567 citations · 730 across the 11 of their papers we have counts for
7 papers · 1 filter
Smooth activations and reproducibility in deep networks
Gil I. Shamir, Dong Lin, Lorenzo Coviello
Deep networks are gradually penetrating almost every domain in our lives due to their amazing success. However, with substantive performance accuracy improvements comes the price o…
MP-ResNet: Multi-path Residual Network for the Semantic segmentation of High-Resolution PolSAR Images
Lei Ding, Kai Zheng, Dong Lin +4
There are limited studies on the semantic segmentation of high-resolution Polarimetric Synthetic Aperture Radar (PolSAR) images due to the scarcity of training data and the inferen…
Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems
Zhe Chen, Yuyan Wang, Dong Lin +4
Despite deep neural network (DNN)'s impressive prediction performance in various domains, it is well known now that a set of DNN models trained with the same model specification an…
Small Towers Make Big Differences
Yuyan Wang, Zhe Zhao, Bo Dai +4
Multi-task learning aims at solving multiple machine learning tasks at the same time. A good solution to a multi-task learning problem should be generalizable in addition to being…
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
Ruoxi Wang, Rakesh Shivanna, Derek Z. Cheng +4
Learning effective feature crosses is the key behind building recommender systems. However, the sparse and large feature space requires exhaustive search to identify effective cros…
Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems
Wang-Cheng Kang, Derek Zhiyuan Cheng, Ting Chen +4
Recommender system models often represent various sparse features like users, items, and categorical features via embeddings. A standard approach is to map each unique feature valu…
Understanding and Improving Knowledge Distillation
Jiaxi Tang, Rakesh Shivanna, Zhe Zhao +4
Knowledge Distillation (KD) is a model-agnostic technique to improve model quality while having a fixed capacity budget. It is a commonly used technique for model compression, wher…