179 citations · 1k across the 84 of their papers we have counts for
6 papers · 1 filter
Learning What to Remember: Long-term Episodic Memory Networks for Learning from Streaming Data
Hyunwoo Jung, Moonsu Han, Minki Kang +1
Current generation of memory-augmented neural networks has limited scalability as they cannot efficiently process data that are too large to fit in the external memory storage. One…
Learning the Compositional Spaces for Generalized Zero-shot Learning
Hanze Dong, Yanwei Fu, Sung Ju Hwang +2
This paper studies the problem of Generalized Zero-shot Learning (G-ZSL), whose goal is to classify instances belonging to both seen and unseen classes at the test time. We propose…
Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss
Sangil Jung, Changyong Son, Seohyung Lee +5
Reducing bit-widths of activations and weights of deep networks makes it efficient to compute and store them in memory, which is crucial in their deployments to resource-limited de…
Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare
Ingyo Chung, Saehoon Kim, Juho Lee +3
We present a personalized and reliable prediction model for healthcare, which can provide individually tailored medical services such as diagnosis, disease treatment, and preventio…
Uncertainty-Aware Attention for Reliable Interpretation and Prediction
Jay Heo, Hae Beom Lee, Saehoon Kim +4
Attention mechanism is effective in both focusing the deep learning models on relevant features and interpreting them. However, attentions may be unreliable since the networks that…
Adaptive Network Sparsification with Dependent Variational Beta-Bernoulli Dropout
Juho Lee, Saehoon Kim, Jaehong Yoon +3
While variational dropout approaches have been shown to be effective for network sparsification, they are still suboptimal in the sense that they set the dropout rate for each neur…