Recent Trend
Recent research highlights a surge in applying artificial intelligence, particularly deep learning models like transformers and graph neural networks, to analyze diverse data types, including conversations, satellite communications, time series, and images. Current efforts focus on improving model interpretability, addressing biases, and enhancing the efficiency of algorithms for tasks such as forecasting, trend identification, and knowledge extraction. This work is significant for advancing various fields, from improving business decision-making through conversation analysis to optimizing resource allocation in satellite communication and enhancing personalized education through data mining.
Papers
A Contemporary Overview: Trends and Applications of Large Language Models on Mobile Devices
Lianjun Liu, Hongli An, Pengxuan Chen, Longxiang Ye
TRENDy: Temporal Regression of Effective Non-linear Dynamics
Matthew Ricci, Guy Pelc, Zoe Piran, Noa Moriel, Mor Nitzan
Exploring trends in audio mixes and masters: Insights from a dataset analysis
Angeliki Mourgela, Elio Quinton, Spyridon Bissas, Joshua D. Reiss, David Ronan
Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges
Minghao Shao, Abdul Basit, Ramesh Karri, Muhammad Shafique
TREND: Unsupervised 3D Representation Learning via Temporal Forecasting for LiDAR Perception
Runjian Chen, Hyoungseob Park, Bo Zhang, Wenqi Shao, Ping Luo, Alex Wong