“‘Dynamic Evaluation (NN)’ Tag”,2020-11-12 (compressed Transformers, continual learning; backlinks)⁠:

Bibliography for tag ai/nn/dynamic-evaluation, most recent first: 4 related tags, 37 annotations, & 11 links (parent).

[page summary]

• See Also
• Gwern
  • “Nenex: A Neural Personal Wiki Idea”, Gwern2023
• Links
  • “AUNN: Simple Implementation of Gwern’s AUNN Proposal”, Roland2024
  • “Emergent Properties With Repeated Examples”, Charton & Kempe2024
  • “Learning to (Learn at Test Time): RNNs With Expressive Hidden States”, Sun et al 2024
  • “Instruction Modeling: Instruction Tuning With Loss Over Instructions”, Shi et al 2024
  • “Test-Time Augmentation to Solve ARC”, Cole2024
  • “An Accurate and Rapidly Calibrating Speech Neuroprosthesis”, Card et al 2024
  • “Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models”, Rannen-Triki et al 2024
  • “Neural Spline Fields for Burst Image Fusion and Layer Separation”, Chugunov et al 2023
  • “Test-Time Adaptation of Discriminative Models via Diffusion Generative Feedback”, Prabhudesai et al 2023
  • “In-Context Pretraining (ICP): Language Modeling Beyond Document Boundaries”, Shi et al 2023
  • “OSD: Online Speculative Decoding”, Liu et al 2023
  • “Re-Reading Improves Reasoning in Large Language Models”, Xu et al 2023
  • “Test-Time Training on Video Streams”, Wang et al 2023
  • “TTT-NN: Test-Time Training on Nearest Neighbors for Large Language Models”, Hardt & Sun2023
  • “FWL: Meta-Learning Fast Weight Language Models”, Clark et al 2022
  • “Test-Time Training With Masked Autoencoders”, Gandelsman et al 2022
  • “Don’t Stop the Training: Continuously-Updating Self-Supervised Algorithms Best Account for Auditory Responses in the Cortex”, Orhan et al 2022
  • “Reconsidering the Past: Optimizing Hidden States in Language Models”, Yoshida & Gimpel2021
  • “Mind the Gap: Assessing Temporal Generalization in Neural Language Models § Scaling”, Lazaridou et al 2021
  • “Mind the Gap: Assessing Temporal Generalization in Neural Language Models § Dynamic Evaluation”, Lazaridou et al 2021 (page 7 org deepmind)
  • “Test-Time Training With Self-Supervision for Generalization under Distribution Shifts”, Sun et al 2019
  • “Unsupervised Domain Adaptation through Self-Supervision”, Sun et al 2019
  • “Mogrifier LSTM”, Melis et al 2019
  • “Dynamic Evaluation of Transformer Language Models”, Krause et al 2019
  • “Learning and Evaluating General Linguistic Intelligence”, Yogatama et al 2019
  • “Faster SGD Training by Minibatch Persistency”, Fischetti et al 2018
  • “Continuous Learning in a Hierarchical Multiscale Neural Network”, Wolf et al 2018
  • “Dynamic Evaluation of Neural Sequence Models”, Krause et al 2017
  • “Bayesian Recurrent Neural Networks”, Fortunato et al 2017
  • “Learning Simpler Language Models With the Differential State Framework”, II et al 2017
  • “Neural Episodic Control”, Pritzel et al 2017
  • “Multiplicative LSTM for Sequence Modeling”, Krause et al 2016
  • “Generating Sequences With Recurrent Neural Networks”, Graves2013
  • “Recurrent Neural Network Based Language Model § Dynamic Evaluation”, Mikolov et al 2010 (page 2)
  • “Fast Text Compression With Neural Networks”, Mahoney2000
  • “OpenAI API § Prompt Caching”
  • “Yu Sun”
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    • dynamic-evaluation
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