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1000 results for “OpenRefine”
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Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networks
Yifan Chen, Tianning Xu, Dilek Hakkani-Tur, Di Jin, Yun Yang, Ruoqing Zhu
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A Stochastic Proximal Polyak Step Size
Fabian Schaipp, Robert M. Gower, Michael Ulbrich
Action editor: Stephen Becker.
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A Stochastic Proximal Polyak Step Size
Fabian Schaipp, Robert M. Gower, Michael Ulbrich
Action editor: Stephen Becker.
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A Stochastic Proximal Polyak Step Size
Fabian Schaipp, Robert M. Gower, Michael Ulbrich
Action editor: Stephen Becker.
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A Stochastic Proximal Polyak Step Size
Fabian Schaipp, Robert M. Gower, Michael Ulbrich
Action editor: Stephen Becker.
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Improved Differentially Private Riemannian Optimization: Fast Sampling and Variance Reduction
Saiteja Utpala, Andi Han, Pratik Jawanpuria, Bamdev Mishra
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Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices...
Saiteja Utpala, Praneeth Vepakomma, Nina Miolane
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Teaching Smaller Language Models To Generalise To Unseen Compositional Questions
Tim Hartill, Neset TAN, Michael Witbrock, Patricia J. Riddle
Action editor: Karthik Narasimhan.
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Teaching Smaller Language Models To Generalise To Unseen Compositional Questions
Tim Hartill, Neset TAN, Michael Witbrock, Patricia J. Riddle
Action editor: Karthik Narasimhan.
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Constrained Parameter Inference as a Principle for Learning
Nasir Ahmad, Ellen Schrader, Marcel van Gerven
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Our #ICLR2023 workshop on Physics4ML is open for submissions. Deadline: 3rd February.
Submit your work on physics-based ML, equivariance, etc here: https://openreview.net/group?id=ICLR.cc/2023/Workshop/Physics4ML
More info:
https://physics4ml.github.io/https://mobile.twitter.com/tk_rusch/status/1610305901558210563
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We're organising a workshop on Physics for ML at #ICLR2023.
Submit your work on physics-based ML, equivariance, etc.
Site: https://physics4ml.github.io
OpenReview: https://openreview.net/group?id=ICLR.cc/2023/Workshop/Physics4MLDeadline 3rd Feb.
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Empirical Limitations of the NTK for Understanding Scaling Laws in Deep Learning
Nikhil Vyas, Yamini Bansal, Preetum Nakkiran
Action editor: Jinwoo Shin.
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Empirical Limitations of the NTK for Understanding Scaling Laws in Deep Learning
Nikhil Vyas, Yamini Bansal, Preetum Nakkiran
Action editor: Jinwoo Shin.
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Empirical Limitations of the NTK for Understanding Scaling Laws in Deep Learning
Nikhil Vyas, Yamini Bansal, Preetum Nakkiran
Action editor: Jinwoo Shin.
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Empirical Limitations of the NTK for Understanding Scaling Laws in Deep Learning
Nikhil Vyas, Yamini Bansal, Preetum Nakkiran
Action editor: Jinwoo Shin.
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Clustering using Approximate Nearest Neighbour Oracles
Enayat Ullah, Harry Lang, Raman Arora, Vladimir Braverman
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Clustering using Approximate Nearest Neighbour Oracles
Enayat Ullah, Harry Lang, Raman Arora, Vladimir Braverman
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Clustering using Approximate Nearest Neighbour Oracles
Enayat Ullah, Harry Lang, Raman Arora, Vladimir Braverman
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Clustering using Approximate Nearest Neighbour Oracles
Enayat Ullah, Harry Lang, Raman Arora, Vladimir Braverman
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Max-Affine Spline Insights Into Deep Network Pruning
Haoran You, Randall Balestriero, Zhihan Lu et al.
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Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration
Mauricio Delbracio, Peyman Milanfar
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LEAD: Min-Max Optimization from a Physical Perspective
Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie, Ioannis Mitliagkas
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Generalizability of Adversarial Robustness Under Distribution Shifts
Kumail Alhamoud, Hasan Abed Al Kader Hammoud, Motasem Alfarra, Bernard Ghanem
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Identification of Negative Transfers in Multitask Learning Using Surrogate Models
Dongyue Li, Huy Nguyen, Hongyang Ryan Zhang
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Numerical Accounting in the Shuffle Model of Differential Privacy
Antti Koskela, Mikko A. Heikkilä, Antti Honkela
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SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch
Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan O Arik, Tomas Pfister
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SolidGen: An Autoregressive Model for Direct B-rep Synthesis
Pradeep Kumar Jayaraman, Joseph George Lambourne, Nishkrit Desai et al.
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On Characterizing the Trade-off in Invariant Representation Learning
Bashir Sadeghi, Sepehr Dehdashtian, Vishnu Boddeti