Summary of See: Sememe Entanglement Encoding For Transformer-bases Models Compression, by Jing Zhang et al.
SEE: Sememe Entanglement Encoding for Transformer-bases Models Compressionby Jing Zhang, Shuzhen Sun, Peng Zhang, Guangxing…
SEE: Sememe Entanglement Encoding for Transformer-bases Models Compressionby Jing Zhang, Shuzhen Sun, Peng Zhang, Guangxing…
EDformer: Embedded Decomposition Transformer for Interpretable Multivariate Time Series Predictionsby Sanjay Chakraborty, Ibrahim Delibasoglu, Fredrik…
Climate Aware Deep Neural Networks (CADNN) for Wind Power Simulationby Ali Forootani, Danial Esmaeili Aliabadi,…
CiTrus: Squeezing Extra Performance out of Low-data Bio-signal Transfer Learningby Eloy Geenjaar, Lie LuFirst submitted…
No More Adam: Learning Rate Scaling at Initialization is All You Needby Minghao Xu, Lichuan…
Transformers Use Causal World Models in Maze-Solving Tasksby Alex F. Spies, William Edwards, Michael I.…
NoteContrast: Contrastive Language-Diagnostic Pretraining for Medical Textby Prajwal Kailas, Max Homilius, Rahul C. Deo, Calum…
Understanding Knowledge Hijack Mechanism in In-context Learning through Associative Memoryby Shuo Wang, Issei SatoFirst submitted…
Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanismby Marzieh Mirzaeibonehkhater, Mohammad Ali Labbaf-Khaniki, Mohammad…
A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformersby Ashish Parmanand Pandey,…