167 citations · 1k across the 81 of their papers we have counts for
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Learning Hierarchical Features with Joint Latent Space Energy-Based Prior
Jiali Cui, Ying Nian Wu, Tian Han
This paper studies the fundamental problem of multi-layer generator models in learning hierarchical representations. The multi-layer generator model that consists of multiple layer…
Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning
Pan Lu, Liang Qiu, Kai-Wei Chang +5
Mathematical reasoning, a core ability of human intelligence, presents unique challenges for machines in abstract thinking and logical reasoning. Recent large pre-trained language…
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection
Cristian Challu, Peihong Jiang, Ying Nian Wu +1
Multivariate time series anomaly detection has become an active area of research in recent years, with Deep Learning models outperforming previous approaches on benchmark datasets.…
Iterative Teacher-Aware Learning
Luyao Yuan, Dongruo Zhou, Junhong Shen +5
In human pedagogy, teachers and students can interact adaptively to maximize communication efficiency. The teacher adjusts her teaching method for different students, and the stude…
Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification
Bo Pang, Ying Nian Wu
We propose a latent space energy-based prior model for text generation and classification. The model stands on a generator network that generates the text sequence based on a conti…
Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI
Quanshi Zhang, Tian Han, Lixin Fan +5
This is the Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI. Deep neural networks (DNNs) have undoubtedly brought grea…