“‘Brain Imitation Learning’ Tag”,2019-09-13 (knowledge distillation, AI safety)⁠:

Bibliography for tag reinforcement-learning/imitation-learning/brain-imitation-learning, most recent first: 66 annotations & 10 links (parent).

• See Also
• Gwern
  • “Modular Brain AUNNs for Uploads”, Gwern2023
  • “WBE and DRL: a Middle Way of Imitation Learning from the Human Brain”, Gwern2018
• Links
  • “Centaur: a Foundation Model of Human Cognition”, Binz et al 2024
  • “Instruction-Tuning Aligns LLMs to the Human Brain”, Aw et al 2023
  • “Improving Neural Network Representations Using Human Similarity Judgments”, Muttenthaler et al 2023
  • “Performance Reserves in Brain-Imaging-Based Phenotype Prediction”, Schulz et al 2022
  • “Brains and Algorithms Partially Converge in Natural Language Processing”, Caucheteux & King2022
  • “Generative Models of Brain Dynamics—A Review”, Panahi et al 2021
  • “Toward Conceptual Networks in Brain: Decoding Imagined Words from Word Reading”, He et al 2021
  • “In Vitro Neurons Learn and Exhibit Sentience When Embodied in a Simulated Game-World”, Kagan et al 2021
  • “Long-Range and Hierarchical Language Predictions in Brains and Algorithms”, Caucheteux et al 2021
  • “Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks”, Harrington & Deza2021
  • “Fine-Tuning of Deep Language Models As a Computational Framework of Modeling Listeners’ Perspective during Language Comprehension”, Tikochinski et al 2021
  • “Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks”, Ericsson et al 2021
  • “Compositional Restricted Boltzmann Machines Unveil the Brain-Wide Organization of Neural Assemblies”, Plas et al 2021
  • “Unsupervised Deep Learning Identifies Semantic Disentanglement in Single Inferotemporal Face Patch Neurons”, Higgins et al 2021
  • “Your Head Is There to Move You Around: Goal-Driven Models of the Primate Dorsal Pathway”, Mineault et al 2021
  • “Deep Learning Models of Cognitive Processes Constrained by Human Brain Connectomes”, Zhang et al 2021
  • “Monkey Plays Pac-Man With Compositional Strategies and Hierarchical Decision-Making”, Yang et al 2021
  • “Text2Brain: Synthesis of Brain Activation Maps from Free-Form Text Query”, Ngo et al 2021
  • “Capturing the Objects of Vision With Neural Networks”, Peters & Kriegeskorte2021
  • “Fitting Summary Statistics of Neural Data With a Differentiable Spiking Network Simulator”, Bellec et al 2021
  • “The Functional Specialization of Visual Cortex Emerges from Training Parallel Pathways With Self-Supervised Predictive Learning”, Bakhtiari et al 2021
  • “Embracing New Techniques in Deep Learning for Estimating Image Memorability”, Needell & Bainbridge2021
  • “A Massive 7T FMRI Dataset to Bridge Cognitive and Computational Neuroscience”, Allen et al 2021
  • “Brain-Computer Interface for Generating Personally Attractive Images”, Spape et al 2021
  • “BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn from Massive Amounts of EEG Data”, Kostas et al 2021
  • “Selective Eye-Gaze Augmentation To Enhance Imitation Learning In Atari Games”, Thammineni et al 2020
  • “MoGaze: A Dataset of Full-Body Motions That Includes Workspace Geometry and Eye-Gaze”, Kratzer et al 2020
  • “The Hearing Aid Dilemma: Amplification, Compression, and Distortion of the Neural Code”, Armstrong et al 2020
  • “Self-Supervised Natural Image Reconstruction and Rich Semantic Classification from Brain Activity”, Gaziv et al 2020
  • “Self-Supervised Learning through the Eyes of a Child”, Orhan et al 2020
  • “Deep Neural Network Models of Sound Localization Reveal How Perception Is Adapted to Real-World Environments”, Francl & McDermott2020
  • “What Does Your Gaze Reveal About You? On the Privacy Implications of Eye Tracking”, Kröger et al 2020
  • “Inducing Brain-Relevant Bias in Natural Language Processing Models”, Schwartz et al 2019
  • “Low-Dimensional Embodied Semantics for Music and Language”, Raposo et al 2019
  • “Improved Object Recognition Using Neural Networks Trained to Mimic the Brain’s Statistical Properties”, Federer et al 2019
  • “Neural System Identification With Neural Information Flow”, Seeliger et al 2019
  • “Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset”, Zhang et al 2019
  • “Neural Population Control via Deep Image Synthesis”, Bashivan et al 2019
  • “Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features”, Palazzo et al 2018
  • “Humans Can Decipher Adversarial Images”, Zhou & Firestone2018
  • “A Neurobiological Evaluation Metric for Neural Network Model Search”, Blanchard et al 2018
  • “Visceral Machines: Risk-Aversion in Reinforcement Learning With Intrinsic Physiological Rewards”, McDuff & Kapoor2018
  • “Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-Of-The-Art Deep Artificial Neural Networks”, Rajalingham et al 2018
  • “Towards Deep Modeling of Music Semantics Using EEG Regularizers”, Raposo et al 2017
  • “Neural Network Based Reinforcement Learning for Audio-Visual Gaze Control in Human-Robot Interaction”, Lathuilière et al 2017
  • “Predicting Driver Attention in Critical Situations”, Xia et al 2017
  • “The Signature of Robot Action Success in EEG Signals of a Human Observer: Decoding and Visualization Using Deep Convolutional Neural Networks”, Behncke et al 2017
  • “Towards Personalized Human AI Interaction—Adapting the Behavior of AI Agents Using Neural Signatures of Subjective Interest”, Shih et al 2017
  • “Brain Responses During Robot-Error Observation”, Welke et al 2017
  • “Using Human Brain Activity to Guide Machine Learning”, Fong et al 2017
  • “Mapping Between FMRI Responses to Movies and Their Natural Language Annotations”, Vodrahalli et al 2016
  • “Deep Learning Human Mind for Automated Visual Classification”, Spampinato et al 2016
  • “Towards an Integration of Deep Learning and Neuroscience”, Marblestone et al 2016
  • “Improving Sentence Compression by Learning to Predict Gaze”, Klerke et al 2016
  • “Neural Encoding and Decoding With Deep Learning for Dynamic Natural Vision Cerebral Cortex”
  • “Exploring Semantic Representation in Brain Activity Using Word Embeddings”
  • “Sequence Classification With Human Attention”
  • “Psych-101 Dataset [For Centaur]”
  • “Deep Reinforcement Learning from Human Preferences”
  • “Paths To High-Level Machine Intelligence”
  • “Randal Koene on Brain Understanding Before Whole Brain Emulation”
  • “The Science of Mind Reading”
  • “The Man Who Controls Computers With His Mind”
  • “Tracking Readers’ Eye Movements Can Help Computers Learn”
  • “Monkeys Play Pac-Man”
  • “AI and Neuroscience”
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