Papers
11,015 papers found
On the Almost Sure Convergence of the Stochastic Three Points Algorithm
Taha EL BAKKALI EL KADI, Omar Saadi
On the Benefits of Attribute-Driven Graph Domain Adaptation
Ruiyi Fang, Bingheng Li, zhao kang et al.
On the Benefits of Memory for Modeling Time-Dependent PDEs
Ricardo Buitrago, Tanya Marwah, Albert Gu et al.
On the Byzantine-Resilience of Distillation-Based Federated Learning
Christophe Roux, Max Zimmer, Sebastian Pokutta
On the Completeness of Invariant Geometric Deep Learning Models
Zian Li, Xiyuan Wang, Shijia Kang et al.
On the Convergence of No-Regret Dynamics in Information Retrieval Games with Proportional Ranking Functions
Omer Madmon, Idan Pipano, Itamar Reinman et al.
On the Crucial Role of Initialization for Matrix Factorization
Bingcong Li, Liang Zhang, Aryan Mokhtari et al.
On the expressiveness and spectral bias of KANs
Yixuan Wang, Jonathan W. Siegel, Ziming Liu et al.
On the Expressiveness of Rational ReLU Neural Networks With Bounded Depth
Gennadiy Averkov, Christopher Hojny, Maximilian Merkert
On the Expressive Power of Sparse Geometric MPNNs
Yonatan Sverdlov, Nadav Dym
On the Feature Learning in Diffusion Models
Andi Han, Wei Huang, Yuan Cao et al.
On-the-fly Preference Alignment via Principle-Guided Decoding
Mingye Zhu, Yi Liu, Lei Zhang et al.
On the Fourier analysis in the SO(3) space : the EquiLoPO Network
Dmitrii Zhemchuzhnikov, Sergei Grudinin
On the Hölder Stability of Multiset and Graph Neural Networks
Yair Davidson, Nadav Dym
On the Identification of Temporal Causal Representation with Instantaneous Dependence
Zijian Li, Yifan Shen, Kaitao Zheng et al.
On the Importance of Language-driven Representation Learning for Heterogeneous Federated Learning
Yunlu Yan, Chun-Mei Feng, Wangmeng Zuo et al.
On the Learn-to-Optimize Capabilities of Transformers in In-Context Sparse Recovery
Renpu Liu, Ruida Zhou, Cong Shen et al.
On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations
GUOJUN XIONG, Shufan Wang, Daniel Jiang et al.
On the Modeling Capabilities of Large Language Models for Sequential Decision Making
Martin Klissarov, R Devon Hjelm, Alexander T Toshev et al.
On the Optimal Memorization Capacity of Transformers
Tokio Kajitsuka, Issei Sato
On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent
Bingrui Li, Wei Huang, Andi Han et al.
On the Optimization Landscape of Low Rank Adaptation Methods for Large Language Models
Xu-Hui Liu, Yali Du, Jun Wang et al.
On the Performance Analysis of Momentum Method: A Frequency Domain Perspective
Xianliang Li, Jun Luo, Zhiwei Zheng et al.
On the Price of Differential Privacy for Hierarchical Clustering
Chengyuan Deng, Jie Gao, Jalaj Upadhyay et al.
On the Relation between Trainability and Dequantization of Variational Quantum Learning Models
Elies Gil-Fuster, Casper Gyurik, Adrian Perez-Salinas et al.