1 citations · 1 across the 17 of their papers we have counts for
6 papers · 1 filter
Early Learning Shapes Later Directions Of Representation Change In Continual Learning
Yuantao Deng, Jinnuo Liu, Kaizhen Tan +1
Representations continually change as a network learns new tasks. We ask whether early representational changes naturally form a geometric structure that continues to shape later l…
Beyond Multimodal Alignment: Shared Physical Representations Across Sensors and Action Orders
Kaizhen Tan, Xin Xu, Siru Tao +4
Multimodal world models are often evaluated by whether different sensors produce similar representations. However, similar representations do not necessarily imply that the models…
What Can Latent World Models Know? Physical Information in Multimodal Predictive Representations
Kaizhen Tan, Sizhe Xu, Xin Xu +6
A central premise of latent world models is that predicting the future encourages representations to internalize the physics of their environment. We ask which physical quantities…
Automorphism-Induced Non-Canonicity in Top-k Explanations of Graph Neural Networks
Xin Xu, Siru Tao, Kaizhen Tan
A gradient-based GNN explainer given a molecule with two chemically equivalent nitro groups assigns them attribution scores that are equal to the last bit. It cannot do otherwise:…
How Much Can a LoRA Adapter Memorize? Measuring Adapter Capacity in Bits
Kaizhen Tan, Heqing Du, Yang Feng
LoRA adapters are often shared on their own, and the amount of information they can hold about their training data bounds what sharing them can reveal. Following Morris et al. (202…
Multimodal Deep Learning for ATCO Command Lifecycle Modeling and Workload Prediction
Kaizhen Tan
Air traffic controllers (ATCOs) issue high-intensity voice commands in dense airspace, where accurate workload modeling is critical for safety and efficiency. This paper proposes a…