75 citations · 374 across the 37 of their papers we have counts for
21 papers · 1 filter
Learning Unnormalized Statistical Models via Compositional Optimization
Wei Jiang, Jiayu Qin, Lingyu Wu +3
Learning unnormalized statistical models (e.g., energy-based models) is computationally challenging due to the complexity of handling the partition function. To eschew this complex…
Learning High-Dimensional Distributions with Latent Neural Fokker-Planck Kernels
Yufan Zhou, Changyou Chen, Jinhui Xu
Learning high-dimensional distributions is an important yet challenging problem in machine learning with applications in various domains. In this paper, we introduce new techniques…
Meta-Learning with Neural Tangent Kernels
Yufan Zhou, Zhenyi Wang, Jiayi Xian +2
Model Agnostic Meta-Learning (MAML) has emerged as a standard framework for meta-learning, where a meta-model is learned with the ability of fast adapting to new tasks. However, as…
Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference
Bang An, Jie Lyu, Zhenyi Wang +6
The neural attention mechanism plays an important role in many natural language processing applications. In particular, the use of multi-head attention extends single-head attentio…
Learning Manifold Implicitly via Explicit Heat-Kernel Learning
Yufan Zhou, Changyou Chen, Jinhui Xu
Manifold learning is a fundamental problem in machine learning with numerous applications. Most of the existing methods directly learn the low-dimensional embedding of the data in…
Generative Semantic Hashing Enhanced via Boltzmann Machines
Lin Zheng, Qinliang Su, Dinghan Shen +1
Generative semantic hashing is a promising technique for large-scale information retrieval thanks to its fast retrieval speed and small memory footprint. For the tractability of tr…