3 accepted papers
''Grokking'' is a phenomenon where a neural network first memorizes training data and generalizes poorly, but then suddenly transitions to near-perfect generalization after prolonged training. While intriguing, this delayed generalization phenomenon compromises predictability and efficiency. Ideally…
Out-of-Distribution (OOD) detection is critical for ensuring the reliability of machine learning models in safety-critical applications such as autonomous driving and medical diagnosis. While deploying personalized OOD detection directly on edge devices is desirable, it remains challenging due to la…
Graph neural networks (GNNs) have displayed significant potential in various graph-based learning tasks. However, the computational demands of deploying GNNs on large-scale graphs can grow exponentially. A recent method, termed unified graph sparsification (UGS), shows that there exists a pair consi…