Papers
11,951 papers found
Differentiable Gradient Sampling for Learning Implicit 3D Scene Reconstructions from a Single Image
Shizhan Zhu, Sayna Ebrahimi, Angjoo Kanazawa et al.
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
Ningyu Zhang, Luoqiu Li, Xiang Chen et al.
Differentiable Scaffolding Tree for Molecule Optimization
Tianfan Fu, Wenhao Gao, Cao Xiao et al.
Differentially Private Fine-tuning of Language Models
Da Yu, Saurabh Naik, Arturs Backurs et al.
Differentially Private Fractional Frequency Moments Estimation with Polylogarithmic Space
Lun Wang, Iosif Pinelis, Dawn Song
DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools
Xingyu Lin, Zhiao Huang, Yunzhu Li et al.
Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme
Vadim Popov, Ivan Vovk, Vladimir Gogoryan et al.
Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching
Pierre-Alexandre Kamienny, Jean Tarbouriech, sylvain lamprier et al.
DISCOVERING AND EXPLAINING THE REPRESENTATION BOTTLENECK OF DNNS
Huiqi Deng, Qihan Ren, Hao Zhang et al.
Discovering Invariant Rationales for Graph Neural Networks
Yingxin Wu, Xiang Wang, An Zhang et al.
Discovering Latent Concepts Learned in BERT
Fahim Dalvi, Abdul Rafae Khan, Firoj Alam et al.
Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning
Chengping Rao, Pu Ren, Yang Liu et al.
Discrepancy-Based Active Learning for Domain Adaptation
Antoine de Mathelin, François Deheeger, Mathilde MOUGEOT et al.
Discrete Representations Strengthen Vision Transformer Robustness
Chengzhi Mao, Lu Jiang, Mostafa Dehghani et al.
Discriminative Similarity for Data Clustering
Yingzhen Yang, Ping Li
Disentanglement Analysis with Partial Information Decomposition
Seiya Tokui, Issei Sato
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals
Asma Ghandeharioun, Been Kim, Chun-Liang Li et al.
Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data
Yaxing Wang, Joost van de weijer, Lu Yu et al.
Distributionally Robust Fair Principal Components via Geodesic Descents
Hieu Vu, Toan Tran, Man-Chung Yue et al.
Distributionally Robust Models with Parametric Likelihood Ratios
Paul Michel, Tatsunori Hashimoto, Graham Neubig
Distributional Reinforcement Learning with Monotonic Splines
Yudong Luo, Guiliang Liu, Haonan Duan et al.
Distribution Compression in Near-Linear Time
Abhishek Shetty, Raaz Dwivedi, Lester Mackey
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions
Chen Zhu, Zheng Xu, Mingqing Chen et al.
DIVA: Dataset Derivative of a Learning Task
Yonatan Dukler, Alessandro Achille, Giovanni Paolini et al.