Backdrop: Stochastic Backpropagation. - Siavash Golkar; Kyle Cranmer (CoRR, 2018)

Continual Learning via Neural Pruning. - Siavash Golkar; Michael Kagan; Kyunghyun Cho (CoRR, 2019)

Task-Driven Data Verification via Gradient Descent. - Siavash Golkar; Kyunghyun Cho (CoRR, 2019)

Emergent Structures and Lifetime Structure Evolution in Artificial Neural Networks. - Siavash Golkar (CoRR, 2019)

A simple normative network approximates local non-Hebbian learning in the cortex. - Siavash Golkar; David Lipshutz; Yanis Bahroun; Anirvan M. Sengupta; Dmitri B. Chklovskii (NeurIPS, 2020)

A Biologically Plausible Neural Network for Slow Feature Analysis. - David Lipshutz; Charles Windolf; Siavash Golkar; Dmitri B. Chklovskii (NeurIPS, 2020)

A biologically plausible neural network for multi-channel Canonical Correlation Analysis. - David Lipshutz; Yanis Bahroun; Siavash Golkar; Anirvan M. Sengupta; Dmitri B. Chklovskii (CoRR, 2020)

A biologically plausible neural network for local supervision in cortical microcircuits. - Siavash Golkar; David Lipshutz; Yanis Bahroun; Anirvan M. Sengupta; Dmitri B. Chklovskii (CoRR, 2020)

Neural optimal feedback control with local learning rules. - Johannes Friedrich; Siavash Golkar; Shiva Farashahi; Alexander Genkin; Anirvan M. Sengupta; Dmitri B. Chklovskii (NeurIPS, 2021)

A Biologically Plausible Neural Network for Multichannel Canonical Correlation Analysis. - David Lipshutz; Yanis Bahroun; Siavash Golkar; Anirvan M. Sengupta; Dmitri B. Chklovskii (Neural Comput., 2021)

Biological Learning of Irreducible Representations of Commuting Transformations. - Alexander Genkin; David Lipshutz; Siavash Golkar; Tiberiu Tesileanu; Dmitri B. Chklovskii (NeurIPS, 2022)

Constrained Predictive Coding as a Biologically Plausible Model of the Cortical Hierarchy. - Siavash Golkar; Tiberiu Tesileanu; Yanis Bahroun; Anirvan M. Sengupta; Dmitri B. Chklovskii (NeurIPS, 2022)

Neural Circuits for Dynamics-Based Segmentation of Time Series. - Tiberiu Tesileanu; Siavash Golkar; Samaneh Nasiri; Anirvan M. Sengupta; Dmitri B. Chklovskii (Neural Comput., 2022)

An Online Algorithm for Contrastive Principal Component Analysis. - Siavash Golkar; David Lipshutz; Tiberiu Tesileanu; Dmitri B. Chklovskii (ICASSP, 2023)

A normative framework for deriving neural networks with multi-compartmental neurons and non-Hebbian plasticity. - David Lipshutz; Yanis Bahroun; Siavash Golkar; Anirvan M. Sengupta; Dmitri B. Chklovskii (CoRR, 2023)

Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures. - Christian Pedersen; Tiberiu Tesileanu; Tinghui Wu; Siavash Golkar; Miles D. Cranmer; Zijun Zhang; Shirley Ho (CoRR, 2023)

xVal: A Continuous Number Encoding for Large Language Models. - 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; et al. (CoRR, 2023)

Multiple Physics Pretraining for Physical Surrogate Models. - 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; et al. (CoRR, 2023)

AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models. - 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; et al. (CoRR, 2023)

Multiple Physics Pretraining for Spatiotemporal Surrogate Models. - 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; et al. (NeurIPS, 2024)

Neuronal Temporal Filters as Normal Mode Extractors. - Siavash Golkar; Jules Berman; David Lipshutz; Robert Mihai Haret; Tim Gollisch; Dmitri B. Chklovskii (CoRR, 2024)

Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task. - 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; et al. (CoRR, 2024)

What's In Your Field? Mapping Scientific Research with Knowledge Graphs and Large Language Models. - Abhipsha Das; Nicholas Lourie; Siavash Golkar; Mariel Pettee (CoRR, 2025)