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ORCID1st UniReps 2023, New Orleans, LA, USA
- Marco Fumero, Emanuele Rodolà, Clémentine Dominé, Francesco Locatello, Karolina Dziugaite, Mathilde Caron:
Proceedings of UniReps: the First Workshop on Unifying Representations in Neural Models, 15 December 2023, Ernest N. Morial Convention Center, New Orleans, USA. Proceedings of Machine Learning Research 243, PMLR 2023 - Marco Fumero, Emanuele Rodolà, Clémentine Dominé, Francesco Locatello, Karolina Dziugaite, Mathilde Caron:
Preface of UniReps: the First Workshop on Unifying Representations in Neural Models. 1-10 - Sarah E. Harvey, Brett W. Larsen, Alex H. Williams:
Duality of Bures and Shape Distances with Implications for Comparing Neural Representations. 11-26 - Yufan Zhuang, Zihan Wang, Fangbo Tao, Jingbo Shang:
WavSpA: Wavelet Space Attention for Boosting Transformers' Long Sequence Learning Ability. 27-46 - Shu Zhao, Huijuan Xu:
NEUCORE: Neural Concept Reasoning for Composed Image Retrieval. 47-59 - Cindy Wu, Ekdeep Singh Lubana, Bruno Kacper Mlodozeniec, Robert Kirk, David Krueger:
What Mechanisms Does Knowledge Distillation Distill? 60-75 - Benjamin Cappell, Andreas Stoll, Williams Chukwudi Umah, Bernhard Egger:
ReWaRD: Retinal Waves for Pre-Training Artificial Neural Networks Mimicking Real Prenatal Development. 76-86 - Matteo Ferrante, Tommaso Boccato, Furkan Ozcelik, Rufin VanRullen, Nicola Toschi:
Multimodal decoding of human brain activity into images and text. 87-101 - Jun-Peng Jiang, Han-Jia Ye, Leye Wang, Yang Yang, Yuan Jiang, De-Chuan Zhan:
On Transferring Expert Knowledge from Tabular Data to Images. 102-115 - Muhammad Osama Khan, Junbang Liang, Chun-Kai Wang, Shan Yang, Yu Lou:
MeSa: Masked, Geometric, and Supervised Pre-training for Monocular Depth Estimation. 116-132 - Michael I. Ivanitskiy, Alex F. Spies, Tilman Räuker, Guillaume Corlouer, Chris Mathwin, Lucia Quirke, Can Rager, Rusheb Shah, Dan Valentine, Cecilia G. Diniz Behn, Katsumi Inoue, Samy Wu Fung:
Linearly Structured World Representations in Maze-Solving Transformers. 133-143 - Gabor Lengyel, Sabyasachi Shivkumar, Ralf M. Haefner:
A General Method for Testing Bayesian Models using Neural Data. 144-157 - Zorah Lähner, Michael Moeller:
On the Direct Alignment of Latent Spaces. 158-169 - Jingyang Zhou, Chanwoo Chun, Ajay Subramanian, Eero P. Simoncelli:
Comparing neural models using their perceptual discriminability predictions. 170-181 - Jiwoon Lee, Jaeho Lee:
Semi-Ensemble: A Simple Approach Over-parameterize Model Interpolation. 182-193 - Andrew Ligeralde, Yilun Kuang, Thomas Edward Yerxa, Miah N. Pitcher, Marla Feller, SueYeon Chung:
Unsupervised learning on spontaneous retinal activity leads to efficient neural representation geometry. 194-208 - Zuowen Wang, Longbiao Cheng, Joachim Ott, Pehuen Moure, Shih-Chii Liu:
Bio-inspired parameter reuse: Exploiting inter-frame representation similarity with recurrence for accelerating temporal visual processing. 209-222 - Khalid Oublal, Saïd Ladjal, David Benhaiem, Emmanuel Le-borgne, François Roueff:
DisCoV: Disentangling Time Series Representations via Contrastive based l-Variational Inference. 223-236 - Mohamed Shawky Sabae, Hoda Anis Baraka, Mayada Mansour Hadhoud:
NoPose-NeuS: Jointly Optimizing Camera Poses with Neural Implicit Surfaces for Multi-view Reconstruction. 237-248 - Yi-Fu Wu, Minseung Lee, Sungjin Ahn:
Object-Centric Semantic Vector Quantization. 249-266 - Thomas Lu, Aboli Marathe, Ada Martin:
Supervising Variational Autoencoder Latent Representations with Language. 267-278 - Martha Gahl, Shubham Kulkarni, Nikhil Pathak, Alex Russell, Garrison W. Cottrell:
Visual Expertise Explains Image Inversion Effects. 279-290 - Yang Zhao, Hao Zhang, Xiuyuan Hu:
Role Taxonomy of Units in Deep Neural Networks. 291-301 - Brian S. Robinson, Nathan Drenkow, Colin Conwell, Michael F. Bonner:
A sparse null code emerges in deep neural networks. 302-314 - Jinyung Hong, Theodore P. Pavlic:
Randomly Weighted Neuromodulation in Neural Networks Facilitates Learning of Manifolds Common Across Tasks. 315-325 - Meenakshi Khosla, Alex H. Williams:
Soft Matching Distance: A metric on neural representations that captures single-neuron tuning. 326-341
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