Papers
11,015 papers found
Decomposition Polyhedra of Piecewise Linear Functions
Marie-Charlotte Brandenburg, Moritz Leo Grillo, Christoph Hertrich
Deconstructing Denoising Diffusion Models for Self-Supervised Learning
Xinlei Chen, Zhuang Liu, Saining Xie et al.
Deconstructing What Makes a Good Optimizer for Autoregressive Language Models
Rosie Zhao, Depen Morwani, David Brandfonbrener et al.
DECO: Unleashing the Potential of ConvNets for Query-based Detection and Segmentation
Xinghao Chen, Siwei Li, Yijing Yang et al.
Decoupled Finetuning for Domain Generalizable Semantic Segmentation
Jaehyun Pahk, Donghyeon Kwon, Seong Joon Oh et al.
Decoupled Graph Energy-based Model for Node Out-of-Distribution Detection on Heterophilic Graphs
Yuhan Chen, Yihong Luo, Yifan Song et al.
Decoupled Subgraph Federated Learning
Javad Aliakbari, Johan Östman, Alexandre Graell i Amat
Decoupling Angles and Strength in Low-rank Adaptation
Massimo Bini, Leander Girrbach, Zeynep Akata
Decoupling Layout from Glyph in Online Chinese Handwriting Generation
Minsi Ren, Yan-Ming Zhang, yi chen
DEEM: Diffusion models serve as the eyes of large language models for image perception
Run Luo, Yunshui Li, Longze Chen et al.
Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models
Junyu Chen, Han Cai, Junsong Chen et al.
Deep Distributed Optimization for Large-Scale Quadratic Programming
Augustinos D Saravanos, Hunter Kuperman, Alex Oshin et al.
DeeperForward: Enhanced Forward-Forward Training for Deeper and Better Performance
Liang Sun, Yang Zhang, Weizhao He et al.
DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scale
Ziyang Zheng, Shan Huang, Jianyuan Zhong et al.
Deep Incomplete Multi-view Learning via Cyclic Permutation of VAEs
Xin Gao, Jian Pu
Deep Kernel Posterior Learning under Infinite Variance Prior Weights
Jorge Loria, Anindya Bhadra
Deep Kernel Relative Test for Machine-generated Text Detection
Yiliao Song, Zhenqiao Yuan, Shuhai Zhang et al.
Deep Learning Alternatives Of The Kolmogorov Superposition Theorem
Leonardo Ferreira Guilhoto, Paris Perdikaris
Deep Linear Probe Generators for Weight Space Learning
Jonathan Kahana, Eliahu Horwitz, Imri Shuval et al.
DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RL
Mathias Jackermeier, Alessandro Abate
Deep MMD Gradient Flow without adversarial training
Alexandre Galashov, Valentin De Bortoli, Arthur Gretton
Deep Networks Learn Features From Local Discontinuities in the Label Function
Prithaj Banerjee, Harish Guruprasad Ramaswamy, Mahesh Lorik Yadav et al.
Deep Random Features for Scalable Interpolation of Spatiotemporal Data
Weibin Chen, Azhir Mahmood, Michel Tsamados et al.
DeepRTL: Bridging Verilog Understanding and Generation with a Unified Representation Model
Yi Liu, Changran XU, Yunhao Zhou et al.
DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search
Huajian Xin, Z.Z. Ren, Junxiao Song et al.