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Found 10 papers, 1 papers with code
Temporal Generalization Estimation in Evolving Graphs
In synthetic random graphs, we further refine the former lower bound to show the inevitable distortion over time and empirically observe that Smart achieves good estimation performance.
AceMap: Knowledge Discovery through Academic Graph
The exponential growth of scientific literature requires effective management and extraction of valuable insights.
Graph Out-of-Distribution Generalization with Controllable Data Augmentation
The spurious correlations over hybrid distribution deviation degrade the performance of previous GNN methods and show large instability among different datasets.
Achieving Small Test Error in Mildly Overparameterized Neural Networks
Recent theoretical works on over-parameterized neural nets have focused on two aspects: optimization and generalization.
The Global Landscape of Neural Networks: An Overview
Second, we discuss a few rigorous results on the geometric properties of wide networks such as "no bad basin", and some modifications that eliminate sub-optimal local minima and/or decreasing paths to infinity.
Revisiting Landscape Analysis in Deep Neural Networks: Eliminating Decreasing Paths to Infinity
More specifically, for a large class of over-parameterized deep neural networks with appropriate regularizers, the loss function has no bad local minima and no decreasing paths to infinity.
Adding One Neuron Can Eliminate All Bad Local Minima
One of the main difficulties in analyzing neural networks is the non-convexity of the loss function which may have many bad local minima.
Understanding the Loss Surface of Neural Networks for Binary Classification
Here we focus on the training performance of single-layered neural networks for binary classification, and provide conditions under which the training error is zero at all local minima of a smooth hinge loss function.
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
We show in a series of experiments that ODIN is compatible with diverse network architectures and datasets.
Why Deep Neural Networks for Function Approximation?
We show that, for a large class of piecewise smooth functions, the number of neurons needed by a shallow network to approximate a function is exponentially larger than the corresponding number of neurons needed by a deep network for a given degree of function approximation.
