NeurIPS 2024 Conference Submissions

Unleashing the power of novel conditional generative approaches for new materials discovery
Lev Novitskiy, Vladimir Lazarev, Mikhail Tiutiulnikov, Nikita Vakhrameev, Roman A. Eremin, Innokentiy S. Humonen, Andrey Kuznetsov, Denis Dimitrov, Semen Budennyy
- 15 May 2024 (modified: 06 Nov 2024)
- Submitted to NeurIPS 2024
- Readers: Everyone
Preventing Model Collapse in Deep Canonical Correlation Analysis by Noise Regularization
Junlin He, Jinxiao Du, Susu Xu, Wei Ma
- Published: 25 Sept 2024, Last Modified: 19 Jan 2025
- NeurIPS 2024 poster
- Readers: Everyone
Metalearning to Continually Learn In Context
Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber
- 15 May 2024 (modified: 06 Nov 2024)
- Submitted to NeurIPS 2024
- Readers: Everyone
What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traits
Harish Babu Manogaran, M. Maruf, Arka Daw, Kazi Sajeed Mehrab, Caleb Patrick Charpentier, Josef Uyeda, Wasila M Dahdul, Matthew J Thompson, Elizabeth G Campolongo, Kaiya L Provost, Paula Mabee, Hilmar Lapp, Anuj Karpatne
- 15 May 2024 (modified: 06 Nov 2024)
- Submitted to NeurIPS 2024
- Readers: Everyone
SARAD: Spatial Association-Aware Anomaly Detection and Diagnosis for Multivariate Time Series
Zhihao Dai, Ligang He, Shuanghua Yang, Matthew Leeke
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Neural decoding from stereotactic EEG: accounting for electrode variability across subjects
Georgios Mentzelopoulos, Evangelos Chatzipantazis, Ashwin G Ramayya, Michelle Hedlund, Vivek Buch, Kostas Daniilidis, Konrad Kording, Flavia Vitale
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning
Abdulkadir Celikkanat, Andres R Masegosa, Thomas Dyhre Nielsen
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 spotlight
- Readers: Everyone
Building on Efficient Foundations: Effective Training of LLMs with Structured Feedforward Layers
Xiuying Wei, Skander Moalla, Razvan Pascanu, Caglar Gulcehre
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Weight decay induces low-rank attention layers
Seijin Kobayashi, Yassir Akram, Johannes von Oswald
- Published: 25 Sept 2024, Last Modified: 12 May 2025
- NeurIPS 2024 poster
- Readers: Everyone
Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignment
Weichao Zhou, Wenchao Li
- Published: 25 Sept 2024, Last Modified: 16 Jan 2025
- NeurIPS 2024 poster
- Readers: Everyone
Trading Place for Space: Increasing Location Resolution Reduces Contextual Capacity in Hippocampal Codes
Spencer Rooke, Zhaoze Wang, Ronald W Di Tullio, Vijay Balasubramanian
- Published: 25 Sept 2024, Last Modified: 11 Jan 2025
- NeurIPS 2024 oral
- Readers: Everyone
Unveiling Induction Heads: Provable Training Dynamics and Feature Learning in Transformers
Siyu Chen, Heejune Sheen, Tianhao Wang, Zhuoran Yang
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
$C^2M^3$: Cycle-Consistent Multi-Model Merging
Donato Crisostomi, Marco Fumero, Daniele Baieri, Florian Bernard, Emanuele Rodolà
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
Keyu Tian, Yi Jiang, Zehuan Yuan, BINGYUE PENG, Liwei Wang
- Published: 25 Sept 2024, Last Modified: 16 Jan 2025
- NeurIPS 2024 oral
- Readers: Everyone
Understanding and Minimising Outlier Features in Transformer Training
Bobby He, Lorenzo Noci, Daniele Paliotta, Imanol Schlag, Thomas Hofmann
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Group-wise oracle-efficient algorithms for online multi-group learning
Samuel Deng, Jingwen Liu, Daniel Hsu
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Sparse Bayesian Generative Modeling for Compressive Sensing
Benedikt Böck, Sadaf Syed, Wolfgang Utschick
- Published: 25 Sept 2024, Last Modified: 15 Jan 2025
- NeurIPS 2024 poster
- Readers: Everyone
MagR: Weight Magnitude Reduction for Enhancing Post-Training Quantization
Aozhong Zhang, Naigang Wang, Yanxia Deng, Xin Li, Zi Yang, Penghang Yin
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
HHD-GP: Incorporating Helmholtz-Hodge Decomposition into Gaussian Processes for Learning Dynamical Systems
Hao Xu, Jia Pan
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Generalized Eigenvalue Problems with Generative Priors
Zhaoqiang Liu, Wen Li, Junren Chen
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Private Online Learning via Lazy Algorithms
Hilal Asi, Tomer Koren, Daogao Liu, Kunal Talwar
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Continuous Partitioning for Graph-Based Semi-Supervised Learning
Chester Holtz, Pengwen Chen, Zhengchao Wan, Chung-Kuan Cheng, Gal Mishne
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
fMRI predictors based on language models of increasing complexity recover brain left lateralization
Laurent Bonnasse-Gahot, Christophe Pallier
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
CodeRosetta: Pushing the Boundaries of Unsupervised Code Translation for Parallel Programming
Ali TehraniJamsaz, Arijit Bhattacharjee, Le Chen, Nesreen K. Ahmed, Amir Yazdanbakhsh, Ali Jannesari
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone
Normalization and effective learning rates in reinforcement learning
Clare Lyle, Zeyu Zheng, Khimya Khetarpal, James Martens, Hado van Hasselt, Razvan Pascanu, Will Dabney
- Published: 25 Sept 2024, Last Modified: 06 Nov 2024
- NeurIPS 2024 poster
- Readers: Everyone