activity
20192023
most citedDo RNN and LSTM have Long Memory?

40 citations · 41 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2023★ 1 cited

Cross-Layer Retrospective Retrieving via Layer Attention

Yanwen Fang, Yuxi Cai, Jintai Chen +3

More and more evidence has shown that strengthening layer interactions can enhance the representation power of a deep neural network, while self-attention excels at learning interd…

cs.LG2023

Fast Gumbel-Max Sketch and its Applications

Yuanming Zhang, Pinghui Wang, Yiyan Qi +4

The well-known Gumbel-Max Trick for sampling elements from a categorical distribution (or more generally a non-negative vector) and its variants have been widely used in areas such…

cs.LG2021

Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting

Longyuan Li, Jihai Zhang, Junchi Yan +4

Time-series is ubiquitous across applications, such as transportation, finance and healthcare. Time-series is often influenced by external factors, especially in the form of asynch…

stat.ML2020★ 40 cited

Do RNN and LSTM have Long Memory?

Jingyu Zhao, Feiqing Huang, Jia Lv +4

The LSTM network was proposed to overcome the difficulty in learning long-term dependence, and has made significant advancements in applications. With its success and drawbacks in…

stat.CO2020

Fast Generating A Large Number of Gumbel-Max Variables

Yiyan Qi, Pinghui Wang, Yuanming Zhang +3

The well-known Gumbel-Max Trick for sampling elements from a categorical distribution (or more generally a nonnegative vector) and its variants have been widely used in areas such…

cs.LG2019

Compact Autoregressive Network

Di Wang, Feiqing Huang, Jingyu Zhao +2

Autoregressive networks can achieve promising performance in many sequence modeling tasks with short-range dependence. However, when handling high-dimensional inputs and outputs, t…