Papers
21,849 papers found
A Phase Transition between Positional and Semantic Learning in a Solvable Model of Dot-Product Attention
Hugo Cui, Freya Behrens, Florent Krzakala et al.
A PID Controller Approach for Adaptive Probability-dependent Gradient Decay in Model Calibration
Siyuan Zhang, Linbo Xie
APIGen: Automated PIpeline for Generating Verifiable and Diverse Function-Calling Datasets
Zuxin Liu, Thai Hoang, Jianguo Zhang et al.
A Polar coordinate system represents syntax in large language models
Pablo Diego-Simón, Stéphane D'Ascoli, Emmanuel Chemla et al.
Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models
Tuomas Kynkäänniemi, Miika Aittala, Tero Karras et al.
Approaching Human-Level Forecasting with Language Models
Danny Halawi, Fred Zhang, Chen Yueh-Han et al.
Approximated Orthogonal Projection Unit: Stabilizing Regression Network Training Using Natural Gradient
Shaoqi Wang, Chunjie Yang, Siwei Lou
Approximately Equivariant Neural Processes
Matthew Ashman, Cristiana Diaconu, Adrian Weller et al.
Approximately Pareto-optimal Solutions for Bi-Objective k-Clustering
Anna Arutyunova, Jan Eube, Heiko Röglin et al.
Approximating mutual information of high-dimensional variables using learned representations
Gokul Gowri, Xiao-Kang Lun, Allon M. Klein et al.
Approximating the Top Eigenvector in Random Order Streams
Praneeth Kacham, David P. Woodruff
Approximation-Aware Bayesian Optimization
Natalie Maus, Kyurae Kim, Geoff Pleiss et al.
Approximation Rate of the Transformer Architecture for Sequence Modeling
Haotian Jiang, Qianxiao Li
A Practitioner's Guide to Real-World Continual Multimodal Pretraining
Vishaal Udandarao, Karsten Roth, Sebastian Dziadzio et al.
A Primal-Dual-Assisted Penalty Approach to Bilevel Optimization with Coupled Constraints
Liuyuan Jiang, Quan Xiao, Victor M. Tenorio et al.
A probability contrastive learning framework for 3D molecular representation learning
Jiayu Qin, Jian Chen, Rohan Sharma et al.
A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoning
Yuanning Cui, Zequn Sun, Wei Hu
A provable control of sensitivity of neural networks through a direct parameterization of the overall bi-Lipschitzness
Yuri Kinoshita, Taro Toyoizumi
ARC: A Generalist Graph Anomaly Detector with In-Context Learning
Yixin Liu, Shiyuan Li, Yu Zheng et al.
Archaeoscape: Bringing Aerial Laser Scanning Archaeology to the Deep Learning Era
Yohann Perron, Vladyslav Sydorov, Adam P. Wijker et al.
Architect: Generating Vivid and Interactive 3D Scenes with Hierarchical 2D Inpainting
Yian Wang, Xiaowen Qiu, Jiageng Liu et al.
Arctique: An artificial histopathological dataset unifying realism and controllability for uncertainty quantification
Jannik Franzen, Claudia Winklmayr, Vanessa E. Guarino et al.
A Recipe for Charge Density Prediction
Xiang Fu, Andrew Rosen, Kyle Bystrom et al.
Are Graph Neural Networks Optimal Approximation Algorithms?
Morris Yau, Nikolaos Karalias, Eric Lu et al.
Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?
Jiacheng Cen, Anyi Li, Ning Lin et al.