activity
20172022
most citedPIANOTREE VAE: Structured Representation Learning for Polyphonic Music

21 citations · 42 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CL20222 cited

Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis

Yiming Zhang, Min Zhang, Sai Wu +1

The aspect-based sentiment analysis (ABSA) is a fine-grained task that aims to determine the sentiment polarity towards targeted aspect terms occurring in the sentence. The develop…

cs.AI20205 cited

Discovering Robust Convolutional Architecture at Targeted Capacity: A Multi-Shot Approach

Xuefei Ning, Junbo Zhao, Wenshuo Li +4

Convolutional neural networks (CNNs) are vulnerable to adversarial examples, and studies show that increasing the model capacity of an architecture topology (e.g., width expansion)…

eess.AS202021 cited

PIANOTREE VAE: Structured Representation Learning for Polyphonic Music

Ziyu Wang, Yiyi Zhang, Yixiao Zhang +4

The dominant approach for music representation learning involves the deep unsupervised model family variational autoencoder (VAE). However, most, if not all, viable attempts on thi…

cs.LG2019

Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

Minjie Wang, Da Zheng, Zihao Ye +12

Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs. In this paper, we present the design principles and im…

cs.LG2018

GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations

Zhilin Yang, Jake Zhao, Bhuwan Dhingra +4

Modern deep transfer learning approaches have mainly focused on learning generic feature vectors from one task that are transferable to other tasks, such as word embeddings in lang…

cs.AI201714 cited

Prediction Under Uncertainty with Error-Encoding Networks

Mikael Henaff, Junbo Zhao, Yann LeCun

In this work we introduce a new framework for performing temporal predictions in the presence of uncertainty. It is based on a simple idea of disentangling components of the future…