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Features Emerge as Discrete States: The First Application of SAEs to 3D Representations
Albert Miao, Chenliang Zhou, Jiawei Zhou +1
Sparse Autoencoders (SAEs) are a powerful dictionary learning technique for decomposing neural network activations, translating the hidden state into human ideas with high semantic…
S2M2ECG: Spatio-temporal bi-directional State Space Model Enabled Multi-branch Mamba for ECG
Huaicheng Zhang, Ruoxin Wang, Chenlian Zhou +7
As one of the most effective methods for cardiovascular disease (CVD) diagnosis, multi-lead Electrocardiogram (ECG) signals present a characteristic multi-sensor information fusion…
FreNBRDF: A Frequency-Rectified Neural Material Representation
Chenliang Zhou, Zheyuan Hu, Cengiz Oztireli
Accurate material modeling is crucial for achieving photorealistic rendering, bridging the gap between computer-generated imagery and real-world photographs. While traditional appr…
RETRO: REthinking Tactile Representation Learning with Material PriOrs
Weihao Xia, Chenliang Zhou, Cengiz Oztireli
Tactile perception is profoundly influenced by the surface properties of objects in contact. However, despite their crucial role in shaping tactile experiences, these material char…