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1000 results for “OpenRefine”
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Assisted Learning for Organizations with Limited Imbalanced Data
Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou
Action editor: Tie-Yan Liu.
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Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training
Utku Ozbulak, Hyun Jung Lee, Beril Boga et al.
Action editor: Neil Houlsby.
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Around the same time, and in a similar vein, a number of scholars inluding Janneke Adema (@openreflections), Gary Hall (@garyhall), Eileen Joy, and Guy Geltner had also written numerous critiques of the thinly-veiled for-profit goals of those academic social networks. #OAWeek #OAWeek23
All of these critiques have been collected & documented (together with a comprehensive bibliography) in Volume 9 of the Culture Machine Liquid Books series titled
"Really, We're Helping To Build This . . . Business: The Academia.edu Files"
http://liquidbooks.pbworks.com/w/page/106236504/The%20Academia_edu%20Files
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Off-Policy Evaluation with Out-of-Sample Guarantees
Sofia Ek, Dave Zachariah, Fredrik D. Johansson, Peter Stoica
Action editor: Alain Oliviero Durmus.
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Off-Policy Evaluation with Out-of-Sample Guarantees
Sofia Ek, Dave Zachariah, Fredrik D. Johansson, Peter Stoica
Action editor: Alain Oliviero Durmus.
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Off-Policy Evaluation with Out-of-Sample Guarantees
Sofia Ek, Dave Zachariah, Fredrik D. Johansson, Peter Stoica
Action editor: Alain Oliviero Durmus.
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Off-Policy Evaluation with Out-of-Sample Guarantees
Sofia Ek, Dave Zachariah, Fredrik D. Johansson, Peter Stoica
Action editor: Alain Oliviero Durmus.
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Off-Policy Evaluation with Out-of-Sample Guarantees
Sofia Ek, Dave Zachariah, Fredrik D. Johansson, Peter Stoica
Action editor: Alain Oliviero Durmus.
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Equivariant Mesh Attention Networks
Sourya Basu, Jose Gallego-Posada, Francesco Viganò, James Rowbottom, Taco Cohen
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Efficient Inference With Model Cascades
Luzian Lebovitz, Lukas Cavigelli, Michele Magno, Lorenz K Muller
Action editor: Yarin Gal.
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Learned Thresholds Token Merging and Pruning for Vision Transformers
Maxim Bonnaerens, Joni Dambre
Action editor: Mathieu Salzmann.
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Optimizing Learning Rate Schedules for Iterative Pruning of Deep Neural Networks
Shiyu Liu, Rohan Ghosh, John Chong Min Tan, Mehul Motani
Action editor: Mingsheng Long.
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Foiling Explanations in Deep Neural Networks
Snir Vitrack Tamam, Raz Lapid, Moshe Sipper
Action editor: Jakub Tomczak.
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Training with Mixed-Precision Floating-Point Assignments
Wonyeol Lee, Rahul Sharma, Alex Aiken
Action editor: Nadav Cohen.
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Partition-Based Active Learning for Graph Neural Networks
Jiaqi Ma, Ziqiao Ma, Joyce Chai, Qiaozhu Mei
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Non-Deterministic Behavior of Thompson Sampling with Linear Payoffs and How to Avoid It
Doruk Kilitcioglu, Serdar Kadioglu
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Scalable Deep Compressive Sensing
Zhonghao Zhang, Yipeng Liu, Xingyu Cao, Fei Wen, Ce Zhu
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Intrinsic Dimension for Large-Scale Geometric Learning
Maximilian Stubbemann, Tom Hanika, Friedrich Martin Schneider
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On Average-Case Error Bounds for Kernel-Based Bayesian Quadrature
Xu Cai, Thanh Lam, Jonathan Scarlett
Action editor: Nishant Mehta.
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Unifying physical systems’ inductive biases in neural ODE using dynamics constraints
Yi Heng Lim, Muhammad Firmansyah Kasim
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On the Role of Fixed Points of Dynamical Systems in Training Physics-Informed Neural Networks
Franz M. Rohrhofer, Stefan Posch, Clemens Gößnitzer, Bernhard C Geiger
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Our work towards the design of deeper and competitive Forward-Forward Networks has been accepted at #TMLR.
https://openreview.net/forum?id=a7KP5uo0FpThis was joint work with Inton Tsang (@inton) and Thomas Dooms. Kudos to Thomas as this was work he conducted as part of his CS Master Thesis project @UAntwerpen
#locallearning #FF @IDLabResearch
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Our work towards the design of deeper and competitive Forward-Forward Networks has been accepted at #TMLR.
https://openreview.net/forum?id=a7KP5uo0FpThis was joint work with Inton Tsang (@inton) and Thomas Dooms. Kudos to Thomas as this was work he conducted as part of his CS Master Thesis project @UAntwerpen
#locallearning #FF @IDLabResearch
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Our work towards the design of deeper and competitive Forward-Forward Networks has been accepted at #TMLR.
https://openreview.net/forum?id=a7KP5uo0FpThis was joint work with Inton Tsang (@inton) and Thomas Dooms. Kudos to Thomas as this was work he conducted as part of his CS Master Thesis project @UAntwerpen
#locallearning #FF @IDLabResearch
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Our work towards the design of deeper and competitive Forward-Forward Networks has been accepted at #TMLR.
https://openreview.net/forum?id=a7KP5uo0FpThis was joint work with Inton Tsang (@inton) and Thomas Dooms. Kudos to Thomas as this was work he conducted as part of his CS Master Thesis project @UAntwerpen
#locallearning #FF @IDLabResearch
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New work by Heiko, Fredrik, Jan-Willem and myself interpreting Generative Flow Networks (GFN) as generative models trained by variational inference!
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New work by Heiko, Fredrik, Jan-Willem and myself interpreting Generative Flow Networks (GFN) as generative models trained by variational inference!
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New work by Heiko, Fredrik, Jan-Willem and myself interpreting Generative Flow Networks (GFN) as generative models trained by variational inference!
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New work by Heiko, Fredrik, Jan-Willem and myself interpreting Generative Flow Networks (GFN) as generative models trained by variational inference!
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New work by Heiko, Fredrik, Jan-Willem and myself interpreting Generative Flow Networks (GFN) as generative models trained by variational inference!