5 papers
IBRSteG: Learning a Generalizable Steganography Framework for 3D Gaussian Splatting
Fanye Kong, Hongyu Xia, Yu Zheng +3
Recent advances in deep learning have notably improved steganographic message hiding. However, designing a generalizable steganographic approach for 3D Gaussian Splatting (3DGS) th…
Signal in the Noise: Polysemantic Interference Transfers and Predicts Cross-Model Influence
Bofan Gong, Shiyang Lai, James Evans +1
Polysemanticity is pervasive in language models and remains a major challenge for interpretation and model behavioral control. Leveraging sparse autoencoders (SAEs), we map the pol…
Fun-ASR Technical Report
Keyu An, Yanni Chen, Zhigao Chen +35
In recent years, automatic speech recognition (ASR) has witnessed transformative advancements driven by three complementary paradigms: data scaling, model size scaling, and deep in…
Adaptive Heavy-Tailed Stochastic Gradient Descent
Bodu Gong, Gustavo Enrique Batista, Pierre Lafaye de Micheaux
In the era of large-scale neural network models, optimization algorithms often struggle with generalization due to an overreliance on training loss. One key insight widely accepted…
Learning Counterfactually Decoupled Attention for Open-World Model Attribution
Yu Zheng, Boyang Gong, Fanye Kong +6
In this paper, we propose a Counterfactually Decoupled Attention Learning (CDAL) method for open-world model attribution. Existing methods rely on handcrafted design of region part…