Statistical Learning
A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions
Mejbah Alam, Justin Gottschlich, Nesime Tatbul, Javier S. Turek, Tim Mattson, Abdullah Muzahid
Neural Information Processing Systems http://nips.cc/
To Trust Or Not To Trust A Classifier
Heinrich Jiang, Been Kim, Melody Guan, Maya Gupta
Neural Information Processing Systems http://nips.cc/
PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification
Standard approaches for uncertainty quantification in deep learning and physics-informed learning have persistent limitations.
Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions
Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Rong Jin, Tianbao Yang
Error bound conditions (EBC) are properties that characterize the growth of an objectivefunction when apoint ismovedawayfrom theoptimal set.
Cost
We then provide an algorithm to efficiently sketch input data using such embeddings. The sketch size of the embedding used in the algorithm has a linear dependence on the number of sketching dimensions of the input.
Clustering Redemption–Beyond the Impossibility of Kleinberg’s Axioms
Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn
Neural Information Processing Systems http://nips.cc/
Supplementary Material for Semantic Image Synthesis with Unconditional Generator JungWoo Chae
This process enables the value (feature maps) to be rearranged (through a weighted sum) to align with the form of the query, thereby reflecting their strong correspondence. The input noise is removed because its stochasticity slows down the training. Given the need for balancing between high correspondence and image quality, we empirically set the weights of our loss terms. To demonstrate the influence of the additional losses introduced in our method, we provide both quantitative and qualitative ablations in Figure S2 and S3, respectively. Nonetheless, caution is warranted when overly increasing the number of clusters.
Multiway clustering via tensor block models
Neural Information Processing Systems http://nips.cc/
