Full Potential
"Full potential" research explores maximizing the capabilities of various models and algorithms across diverse fields. Current efforts focus on improving model performance in tasks like program repair, multimodal search, and medical image segmentation, often leveraging large language models (LLMs), diffusion models, and graph neural networks. This research is significant because it aims to enhance the efficiency and accuracy of existing technologies, leading to advancements in areas such as software development, AI-assisted content creation, and healthcare diagnostics. The ultimate goal is to unlock the full capabilities of these models for practical applications and scientific discovery.
Papers
Unlocking the Potential of Digital Pathology: Novel Baselines for Compression
Maximilian Fischer, Peter Neher, Peter Schüffler, Sebastian Ziegler, Shuhan Xiao, Robin Peretzke, David Clunie, Constantin Ulrich, Michael Baumgartner, Alexander Muckenhuber, Silvia Dias Almeida, Michael Götz, Jens Kleesiek, Marco Nolden, Rickmer Braren, Klaus Maier-Hein
Unleashing the Potential of Model Bias for Generalized Category Discovery
Wenbin An, Haonan Lin, Jiahao Nie, Feng Tian, Wenkai Shi, Yaqiang Wu, Qianying Wang, Ping Chen
Evaluating the Potential of Federated Learning for Maize Leaf Disease Prediction
Thalita Mendonça Antico, Larissa F. Rodrigues Moreira, Rodrigo Moreira
Unlocking the Potential of Reverse Distillation for Anomaly Detection
Xinyue Liu, Jianyuan Wang, Biao Leng, Shuo Zhang
Untapped Potential in Self-Optimization of Hopfield Networks: The Creativity of Unsupervised Learning
Natalya Weber, Christian Guckelsberger, Tom Froese
Reconstructing Deep Neural Networks: Unleashing the Optimization Potential of Natural Gradient Descent
Weihua Liu, Said Boumaraf, Jianwu Li, Chaochao Lin, Xiabi Liu, Lijuan Niu, Naoufel Werghi
Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-Tuning
Long Mai, Julie Carson-Berndsen
Beyond [cls]: Exploring the true potential of Masked Image Modeling representations
Marcin Przewięźlikowski, Randall Balestriero, Wojciech Jasiński, Marek Śmieja, Bartosz Zieliński