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
StarGen: A Spatiotemporal Autoregression Framework with Video Diffusion Model for Scalable and Controllable Scene Generation
Shangjin Zhai, Zhichao Ye, Jialin Liu, Weijian Xie, Jiaqi Hu, Zhen Peng, Hua Xue, Danpeng Chen, Xiaomeng Wang, Lei Yang, Nan Wang, Haomin Liu, Guofeng Zhang
From My View to Yours: Ego-Augmented Learning in Large Vision Language Models for Understanding Exocentric Daily Living Activities
Dominick Reilly, Manish Kumar Govind, Srijan Das
Facilitate Collaboration between Large Language Model and Task-specific Model for Time Series Anomaly Detection
Feiyi Chen, Leilei Zhang, Guansong Pang, Roger Zimmermann, Shuiguang Deng
Soup to go: mitigating forgetting during continual learning with model averaging
Anat Kleiman, Gintare Karolina Dziugaite, Jonathan Frankle, Sham Kakade, Mansheej Paul
The dynamics of meaning through time: Assessment of Large Language Models
Mohamed Taher Alrefaie, Fatty Salem, Nour Eldin Morsy, Nada Samir, Mohamed Medhat Gaber
A survey of textual cyber abuse detection using cutting-edge language models and large language models
Jose A. Diaz-Garcia, Joao Paulo Carvalho
Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model
Gregor Geigle, Florian Schneider, Carolin Holtermann, Chris Biemann, Radu Timofte, Anne Lauscher, Goran Glavaš
Self-Adaptive ERP: Embedding NLP into Petri-Net creation and Model Matching
Ahmed Maged, Gamal Kassem
Data Augmentation for Deep Learning Regression Tasks by Machine Learning Models
Assaf Shmuel, Oren Glickman, Teddy Lazebnik
Applying Large Language Models in Knowledge Graph-based Enterprise Modeling: Challenges and Opportunities
Benedikt Reitemeyer, Hans-Georg Fill
From Models to Network Topologies: A Topology Inference Attack in Decentralized Federated Learning
Chao Feng, Yuanzhe Gao, Alberto Huertas Celdran, Gerome Bovet, Burkhard Stiller
DDRM-PR: Fourier Phase Retrieval using Denoising Diffusion Restoration Models
Mehmet Onurcan Kaya, Figen S. Oktem
Predicting band gap from chemical composition: A simple learned model for a material property with atypical statistics
Andrew Ma, Owen Dugan, Marin Soljačić
InpDiffusion: Image Inpainting Localization via Conditional Diffusion Models
Kai Wang, Shaozhang Niu, Qixian Hao, Jiwei Zhang
Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model
Yueqin Yin, Shentao Yang, Yujia Xie, Ziyi Yang, Yuting Sun, Hany Awadalla, Weizhu Chen, Mingyuan Zhou
Exploring a Datasets Statistical Effect Size Impact on Model Performance, and Data Sample-Size Sufficiency
Arya Hatamian, Lionel Levine, Haniyeh Ehsani Oskouie, Majid Sarrafzadeh
Reducing the Gap Between Pretrained Speech Enhancement and Recognition Models Using a Real Speech-Trained Bridging Module
Zhongjian Cui, Chenrui Cui, Tianrui Wang, Mengnan He, Hao Shi, Meng Ge, Caixia Gong, Longbiao Wang, Jianwu Dang
Towards Multimodal Metaphor Understanding: A Chinese Dataset and Model for Metaphor Mapping Identification
Dongyu Zhang, Shengcheng Yin, Jingwei Yu, Zhiyao Wu, Zhen Li, Chengpei Xu, Xiaoxia Wang, Feng Xia