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PIDForest: Anomaly Detection via Partial Identification
Parikshit Gopalan, Vatsal Sharan, Udi Wieder
Neural Information Processing Systems http://nips.cc/
Practical Methods for Graph Two-Sample Testing
Debarghya Ghoshdastidar, Ulrike von Luxburg
We demonstrate the practical merits and limitations of existing theoretical tests and their bootstrapped variants.
Universal Invariant and Equivariant Graph Neural Networks
Nicolas Keriven, Gabriel Peyré
More precisely, we consider networks with a single hidden layer,obtained bysumming channels formed byapplying anequivariant linear operator, a pointwise non-linearity, and either an invariant or equivariant linearoutputlayer.
FineStyle: Fine-grained Controllable Style Personalization for Text-to-image Models
Full prompts are available in Appendix A.1.
Deep Generative Markov State Models
Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noe
We propose a deep generative Markov State Model (DeepGenMSM) learningframework for inference of metastable dynamical systems and prediction of tra-jectories.
Appendix [KAKURENBO: Adaptively Hiding Samples in Deep Neural Network Training ] Anonymous Author(s) Affiliation Address email Appendix A. Proof of Lemma 1
Table 1 summarizes the models and datasets used in this work. ImageNet-1K Deng u. a. (2009): We use the subset of the ImageNet dataset containing DeepCAM Kurth u. a. (2018): DeepCAM dataset for image segmentation, which consists of Fractal-3K Kataoka u. a. (2022) A rendered dataset from the Visual Atom method Kataoka We also use the setting in Kataoka u. a. (2022) Table 2 shows the detail of our hyper-parameters. Specifically, We follow the guideline of'TorchVision' to train the ResNet-50 that uses the CosineLR To show the robustness of KAKURENBO, we also train ResNet-50 with different settings, e.g., ResNet-50 (A) setting, we follow the hyper-parameters reported in Goyal u. a. (2017). It is worth noting that KAKURENBO merely hides samples before the input pipeline. In this section, we present an analysis of the factors affecting KAKURENBO's performance, e.g., the The result shows that our method could dynamically hide the samples at each epoch.
KAKURENBO: Adaptively Hiding Samples in Deep Neural Network Training
This paper proposes a method for hiding the least-important samples during the training of deep neural networks to increase efficiency, i.e., to reduce the cost of
Astronauts arrive at ISS for 8-month mission after medical emergency forced early evacuation
Four astronauts from the U.S., France and Russia successfully arrived at the International Space Station via SpaceX rocket on Saturday, restoring full crew capacity.