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1912.01703
Cited By
PyTorch: An Imperative Style, High-Performance Deep Learning Library
3 December 2019
Adam Paszke
Sam Gross
Francisco Massa
Adam Lerer
James Bradbury
Gregory Chanan
Trevor Killeen
Zeming Lin
N. Gimelshein
L. Antiga
Alban Desmaison
Andreas Köpf
E. Yang
Zach DeVito
Martin Raison
Alykhan Tejani
Sasank Chilamkurthy
Benoit Steiner
Lu Fang
Junjie Bai
Soumith Chintala
ODL
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Papers citing
"PyTorch: An Imperative Style, High-Performance Deep Learning Library"
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Title
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Useful Compact Representations for Data-Fitting
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PARMESAN: Parameter-Free Memory Search and Transduction for Dense Prediction Tasks
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97
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0
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Wasserstein Gradient Flows for Moreau Envelopes of f-Divergences in Reproducing Kernel Hilbert Spaces
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80
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Kernel PCA for Out-of-Distribution Detection
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Qinghua Tao
Kexin Lv
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OODD
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Dual Interior Point Optimization Learning
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Mathieu Tanneau
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47
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TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)
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Shae McFadden
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Roberto Jordaney
Johannes Kinder
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51
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69
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Self-Supervised Representation Learning for Nerve Fiber Distribution Patterns in 3D-PLI
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Sascha E. A. Muenzing
Meiqi Niu
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M. Axer
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Timo Dickscheid
45
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30 Jan 2024
Product Manifold Representations for Learning on Biological Pathways
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LW-FedSSL: Resource-efficient Layer-wise Federated Self-supervised Learning
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Differentially Private Gradient Flow based on the Sliced Wasserstein Distance
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Optimized View and Geometry Distillation from Multi-view Diffuser
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MIMIR: Masked Image Modeling for Mutual Information-based Adversarial Robustness
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SoftMAC: Differentiable Soft Body Simulation with Forecast-based Contact Model and Two-way Coupling with Articulated Rigid Bodies and Clothes
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Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI
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