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ORCIDSiavash Golkar
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2020 – today
- 2025
[i22]Abhipsha Das, Nicholas Lourie, Siavash Golkar, Mariel Pettee:
What's In Your Field? Mapping Scientific Research with Knowledge Graphs and Large Language Models. CoRR abs/2503.09894 (2025)- [i21]Sofiane Ennadir, Siavash Golkar, Leopoldo Sarra:
Joint Embeddings Go Temporal. CoRR abs/2509.25449 (2025) - [i20]Jeff Shen
, François Lanusse, Liam Holden Parker, Ollie Liu, Tom Hehir, Leopoldo Sarra, Lucas Meyer, Micah Bowles, Sebastian Wagner-Carena, Helen Qu, Siavash Golkar, Alberto Bietti, Hatim Bourfoune, Nathan Cassereau, Pierre Cornette, Keiya Hirashima, Géraud Krawezik, Ruben Ohana, Nicholas Lourie, Michael McCabe, Rudy Morel, Payel Mukhopadhyay, Mariel Pettee, Bruno Régaldo-Saint Blancard, Kyunghyun Cho, Miles D. Cranmer, Shirley Ho:
Universal Spectral Tokenization via Self-Supervised Panchromatic Representation Learning. CoRR abs/2510.17959 (2025) - [i19]Michael McCabe, Payel Mukhopadhyay, Tanya Marwah, Bruno Régaldo-Saint Blancard, François Rozet, Cristiana Diaconu, Lucas Meyer, Kaze W. K. Wong, Hadi Sotoudeh, Alberto Bietti, Irina Espejo, Rio Fear, Siavash Golkar, Tom Hehir, Keiya Hirashima, Géraud Krawezik, François Lanusse, Rudy Morel, Ruben Ohana, Liam Holden Parker, Mariel Pettee, Jeff Shen, Kyunghyun Cho, Miles D. Cranmer, Shirley Ho:
Walrus: A Cross-Domain Foundation Model for Continuum Dynamics. CoRR abs/2511.15684 (2025) - [i18]Rudy Morel, Francesco Pio Ramunno, Jeff Shen, Alberto Bietti, Kyunghyun Cho, Miles D. Cranmer, Siavash Golkar, Olexandr Gugnin, Géraud Krawezik, Tanya Marwah, Michael McCabe, Lucas Meyer, Payel Mukhopadhyay, Ruben Ohana, Liam Holden Parker, Helen Qu, François Rozet, K. D. Leka, François Lanusse, David Fouhey, Shirley Ho:
Predicting partially observable dynamical systems via diffusion models with a multiscale inference scheme. CoRR abs/2511.19390 (2025) - 2024
- [c7]Michael McCabe, Bruno Régaldo-Saint Blancard, Liam Holden Parker, Ruben Ohana, Miles D. Cranmer, Alberto Bietti, Michael Eickenberg, Siavash Golkar, Géraud Krawezik, François Lanusse, Mariel Pettee, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho:
Multiple Physics Pretraining for Spatiotemporal Surrogate Models. NeurIPS 2024 - [i17]Siavash Golkar, Jules Berman, David Lipshutz, Robert Mihai Haret, Tim Gollisch, Dmitri B. Chklovskii:
Neuronal Temporal Filters as Normal Mode Extractors. CoRR abs/2401.03248 (2024) - [i16]Siavash Golkar, Alberto Bietti, Mariel Pettee, Michael Eickenberg, Miles D. Cranmer, Keiya Hirashima, Géraud Krawezik, Nicholas Lourie, Michael McCabe, Rudy Morel, Ruben Ohana, Liam Holden Parker, Bruno Régaldo-Saint Blancard, Kyunghyun Cho, Shirley Ho:
Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task. CoRR abs/2406.02585 (2024) - 2023
- [c6]Siavash Golkar, David Lipshutz, Tiberiu Tesileanu, Dmitri B. Chklovskii:
An Online Algorithm for Contrastive Principal Component Analysis. ICASSP 2023: 1-5 - [i15]David Lipshutz, Yanis Bahroun, Siavash Golkar, Anirvan M. Sengupta, Dmitri B. Chklovskii:
A normative framework for deriving neural networks with multi-compartmental neurons and non-Hebbian plasticity. CoRR abs/2302.10051 (2023) - [i14]Christian Pedersen, Tiberiu Tesileanu, Tinghui Wu, Siavash Golkar, Miles D. Cranmer, Zijun Zhang, Shirley Ho:
Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures. CoRR abs/2309.16645 (2023) - [i13]Siavash Golkar, Mariel Pettee, Michael Eickenberg, Alberto Bietti, Miles D. Cranmer, Géraud Krawezik, François Lanusse, Michael McCabe, Ruben Ohana, Liam Holden Parker, Bruno Régaldo-Saint Blancard, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho:
xVal: A Continuous Number Encoding for Large Language Models. CoRR abs/2310.02989 (2023) - [i12]Michael McCabe, Bruno Régaldo-Saint Blancard, Liam Holden Parker, Ruben Ohana, Miles D. Cranmer, Alberto Bietti, Michael Eickenberg, Siavash Golkar, Géraud Krawezik, François Lanusse, Mariel Pettee, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho:
Multiple Physics Pretraining for Physical Surrogate Models. CoRR abs/2310.02994 (2023) - [i11]François Lanusse, Liam Holden Parker, Siavash Golkar, Miles D. Cranmer, Alberto Bietti, Michael Eickenberg, Géraud Krawezik, Michael McCabe, Ruben Ohana, Mariel Pettee, Bruno Régaldo-Saint Blancard, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho:
AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models. CoRR abs/2310.03024 (2023) - 2022
- [j2]Tiberiu Tesileanu, Siavash Golkar, Samaneh Nasiri, Anirvan M. Sengupta, Dmitri B. Chklovskii:
Neural Circuits for Dynamics-Based Segmentation of Time Series. Neural Comput. 34(4): 891-938 (2022) - [c5]Alexander Genkin, David Lipshutz, Siavash Golkar, Tiberiu Tesileanu, Dmitri B. Chklovskii:
Biological Learning of Irreducible Representations of Commuting Transformations. NeurIPS 2022 - [c4]Siavash Golkar, Tiberiu Tesileanu, Yanis Bahroun, Anirvan M. Sengupta, Dmitri B. Chklovskii:
Constrained Predictive Coding as a Biologically Plausible Model of the Cortical Hierarchy. NeurIPS 2022 - [i10]Siavash Golkar, David Lipshutz, Tiberiu Tesileanu, Dmitri B. Chklovskii:
An online algorithm for contrastive Principal Component Analysis. CoRR abs/2211.07723 (2022) - 2021
- [j1]David Lipshutz, Yanis Bahroun, Siavash Golkar, Anirvan M. Sengupta, Dmitri B. Chklovskii:
A Biologically Plausible Neural Network for Multichannel Canonical Correlation Analysis. Neural Comput. 33(9): 2309-2352 (2021) - [c3]Johannes Friedrich, Siavash Golkar, Shiva Farashahi, Alexander Genkin, Anirvan M. Sengupta, Dmitri B. Chklovskii:
Neural optimal feedback control with local learning rules. NeurIPS 2021: 16358-16370 - [i9]Johannes Friedrich, Siavash Golkar, Shiva Farashahi, Alexander Genkin, Anirvan M. Sengupta, Dmitri B. Chklovskii:
Neural optimal feedback control with local learning rules. CoRR abs/2111.06920 (2021) - 2020
- [c2]Siavash Golkar, David Lipshutz, Yanis Bahroun, Anirvan M. Sengupta, Dmitri B. Chklovskii:
A simple normative network approximates local non-Hebbian learning in the cortex. NeurIPS 2020 - [c1]David Lipshutz, Charles Windolf, Siavash Golkar, Dmitri B. Chklovskii:
A Biologically Plausible Neural Network for Slow Feature Analysis. NeurIPS 2020 - [i8]David Lipshutz, Yanis Bahroun, Siavash Golkar, Anirvan M. Sengupta, Dmitri B. Chklovskii:
A biologically plausible neural network for multi-channel Canonical Correlation Analysis. CoRR abs/2010.00525 (2020) - [i7]David Lipshutz, Charlie Windolf, Siavash Golkar, Dmitri B. Chklovskii:
A biologically plausible neural network for Slow Feature Analysis. CoRR abs/2010.12644 (2020) - [i6]Siavash Golkar, David Lipshutz, Yanis Bahroun, Anirvan M. Sengupta, Dmitri B. Chklovskii:
A simple normative network approximates local non-Hebbian learning in the cortex. CoRR abs/2010.12660 (2020) - [i5]Siavash Golkar, David Lipshutz, Yanis Bahroun, Anirvan M. Sengupta, Dmitri B. Chklovskii:
A biologically plausible neural network for local supervision in cortical microcircuits. CoRR abs/2011.15031 (2020)
2010 – 2019
- 2019
- [i4]Siavash Golkar, Michael Kagan, Kyunghyun Cho:
Continual Learning via Neural Pruning. CoRR abs/1903.04476 (2019) - [i3]Siavash Golkar, Kyunghyun Cho:
Task-Driven Data Verification via Gradient Descent. CoRR abs/1905.05843 (2019) - [i2]Siavash Golkar:
Emergent Structures and Lifetime Structure Evolution in Artificial Neural Networks. CoRR abs/1911.11691 (2019) - 2018
- [i1]Siavash Golkar, Kyle Cranmer:
Backdrop: Stochastic Backpropagation. CoRR abs/1806.01337 (2018)
Coauthor Index
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