Full Model
"Full Model" research encompasses the development and improvement of large-scale machine learning models across diverse applications, aiming to enhance performance, efficiency, and robustness. Current research focuses on addressing model vulnerabilities (e.g., adversarial attacks, hallucinations), improving efficiency for resource-constrained devices, and developing specialized models for specific domains (e.g., finance, astronomy, medical imaging). This work is significant for advancing AI capabilities in various fields and for mitigating potential risks associated with deploying complex models in real-world settings.
Papers
Distilling Fine-grained Sentiment Understanding from Large Language Models
Yice Zhang, Guangyu Xie, Hongling Xu, Kaiheng Hou, Jianzhu Bao, Qianlong Wang, Shiwei Chen, Ruifeng Xu
Fashionability-Enhancing Outfit Image Editing with Conditional Diffusion Models
Qice Qin, Yuki Hirakawa, Ryotaro Shimizu, Takuya Furusawa, Edgar Simo-Serra
AEIOU: A Unified Defense Framework against NSFW Prompts in Text-to-Image Models
Yiming Wang, Jiahao Chen, Qingming Li, Xing Yang, Shouling Ji
A Grounded Observer Framework for Establishing Guardrails for Foundation Models in Socially Sensitive Domains
Rebecca Ramnauth, Dražen Brščić, Brian Scassellati
Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion Models
Jinhao Liang, Jacob K. Christopher, Sven Koenig, Ferdinando Fioretto
CARL-GT: Evaluating Causal Reasoning Capabilities of Large Language Models
Ruibo Tu, Hedvig Kjellström, Gustav Eje Henter, Cheng Zhang
From Models to Microtheories: Distilling a Model's Topical Knowledge for Grounded Question Answering
Nathaniel Weir, Bhavana Dalvi Mishra, Orion Weller, Oyvind Tafjord, Sam Hornstein, Alexander Sabol, Peter Jansen, Benjamin Van Durme, Peter Clark
Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric Ensembles
Evi M.C. Huijben, Sina Amirrajab, Josien P.W. Pluim
Enhancing Cancer Diagnosis with Explainable & Trustworthy Deep Learning Models
Badaru I. Olumuyiwa, The Anh Han, Zia U. Shamszaman
Impact of Evidence Theory Uncertainty on Training Object Detection Models
M. Tahasanul Ibrahim, Rifshu Hussain Shaik, Andreas Schwung
A Dual-Perspective Metaphor Detection Framework Using Large Language Models
Yujie Lin, Jingyao Liu, Yan Gao, Ante Wang, Jinsong Su
Where Did Your Model Learn That? Label-free Influence for Self-supervised Learning
Nidhin Harilal, Amit Kiran Rege, Reza Akbarian Bafghi, Maziar Raissi, Claire Monteleoni
Survey on Abstractive Text Summarization: Dataset, Models, and Metrics
Gospel Ozioma Nnadi, Flavio Bertini
A Backdoor Attack Scheme with Invisible Triggers Based on Model Architecture Modification
Yuan Ma, Xu Ma, Jiankang Wei, Jinmeng Tang, Xiaoyu Zhang, Yilun Lyu, Kehao Chen, Jingtong Huang
The Only Way is Ethics: A Guide to Ethical Research with Large Language Models
Eddie L. Ungless, Nikolas Vitsakis, Zeerak Talat, James Garforth, Björn Ross, Arno Onken, Atoosa Kasirzadeh, Alexandra Birch
BabyHGRN: Exploring RNNs for Sample-Efficient Training of Language Models
Patrick Haller, Jonas Golde, Alan Akbik
Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference
Jorge García-Carrasco, Alejandro Maté, Juan Trujillo
From Model Based to Learned Regularization in Medical Image Registration: A Comprehensive Review
Anna Reithmeir, Veronika Spieker, Vasiliki Sideri-Lampretsa, Daniel Rueckert, Julia A. Schnabel, Veronika A. Zimmer