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1000 results for “OpenRefine”
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Black-Box Batch Active Learning for Regression
Andreas Kirsch
Action editor: Ying Nian Wu.
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Black-Box Batch Active Learning for Regression
Andreas Kirsch
Action editor: Ying Nian Wu.
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Attacking Perceptual Similarity Metrics
Abhijay Ghildyal, Feng Liu
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Attacking Perceptual Similarity Metrics
Abhijay Ghildyal, Feng Liu
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Bridging performance gap between minimal and maximal SVM models
Ondrej Such, René Fabricius
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Bridging performance gap between minimal and maximal SVM models
Ondrej Such, René Fabricius
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Bridging performance gap between minimal and maximal SVM models
Ondrej Such, René Fabricius
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Bridging performance gap between minimal and maximal SVM models
Ondrej Such, René Fabricius
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Bridging performance gap between minimal and maximal SVM models
Ondrej Such, René Fabricius
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FASTRAIN-GNN: Fast and Accurate Self-Training for Graph Neural Networks
Amrit Nagarajan, Anand Raghunathan
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Integrating Bayesian Network Structure into Residual Flows and Variational Autoencoders
Jacobie Mouton, Rodney Stephen Kroon
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Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes
Magnus Ross, Markus Heinonen
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Sparse Coding with Multi-layer Decoders using Variance Regularization
Katrina Evtimova, Yann LeCun
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Bayesian Causal Bandits with Backdoor Adjustment Prior
Jireh Huang, Qing Zhou
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Stacking Diverse Architectures to Improve Machine Translation
Andrea Schioppa, Nal Kalchbrenner
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First paper of a PhD student in our team.
It is a preprint #OpenAccess and #OpenReview paper, so you can comment online.It is about correcting for alignement with rotation axis of the main inertial axis of the Earth in a #simulation of Mantle #convection. We need this to get plausible heat flux maps for later #geodynamo simulations.
https://egusphere.copernicus.org/preprints/2022/egusphere-2022-1172/
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Target Propagation via Regularized Inversion for Recurrent Neural Networks
Vincent Roulet, Zaid Harchaoui
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Contrastive Search Is What You Need For Neural Text Generation
Yixuan Su, Nigel Collier
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New #SurveyCertification:
Better Theory for SGD in the Nonconvex World
Ahmed Khaled, Peter Richtárik
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I am happy to share that our paper on the «#Intrinsic #Dimension for Large-Scale Geometric Learning» was published today at #TMLR (https://openreview.net/pdf?id=85BfDdYMBY)
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I am happy to share that our paper on the «#Intrinsic #Dimension for Large-Scale Geometric Learning» was published today at #TMLR (https://openreview.net/pdf?id=85BfDdYMBY)
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I am happy to share that our paper on the «#Intrinsic #Dimension for Large-Scale Geometric Learning» was published today at #TMLR (https://openreview.net/pdf?id=85BfDdYMBY)
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I am happy to share that our paper on the «#Intrinsic #Dimension for Large-Scale Geometric Learning» was published today at #TMLR (https://openreview.net/pdf?id=85BfDdYMBY)
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Don’t miss the first day of our #ExperimentalBooks conference organised by COPIM’s Experimental Publishing group (WP6) with @openreflections, @simonxix, @[email protected], @[email protected], @[email protected], @Rebekka_Kie, @garyhall, @mrchristian, and many others #OAbooks
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Sparse Coding with Multi-layer Decoders using Variance Regularization
Katrina Evtimova, Yann LeCun
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Sparse Coding with Multi-layer Decoders using Variance Regularization
Katrina Evtimova, Yann LeCun
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Sparse Coding with Multi-layer Decoders using Variance Regularization
Katrina Evtimova, Yann LeCun
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Sparse Coding with Multi-layer Decoders using Variance Regularization
Katrina Evtimova, Yann LeCun
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Optimal Convergence Rates of Deep Convolutional Neural Networks: Additive Ridge Functions
Zhiying Fang, Guang Cheng
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Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning https://arxiv.org/abs/2505.11349
Context parroting relies on short stretches of time-series data (or context). As it moves through the time series, it scans for similar patterns or motifs that appeared earlier in the sequence, and uses those patterns to predict what might come
https://openreview.net/forum?id=EUAXc9Hlvm
https://www.santafe.edu/news-center/news/a-simple-baseline-for-ai-forecasting-in-machine-learning