Publications

• Altabaa, A., & Lafferty, J. (2024a). Approximation of relation functions and attention mechanisms (arXiv:2402.08856). arXiv. http://arxiv.org/abs/2402.08856
  https://doi.org/10.48550/arXiv.2402.08856

 

• Altabaa, A., & Lafferty, J. (2024b). Learning Hierarchical Relational Representations through Relational Convolutions (arXiv:2310.03240). arXiv. http://arxiv.org/abs/2310.03240
  https://doi.org/10.48550/arXiv.2310.03240

 

• Altabaa, A., Webb, T., Cohen, J., & Lafferty, J. (2023). Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in Transformers (arXiv:2304.00195). arXiv. http://arxiv.org/abs/2304.00195
  https://doi.org/10.48550/arXiv.2304.00195

 

• Azabou, M., Pan, K. X., Arora, V., Knight, I. J., Dyer, E. L., & Richards, B. A. (2024, October 4). Multi-session, multi-task neural decoding from distinct cell-types and brain regions. The Thirteenth International Conference on Learning Representations.
  https://openreview.net/forum?id=IuU0wcO0mo

 

• Blau, A., Schaffer, E. S., Mishra, N., Miska, N. J., Laboratory, T. I. B., Paninski, L., & Whiteway, M. R. (2024). A study of animal action segmentation algorithms across supervised, unsupervised, and semi-supervised learning paradigms (arXiv:2407.16727). arXiv. http://arxiv.org/abs/2407.16727

 

• Biderman, D., Whiteway, M. R., Hurwitz, C., Greenspan, N., Lee, R. S., Vishnubhotla, A., Warren, R., Pedraja, F., Noone, D., Schartner, M. M., Huntenburg, J. M., Khanal, A., Meijer, G. T., Noel, J.-P., Pan-Vazquez, A., Socha, K. Z., Urai, A. E., Abbot, L., Acerbi, L., … The International Brain Laboratory. (2024). Lightning Pose: Improved animal pose estimation via semi-supervised learning, Bayesian ensembling and cloud-native open-source tools. Nature Methods, 21(7), 1316–1328. https://doi.org/10.1038/s41592-024-02319-1

 

• Chiquier, M., Mall, U., & Vondrick, C. (2024). Evolving Interpretable Visual Classifiers with Large Language Models. (In submission to European Conference on Computer Vision (ECCV) 2024). https://doi.org/10.48550/ARXIV.2404.09941

 

• De Silva, Ashwin, Rahul Ramesh, Rubing Yang, Siyu Yu, Joshua T. Vogelstein, and Pratik Chaudhari. "Prospective Learning: Learning for a Dynamic Future." NeurIPS 2024.
  https://arxiv.org/abs/2411.00109

 

• Eyre, B., Creager, E., Madras, D., Papyan, V., & Zemel, R. (2023). Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift (arXiv:2312.17463). arXiv. http://arxiv.org/abs/2312.17463
  https://doi.org/10.48550/arXiv.2312.17463

 

• H. Yang, J. Gee, and J. Shi. Brain decodes deep nets. CVPR, Spotlight, 2024. arXiv.2312.01280.
  https://doi.org/10.48550/arXiv.2312.01280

 

• Mahdaviyeh, Y., Lucas, J., Ren. M., Tolias, A., Zemel, R., Pitassi,. T. (2024). Replay Can Probably Increase Forgetting. Submitted to NeurIPS.

 

• Mao, J., Rothkopf, C. A., & Stocker, A. A. (2025). Adaptation optimizes sensory encoding for future stimuli. PLOS Computational Biology, 21(1), e1012746.
  https://doi.org/10.1371/journal.pcbi.1012746

 

• McGaughey, K. D., & Gold, J. (2023). Neuroscience 2023.
  Contributions of sensory adaptation and pupil-linked arousal to perceptual decisions about uncertain and unstable visual stimuli. Society for Neuroscience.

 

• Ramesh, R., Bisulco, A., DiTullio, R.W., Wei, L., Balasubramanian, V., Daniilidis, K. and Chaudhari, P., 2024. "Many Perception Tasks are Highly Redundant Functions of their Input Data". arXiv preprint https://arxiv.org/abs/2407.13841

 

• Rooke, S., Wang, Z., Di Tullio, R.W. and Balasubramanian, V., "Trading Place for Space: Increasing Location Resolution Reduces Contextual Capacity in Hippocampal Codes.", NeurIPS 2024. https://www.biorxiv.org/content/10.1101/2024.10.29.620785v1

 

• Tyulina, N., Emmanouil, T. A., & Levitan, S. I. 2024. ACM Conversational User Interfaces 2024. In Understanding Linguistic and Visual Factors that Affect Human Trust Perception of Virtual Agents. Luxembourg City.

 

• Wang, Z., Di Tullio, R.W., Rooke, S. and Balasubramanian, V., "Time makes space: Emergence of place fields in networks encoding temporally continuous sensory experiences." NeurIPS 2024.
  https://arxiv.org/abs/2408.05798

 

• Webb, T. W., Frankland, S. M., Altabaa, A., Segert, S., Krishnamurthy, K., Campbell, D., Russin, J., Giallanza, T., O’Reilly, R., Lafferty, J., & Cohen, J. D. (2024). The Relational Bottleneck as an Inductive Bias for Efficient Abstraction. Accepted in Trends in Cognitive Science, 2024. (arXiv:2309.06629). arXiv. http://arxiv.org/abs/2309.06629)
  https://doi.org/10.48550/arXiv.2309.06629
  

 

• Zhang, Y., Wang, Y., Jimenez-Beneto, D., Wang, Z., Azabou, M., Richards, B., Winter, O., Laboratory, I. B., Dyer, E., Paninski, L., & Hurwitz, C. (2024). Towards a “universal translator” for neural dynamics at single-cell, single-spike resolution (arXiv:2407.14668). arXiv.
  http://arxiv.org/abs/2407.14668