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On the Feature Evolution of Temporal Difference Learning
Shangzhe Li, Dechen Zhang, Difan Zou +1
Temporal-difference (TD) learning trains neural representations using bootstrapped targets generated by evolving value estimates, yet how predictive features emerge and propagate u…
On the Memorization of Consistency Distillation for Diffusion Models
Bingqing Jiang, Difan Zou
Diffusion models are central to modern generative modeling, and understanding how they balance memorization and generalization is critical for reliable deployment. Recent work has…
Improving Implicit Regularization of SGD with Preconditioning for Least Square Problems
Junwei Su, Difan Zou, Chuan Wu
Stochastic gradient descent (SGD) exhibits strong algorithmic regularization effects in practice and plays an important role in the generalization of modern machine learning. Howev…
PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks
Junwei Su, Difan Zou, Chuan Wu
Memory-based Dynamic Graph Neural Networks (MDGNNs) are a family of dynamic graph neural networks that leverage a memory module to extract, distill, and memorize long-term temporal…
On the Limitation and Experience Replay for GNNs in Continual Learning
Junwei Su, Difan Zou, Chuan Wu
Continual learning seeks to empower models to progressively acquire information from a sequence of tasks. This approach is crucial for many real-world systems, which are dynamic an…