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ORCIDGabriel Kreiman
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2020 – today
- 2025
[j11]Morgan B. Talbot
, Rushikesh Zawar, Rohil Badkundri, Mengmi Zhang, Gabriel Kreiman:
Tuned Compositional Feature Replays for Efficient Stream Learning. IEEE Trans. Neural Networks Learn. Syst. 36(2): 3300-3314 (2025)- 2024
- [j10]Chenguang Li, Gabriel Kreiman, Sharad Ramanathan:
Discovering neural policies to drive behaviour by integrating deep reinforcement learning agents with biological neural networks. Nat. Mac. Intell. 6(6): 726-738 (2024) - [c21]Ravi Francesco Srinivasan, Francesca Mignacco, Martino Sorbaro, Maria Refinetti, Avi Cooper, Gabriel Kreiman, Giorgia Dellaferrera:
Forward Learning with Top-Down Feedback: Empirical and Analytical Characterization. ICLR 2024 - [c20]Vighnesh Subramaniam, Colin Conwell, Christopher Wang, Gabriel Kreiman, Boris Katz, Ignacio Cases, Andrei Barbu:
Revealing Vision-Language Integration in the Brain with Multimodal Networks. ICML 2024 - [c19]Spandan Madan, Will Xiao, Mingran Cao, Hanspeter Pfister, Margaret S. Livingstone, Gabriel Kreiman:
Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex. NeurIPS 2024 - [c18]Christopher Wang, Adam Uri Yaari, Aaditya Singh, Vighnesh Subramaniam, Dana Rosenfarb, Jan DeWitt, Pranav Misra, Joseph R. Madsen, Scellig S. Stone, Gabriel Kreiman, Boris Katz, Ignacio Cases, Andrei Barbu:
Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli. NeurIPS 2024 - [i36]Serena Bono, Spandan Madan, Ishaan Grover, Mao Yasueda, Cynthia Breazeal, Hanspeter Pfister, Gabriel Kreiman:
Look Around! Unexpected gains from training on environments in the vicinity of the target. CoRR abs/2401.15856 (2024) - [i35]Veedant Jain, Felipe dos Santos Alves Feitosa, Gabriel Kreiman:
Is AI fun? HumorDB: a curated dataset and benchmark to investigate graphical humor. CoRR abs/2406.13564 (2024) - [i34]Vighnesh Subramaniam, Colin Conwell, Christopher Wang, Gabriel Kreiman, Boris Katz, Ignacio Cases, Andrei Barbu:
Revealing Vision-Language Integration in the Brain with Multimodal Networks. CoRR abs/2406.14481 (2024) - [i33]Spandan Madan, Will Xiao, Mingran Cao, Hanspeter Pfister, Margaret S. Livingstone, Gabriel Kreiman:
Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex. CoRR abs/2406.16935 (2024) - [i32]Morgan B. Talbot, Gabriel Kreiman, James J. DiCarlo, Guy Gaziv:
L-WISE: Boosting Human Image Category Learning Through Model-Based Image Selection And Enhancement. CoRR abs/2412.09765 (2024) - 2023
- [c17]Stephen Casper, Dylan Hadfield-Menell, Gabriel Kreiman:
White-Box Adversarial Policies in Deep Reinforcement Learning. SafeAI@AAAI 2023 - [c16]Parantak Singh, You Li, Ankur Sikarwar, Weixian Lei, Difei Gao, Morgan B. Talbot, Ying Sun, Mike Zheng Shou, Gabriel Kreiman, Mengmi Zhang:
Learning to Learn: How to Continuously Teach Humans and Machines. ICCV 2023: 11674-11685 - [c15]Trenton Bricken, Xander Davies, Deepak Singh, Dmitry Krotov, Gabriel Kreiman:
Sparse Distributed Memory is a Continual Learner. ICLR 2023 - [c14]Christopher Wang, Vighnesh Subramaniam, Adam Uri Yaari, Gabriel Kreiman, Boris Katz, Ignacio Cases, Andrei Barbu:
BrainBERT: Self-supervised representation learning for intracranial recordings. ICLR 2023 - [c13]Trenton Bricken, Rylan Schaeffer, Bruno A. Olshausen, Gabriel Kreiman:
Emergence of Sparse Representations from Noise. ICML 2023: 3148-3191 - [i31]Ravi Francesco Srinivasan, Francesca Mignacco, Martino Sorbaro, Maria Refinetti, Avi Cooper, Gabriel Kreiman, Giorgia Dellaferrera:
Forward Learning with Top-Down Feedback: Empirical and Analytical Characterization. CoRR abs/2302.05440 (2023) - [i30]Christopher Wang, Vighnesh Subramaniam, Adam Uri Yaari, Gabriel Kreiman, Boris Katz, Ignacio Cases, Andrei Barbu:
BrainBERT: Self-supervised representation learning for intracranial recordings. CoRR abs/2302.14367 (2023) - [i29]Trenton Bricken, Xander Davies, Deepak Singh, Dmitry Krotov, Gabriel Kreiman:
Sparse Distributed Memory is a Continual Learner. CoRR abs/2303.11934 (2023) - 2022
- [j9]Mengmi Zhang, Marcelo Armendáriz, Will Xiao, Olivia Rose, Katarina Bendtz, Margaret S. Livingstone, Carlos R. Ponce, Gabriel Kreiman:
Look twice: A generalist computational model predicts return fixations across tasks and species. PLoS Comput. Biol. 18(11): 1010654 (2022) - [j8]Karst M. P. Hoogsteen, Sarit Szpiro, Gabriel Kreiman, Eli Peli:
Beyond the Cane: Describing Urban Scenes to Blind People for Mobility Tasks. ACM Trans. Access. Comput. 15(3): 20:1-20:29 (2022) - [c12]Giorgia Dellaferrera, Gabriel Kreiman:
Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass. ICML 2022: 4937-4955 - [c11]Stephen Casper, Max Nadeau, Dylan Hadfield-Menell, Gabriel Kreiman:
Robust Feature-Level Adversaries are Interpretability Tools. NeurIPS 2022 - [i28]Ankur Sikarwar, Gabriel Kreiman:
On the Efficacy of Co-Attention Transformer Layers in Visual Question Answering. CoRR abs/2201.03965 (2022) - [i27]Giorgia Dellaferrera, Gabriel Kreiman:
Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass. CoRR abs/2201.11665 (2022) - [i26]Spandan Madan, Li You, Mengmi Zhang, Hanspeter Pfister, Gabriel Kreiman:
What makes domain generalization hard? CoRR abs/2206.07802 (2022) - [i25]Stephen Casper, Dylan Hadfield-Menell, Gabriel Kreiman:
White-Box Adversarial Policies in Deep Reinforcement Learning. CoRR abs/2209.02167 (2022) - [i24]Xiao Liu, Ankur Sikarwar, Joo Hwee Lim, Gabriel Kreiman, Zenglin Shi, Mengmi Zhang:
Reason from Context with Self-supervised Learning. CoRR abs/2211.12817 (2022) - [i23]Mengmi Zhang, Giorgia Dellaferrera, Ankur Sikarwar, Marcelo Armendáriz, Noga Mudrik, Prachi Agrawal, Spandan Madan, Andrei Barbu, Haochen Yang, Tanishq Kumar, Meghna Sadwani, Stella Dellaferrera, Michele Pizzochero, Hanspeter Pfister, Gabriel Kreiman:
Human or Machine? Turing Tests for Vision and Language. CoRR abs/2211.13087 (2022) - [i22]Zhiwei Ding, Xuezhe Ren, Erwan J. David, Melissa Le-Hoa Vo, Gabriel Kreiman, Mengmi Zhang:
Efficient Zero-shot Visual Search via Target and Context-aware Transformer. CoRR abs/2211.13470 (2022) - [i21]Parantak Singh, You Li, Ankur Sikarwar, Weixian Lei, Daniel Gao, Morgan Bruce Talbot, Ying Sun, Mike Zheng Shou, Gabriel Kreiman, Mengmi Zhang:
Learning to Learn: How to Continuously Teach Humans and Machines. CoRR abs/2211.15470 (2022) - 2021
- [c10]Stephen Casper, Xavier Boix, Vanessa D'Amario, Ling Guo, Martin Schrimpf, Kasper Vinken, Gabriel Kreiman:
Frivolous Units: Wider Networks Are Not Really That Wide. AAAI 2021: 6921-6929 - [c9]Philipp Bomatter, Mengmi Zhang, Dimitar Karev, Spandan Madan, Claire Tseng, Gabriel Kreiman:
When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes. ICCV 2021: 255-264 - [c8]Shashi Kant Gupta, Mengmi Zhang, Chia-Chien Wu, Jeremy M. Wolfe, Gabriel Kreiman:
Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases. NeurIPS 2021: 6946-6959 - [i20]Mengmi Zhang, Will Xiao, Olivia Rose, Katarina Bendtz, Margaret S. Livingstone, Carlos R. Ponce, Gabriel Kreiman:
Look Twice: A Computational Model of Return Fixations across Tasks and Species. CoRR abs/2101.01611 (2021) - [i19]Mengmi Zhang, Rohil Badkundri, Morgan B. Talbot, Gabriel Kreiman:
Hypothesis-driven Stream Learning with Augmented Memory. CoRR abs/2104.02206 (2021) - [i18]Philipp Bomatter, Mengmi Zhang, Dimitar Karev, Spandan Madan, Claire Tseng, Gabriel Kreiman:
When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes. CoRR abs/2104.02215 (2021) - [i17]Guy Ben-Yosef, Gabriel Kreiman, Shimon Ullman:
What can human minimal videos tell us about dynamic recognition models? CoRR abs/2104.09447 (2021) - [i16]Shashi Kant Gupta, Mengmi Zhang, Chia-Chien Wu, Jeremy M. Wolfe, Gabriel Kreiman:
Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases. CoRR abs/2106.02953 (2021) - [i15]Stephen Casper, Max Nadeau, Gabriel Kreiman:
One Thing to Fool them All: Generating Interpretable, Universal, and Physically-Realizable Adversarial Features. CoRR abs/2110.03605 (2021) - 2020
- [j7]William Lotter, Gabriel Kreiman, David D. Cox:
A neural network trained for prediction mimics diverse features of biological neurons and perception. Nat. Mach. Intell. 2(4): 210-219 (2020) - [j6]Will Xiao, Gabriel Kreiman:
XDream: Finding preferred stimuli for visual neurons using generative networks and gradient-free optimization. PLoS Comput. Biol. 16(6) (2020) - [c7]Mengmi Zhang, Claire Tseng, Gabriel Kreiman:
Putting Visual Object Recognition in Context. CVPR 2020: 12982-12991 - [c6]Vincent Jacquot, Zhuofan Ying, Gabriel Kreiman:
Can Deep Learning Recognize Subtle Human Activities? CVPR 2020: 14232-14241 - [i14]Vincent Jacquot, Zhuofan Ying, Gabriel Kreiman:
Can Deep Learning Recognize Subtle Human Activities? CoRR abs/2003.13852 (2020) - [i13]Li Yuan, Will Xiao, Gabriel Kreiman, Francis E. H. Tay, Jiashi Feng, Margaret S. Livingstone:
Adversarial images for the primate brain. CoRR abs/2011.05623 (2020)
2010 – 2019
- 2019
- [i12]Mengmi Zhang, Jiashi Feng, Karla Montejo, Joseph Kwon, Joo Hwee Lim, Gabriel Kreiman:
Lift-the-Flap: Context Reasoning Using Object-Centered Graphs. CoRR abs/1902.00163 (2019) - [i11]Will Xiao, Gabriel Kreiman:
Gradient-free activation maximization for identifying effective stimuli. CoRR abs/1905.00378 (2019) - [i10]Mengmi Zhang, Claire Tseng, Gabriel Kreiman:
Putting visual object recognition in context. CoRR abs/1911.07349 (2019) - [i9]Stephen Casper, Xavier Boix, Vanessa D'Amario, Ling Guo, Martin Schrimpf, Kasper Vinken, Gabriel Kreiman:
Removable and/or Repeated Units Emerge in Overparametrized Deep Neural Networks. CoRR abs/1912.04783 (2019) - 2018
- [j5]Leyla Isik, Jedediah Singer, Joseph R. Madsen, Nancy Kanwisher, Gabriel Kreiman:
What is changing when: Decoding visual information in movies from human intracranial recordings. NeuroImage 180(Part): 147-159 (2018) - [c5]Kevin Wu, Eric Wu, Gabriel Kreiman:
Learning scene gist with convolutional neural networks to improve object recognition. CISS 2018: 1-6 - [c4]Anil Palepu, Christine Sharmini Premanathan, Feraz Azhar, Martina Vendrame, Tobias Loddenkemper, Claus Reinsberger, Gabriel Kreiman, Kimberly A. Parkerson, Sridevi V. Sarma, William S. Anderson:
Automating Interictal Spike Detection: Revisiting A Simple Threshold Rule. EMBC 2018: 299-302 - [i8]Kevin Wu, Eric Wu, Gabriel Kreiman:
Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition. CoRR abs/1803.01967 (2018) - [i7]William Lotter, Gabriel Kreiman, David D. Cox:
A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception. CoRR abs/1805.10734 (2018) - [i6]Mengmi Zhang, Jiashi Feng, Keng Teck Ma, Joo Hwee Lim, Qi Zhao, Gabriel Kreiman:
Finding any Waldo: zero-shot invariant and efficient visual search. CoRR abs/1807.10587 (2018) - [i5]Mengmi Zhang, Jiashi Feng, Joo Hwee Lim, Qi Zhao, Gabriel Kreiman:
What am I searching for? CoRR abs/1807.11926 (2018) - 2017
- [c3]William Lotter, Gabriel Kreiman, David D. Cox:
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. ICLR (Poster) 2017 - [i4]Nicholas Cheney, Martin Schrimpf, Gabriel Kreiman:
On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations. CoRR abs/1703.08245 (2017) - [i3]Hanlin Tang, Bill Lotter, Martin Schrimpf, Ana Paredes, Josue Ortega Caro, Walter Hardesty, David D. Cox, Gabriel Kreiman:
Recurrent computations for visual pattern completion. CoRR abs/1706.02240 (2017) - 2016
- [c2]Hanlin Tang, Jedediah Singer, Matias Ison, Gnel Pivazyan, Melissa Romaine, Elizabeth Meller, Victoria Perron, Marlise Arlellano, Gabriel Kreiman, Adriana Boulin, Rosa Frias, James Carroll, Sarah Dowcett:
A machine learning approach to predict episodic memory formation. CISS 2016: 539-544 - [i2]William Lotter, Gabriel Kreiman, David D. Cox:
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. CoRR abs/1605.08104 (2016) - 2015
- [i1]William Lotter, Gabriel Kreiman, David D. Cox:
Unsupervised Learning of Visual Structure using Predictive Generative Networks. CoRR abs/1511.06380 (2015) - 2012
- [j4]Kendra S. Burbank, Gabriel Kreiman:
Depression-Biased Reverse Plasticity Rule Is Required for Stable Learning at Top-Down Connections. PLoS Comput. Biol. 8(3) (2012) - [j3]Rajamanickam Murugan, Gabriel Kreiman:
Theory on the Coupled Stochastic Dynamics of Transcription and Splice-Site Recognition. PLoS Comput. Biol. 8(11) (2012) - 2011
- [j2]Gabriel Kreiman, John H. R. Maunsell:
Nine Criteria for a Measure of Scientific Output. Frontiers Comput. Neurosci. 5: 48 (2011) - [c1]Gabriel Kreiman:
Decoding ensemble activity from neurophysiological recordings in the temporal cortex. EMBC 2011: 5904-5907
2000 – 2009
- 2008
- [j1]Gabriel Kreiman:
Biological object recognition. Scholarpedia 3(6): 2667 (2008)
Coauthor Index
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