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Improving Anytime Prediction with Parallel Cascaded Networks and a
  Temporal-Difference Loss
v1v2v3v4 (latest)

Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss

Neural Information Processing Systems (NeurIPS), 2021
19 February 2021
Michael L. Iuzzolino
Michael C. Mozer
Samy Bengio
    OOD
ArXiv (abs)PDFHTML

Papers citing "Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss"

5 / 5 papers shown
Catch Your Breath: Adaptive Computation for Self-Paced Sequence Production
Catch Your Breath: Adaptive Computation for Self-Paced Sequence Production
Alexandre Galashov
Matt Jones
Rosemary Ke
Yuan Cao
Vaishnavh Nagarajan
Michael C. Mozer
165
1
0
13 Oct 2025
Handling Delay in Real-Time Reinforcement Learning
Handling Delay in Real-Time Reinforcement LearningInternational Conference on Learning Representations (ICLR), 2025
Ivan Anokhin
Rishav Rishav
Matthew D Riemer
Stephen Chung
Irina Rish
Samira Ebrahimi Kahou
277
5
0
30 Mar 2025
Adaptive recurrent vision performs zero-shot computation scaling to
  unseen difficulty levels
Adaptive recurrent vision performs zero-shot computation scaling to unseen difficulty levelsNeural Information Processing Systems (NeurIPS), 2023
Vijay Veerabadran
Srinivas Ravishankar
Yuan Tang
Ritik Raina
Virginia R. de Sa
271
8
0
12 Nov 2023
Understanding the Robustness of Multi-Exit Models under Common
  Corruptions
Understanding the Robustness of Multi-Exit Models under Common Corruptions
Akshay Mehra
Skyler Seto
Navdeep Jaitly
B. Theobald
AAML
316
5
0
03 Dec 2022
SATBench: Benchmarking the speed-accuracy tradeoff in object recognition
  by humans and dynamic neural networks
SATBench: Benchmarking the speed-accuracy tradeoff in object recognition by humans and dynamic neural networks
Ajay Subramanian
Sara A. Price
Omkar Kumbhar
E. Sizikova
N. Majaj
D. Pelli
159
1
0
16 Jun 2022
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