Konrad P. Kording

38 papers A* 2A 1B 1Journal 28Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Compiling to Linear Neurons. Proc. ACM Program. Lang. Joey Velez-Ginorio, Nada Amin, Konrad P. Kording, Steve Zdancewic
2025	J	`jnl`	A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks. CoRR Georgios Mentzelopoulos, Ioannis Asmanis, Konrad P. Kording, Eva L. Dyer, Kostas Daniilidis, Flavia Vitale
2025	J	`jnl`	Causality in the human niche: lessons for machine learning. CoRR Richard D. Lange, Konrad P. Kording
2025	J	`jnl`	Compiling to linear neurons. CoRR Joey Velez-Ginorio, Nada Amin, Konrad P. Kording, Steve Zdancewic
2025	J	`jnl`	Compiling to recurrent neurons. CoRR Joey Velez-Ginorio, Nada Amin, Konrad P. Kording, Steve Zdancewic
2025	J	`jnl`	Does Object Binding Naturally Emerge in Large Pretrained Vision Transformers? CoRR Yihao Li, Saeed Salehi, Lyle H. Ungar, Konrad P. Kording
2025	J	`jnl`	Falcon: Fractional Alternating Cut with Overcoming Minima in Unsupervised Segmentation. CoRR Xiao Zhang, Xiangyu Han, Xiwen Lai, Yao Sun, Pei Zhang, Konrad P. Kording
2025	J	`jnl`	Grounding Intelligence in Movement. CoRR Melanie Segado, Felipe Parodi, Jordan K. Matelsky, Michael L. Platt, Eva B. Dyer, Konrad P. Kording
2025	J	`jnl`	Who Does What in Deep Learning? Multidimensional Game-Theoretic Attribution of Function of Neural Units. CoRR Shrey Dixit, Kayson Fakhar, Fatemeh Hadaeghi, Patrick Mineault, Konrad P. Kording, Claus C. Hilgetag
2024	J	`jnl`	Computational kinematics of dance: distinguishing hip hop genres. Frontiers Robotics AI Ben Baker, Tony Liu, Jordan Matelsky, Felipe Parodi, Brett Mensh, John W. Krakauer, Konrad P. Kording
2024	J	`jnl`	Empirical influence functions to understand the logic of fine-tuning. CoRR Jordan K. Matelsky, Lyle H. Ungar, Konrad P. Kording
2024	A	`conf`	Measuring Causal Effects of Civil Communication without Randomization. ICWSM Tony Liu, Lyle H. Ungar, Konrad P. Kording, Morgan McGuire
2024	—	`conf`	Neural decoding for BCI: the objectives, principles and the future. BCI Konrad P. Kording
2024	A*	`conf`	Neural decoding from stereotactic EEG: accounting for electrode variability across subjects. NeurIPS Georgios Mentzelopoulos, Evangelos Chatzipantazis, Ashwin G. Ramayya, Michelle J. Hedlund, Vivek P. Buch, Kostas Daniilidis, Konrad P. Kording, Flavia Vitale
2024	J	`jnl`	Neural decoding from stereotactic EEG: accounting for electrode variability across subjects. CoRR Georgios Mentzelopoulos, Evangelos Chatzipantazis, Ashwin G. Ramayya, Michelle J. Hedlund, Vivek P. Buch, Kostas Daniilidis, Konrad P. Kording, Flavia Vitale
2024	J	`jnl`	The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications. CoRR Philippe Brouillard, Chandler Squires, Jonas Wahl, Konrad P. Kording, Karen Sachs, Alexandre Drouin, Dhanya Sridhar
2024	—	`conf`	Vision-language models for decoding provider attention during neonatal resuscitation. CVPR Workshops Felipe Parodi, Jordan K. Matelsky, Alejandra Regla-Vargas, Elizabeth E. Foglia, Charis Lim, Danielle Weinberg, Konrad P. Kording, Heidi M. Herrick, Michael L. Platt
2024	J	`jnl`	Vision-language models for decoding provider attention during neonatal resuscitation. CoRR Felipe Parodi, Jordan Matelsky, Alejandra Regla-Vargas, Elizabeth Foglia, Charis Lim, Danielle Weinberg, Konrad P. Kording, Heidi M. Herrick, Michael L. Platt
2023	J	`jnl`	A large language model-assisted education tool to provide feedback on open-ended responses. CoRR Jordan K. Matelsky, Felipe Parodi, Tony Liu, Richard D. Lange, Konrad P. Kording
2023	J	`jnl`	A role for cortical interneurons as adversarial discriminators. PLoS Comput. Biol. Ari S. Benjamin, Konrad P. Kording
2023	J	`jnl`	Automated Detection of Causal Inference Opportunities: Regression Discontinuity Subgroup Discovery. Trans. Mach. Learn. Res. Tony Liu, Patrick N. Lawlor, Lyle H. Ungar, Konrad P. Kording, Rahul Ladhania
2023	—	`conf`	Deep Networks as Paths on the Manifold of Neural Representations. TAG-ML Richard D. Lange, Devin Kwok, Jordan Kyle Matelsky, Xinyue Wang, David Rolnick, Konrad P. Kording
2023	J	`jnl`	From Puzzle to Progress: How Engaging With Neurodiversity Can Improve Cognitive Science. Cogn. Sci. Marie A. R. Manalili, Amy Pearson, Justin Sulik, Louise Creechan, Mahmoud Elsherif, Inika Murkumbi, Flavio Azevedo, Kathryn L. Bonnen, Judy S. Kim, Konrad P. Kording, Julie J. Lee, Manifold Obscura, Steven K. Kapp, Jan Philipp Röer, Talia Morstead
2023	J	`jnl`	Learning domain-specific causal discovery from time series. Trans. Mach. Learn. Res. Xinyue Wang, Konrad P. Kording
2023	—	`conf`	Prospective Learning: Principled Extrapolation to the Future. CoLLAs Ashwin De Silva, Rahul Ramesh, Lyle H. Ungar, Marshall G. Hussain Shuler, Noah J. Cowan, Michael L. Platt, Chen Li, Leyla Isik, Seung-Eon Roh, Adam S. Charles, Archana Venkataraman, Brian Caffo, Javier J. How, Justus M. Kebschull, John W. Krakauer, Maxim Bichuch, Kaleab Alemayehu Kinfu, Eva Yezerets, Dinesh Jayaraman, Jong M. Shin, Soledad Villar, Ian Phillips, Carey E. Priebe, Thomas Hartung, Michael I. Miller, Jayanta Dey, Ningyuan Huang, Eric Eaton, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Randal C. Burns, Onyema Osuagwu, Brett Mensh, Alysson R. Muotri, Julia Brown, Chris White, Weiwei Yang, Andrei A. Rusu, Timothy D. Verstynen, Konrad P. Kording, Pratik Chaudhari, Joshua T. Vogelstein
2023	J	`jnl`	Tackling Climate Change with Machine Learning. ACM Comput. Surv. David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Sasha Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Kording, Carla P. Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer T. Chayes, Yoshua Bengio
2023	B	`conf`	The Interplay of Relevance, Sensory Uncertainty and Statistical Learning Influences Auditory Categorization. CogSci Janaki Sheth, Jared S. Collina, Konrad P. Kording, Yale Cohen, Maria N. Geffen
2022	J	`jnl`	Clustering units in neural networks: upstream vs downstream information. CoRR Richard D. Lange, David S. Rolnick, Konrad P. Kording
2022	J	`jnl`	Clustering units in neural networks: upstream vs downstream information. Trans. Mach. Learn. Res. Richard D. Lange, David Rolnick, Konrad P. Kording
2022	—	`conf`	Data-driven exclusion criteria for instrumental variable studies. CLeaR Tony Liu, Patrick N. Lawlor, Lyle H. Ungar, Konrad P. Kording
2022	J	`jnl`	Meta-learning Causal Discovery. CoRR Xinyue Wang, Konrad P. Kording
2022	J	`jnl`	Neural Networks as Paths through the Space of Representations. CoRR Richard D. Lange, Jordan Matelsky, Xinyue Wang, Devin Kwok, David S. Rolnick, Konrad P. Kording
2022	J	`jnl`	Prospective Learning: Back to the Future. CoRR Joshua T. Vogelstein, Timothy D. Verstynen, Konrad P. Kording, Leyla Isik, John W. Krakauer, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Carey E. Priebe, Randal C. Burns, Kwame S. Kutten, James J. Knierim, James B. Potash, Thomas Hartung, Lena Smirnova, Paul Worley, Alena V. Savonenko, Ian Phillips, Michael I. Miller, René Vidal, Jeremias Sulam, Adam S. Charles, Noah J. Cowan, Maxim Bichuch, Archana Venkataraman, Chen Li, Nitish V. Thakor, Justus M. Kebschull, Marilyn S. Albert, Jinchong Xu, Marshall G. Hussain Shuler, Brian Caffo, J. Tilak Ratnanather, Ali Geisa, Seung-Eon Roh, Eva Yezerets, Meghana Madhyastha, Javier J. How, Tyler M. Tomita, Jayanta Dey, Ningyuan Huang, Jong M. Shin, Kaleab Alemayehu Kinfu, Pratik Chaudhari, Ben Baker, Anna Schapiro, Dinesh Jayaraman, Eric Eaton, Michael L. Platt, Lyle H. Ungar, Leila Wehbe, Ádám Kepecs, Amy Christensen, Onyema Osuagwu, Bing Brunton, Brett Mensh, Alysson R. Muotri, Gabriel A. Silva, Francesca Puppo, Florian Engert, Elizabeth Hillman, Julia Brown, Chris White, Weiwei Yang
2021	J	`jnl`	Object Based Attention Through Internal Gating. CoRR Jordan Lei, Ari S. Benjamin, Konrad P. Kording
2021	J	`jnl`	Quantifying causality in data science with quasi-experiments. Nat. Comput. Sci. Tony Liu, Lyle H. Ungar, Konrad P. Kording
2020	J	`jnl`	An adversarial algorithm for variational inference with a new role for acetylcholine. CoRR Ari S. Benjamin, Konrad P. Kording
2020	A*	`conf`	Reverse-engineering deep ReLU networks. ICML David Rolnick, Konrad P. Kording
2019	—	`conf`	Towards Data-Driven Autonomous Robot-Assisted Physical Rehabilitation Therapy. ICORR Wei Shi Wang, Rochelle Mendonca, Konrad P. Kording, Mikael Avery, Michelle J. Johnson