzhe, Yue, Yinliang, Moens, Marie-Francine
Fine-grained category discovery using only coarse-grained supervision is a cost-effective yet challenging task. Previous training methods focus on aligning query samples with positive samples and distancing them from negatives. They often neglect int
Externí odkaz:
http://arxiv.org/abs/2406.13103
Autor:
Van Landeghem, Jordy, Maity, Subhajit, Banerjee, Ayan, Blaschko, Matthew, Moens, Marie-Francine, Lladós, Josep, Biswas, Sanket
This work explores knowledge distillation (KD) for visually-rich document (VRD) applications such as document layout analysis (DLA) and document image classification (DIC). While VRD research is dependent on increasingly sophisticated and cumbersome
Externí odkaz:
http://arxiv.org/abs/2406.08226
Few-shot named entity recognition (NER) systems recognize entities using a few labeled training examples. The general pipeline consists of a span detector to identify entity spans in text and an entity-type classifier to assign types to entities. Cur
Externí odkaz:
http://arxiv.org/abs/2406.05460