Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Chinese Academy of SciencesCN2.9k papers
- Centre National de la Recherche ScientifiqueFR2.7k papers
- Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali di FrascatiIT2.6k papers
- Institute of High Energy PhysicsCN2.6k papers
- University of Science and Technology of ChinaCN2.5k papers
- Peking UniversityCN2.4k papers
- University of Chinese Academy of SciencesCN2.3k papers
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma IIT2.3k papers
- Institut National de Physique Nucléaire et de Physique des ParticulesFR2.3k papers
- Rutherford Appleton LaboratoryGB2.2k papers
- Istituto Nazionale di Fisica Nucleare, Sezione di GenovaIT2.2k papers
- European Organization for Nuclear ResearchCH2.2k papers
17 papers · 2 filters
Collaborative Filtering with User-Item Co-Autoregressive Models
Chao Du, Chongxuan Li, Yin Zheng +2
Deep neural networks have shown promise in collaborative filtering (CF). However, existing neural approaches are either user-based or item-based, which cannot leverage all the unde…
Trained Ternary Quantization
Chenzhuo Zhu, Song Han, Huizi Mao +1
Deep neural networks are widely used in machine learning applications. However, the deployment of large neural networks models can be difficult to deploy on mobile devices with lim…
Doubly Convolutional Neural Networks
Shuangfei Zhai, Yu Cheng, Weining Lu +1
Building large models with parameter sharing accounts for most of the success of deep convolutional neural networks (CNNs). In this paper, we propose doubly convolutional neural ne…
Combinatorial Multi-Armed Bandit with General Reward Functions
Wei Chen, Wei Hu, Fu Li +3
In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB) framework that allows a general nonlinear reward function, whose expected value may not depend only o…
Learning Boltzmann Machine with EM-like Method
Jinmeng Song, Chun Yuan
We propose an expectation-maximization-like(EMlike) method to train Boltzmann machine with unconstrained connectivity. It adopts Monte Carlo approximation in the E-step, and replac…
Towards Instance Optimal Bounds for Best Arm Identification
Lijie Chen, Jian Li, Mingda Qiao
In the classical best arm identification (Best--Arm) problem, we are given stochastic bandit arms, each associated with a reward distribution with an unknown mean. We would…
Deep Structured Energy Based Models for Anomaly Detection
Shuangfei Zhai, Yu Cheng, Weining Lu +1
In this paper, we attack the anomaly detection problem by directly modeling the data distribution with deep architectures. We propose deep structured energy based models (DSEBMs),…
Open Problem: Best Arm Identification: Almost Instance-Wise Optimality and the Gap Entropy Conjecture
Lijie Chen, Jian Li
The best arm identification problem (BEST-1-ARM) is the most basic pure exploration problem in stochastic multi-armed bandits. The problem has a long history and attracted signific…
Pure Exploration of Multi-armed Bandit Under Matroid Constraints
Lijie Chen, Anupam Gupta, Jian Li
We study the pure exploration problem subject to a matroid constraint (Best-Basis) in a stochastic multi-armed bandit game. In a Best-Basis instance, we are given stochastic ar…
Deep Transfer Learning with Joint Adaptation Networks
Mingsheng Long, Han Zhu, Jianmin Wang +1
Deep networks have been successfully applied to learn transferable features for adapting models from a source domain to a different target domain. In this paper, we present joint a…
Deep Networks with Stochastic Depth
Gao Huang, Yu Sun, Zhuang Liu +2
Very deep convolutional networks with hundreds of layers have led to significant reductions in error on competitive benchmarks. Although the unmatched expressiveness of the many la…
Joint Stochastic Approximation learning of Helmholtz Machines
Haotian Xu, Zhijian Ou
Though with progress, model learning and performing posterior inference still remains a common challenge for using deep generative models, especially for handling discrete hidden v…
Max-Margin Nonparametric Latent Feature Models for Link Prediction
Jun Zhu, Jiaming Song, Bei Chen
Link prediction is a fundamental task in statistical network analysis. Recent advances have been made on learning flexible nonparametric Bayesian latent feature models for link pre…
A Harmonic Extension Approach for Collaborative Ranking
Da Kuang, Zuoqiang Shi, Stanley Osher +1
We present a new perspective on graph-based methods for collaborative ranking for recommender systems. Unlike user-based or item-based methods that compute a weighted average of ra…
Learning to Generate with Memory
Chongxuan Li, Jun Zhu, Bo Zhang
Memory units have been widely used to enrich the capabilities of deep networks on capturing long-term dependencies in reasoning and prediction tasks, but little investigation exist…
Unsupervised Domain Adaptation with Residual Transfer Networks
Mingsheng Long, Han Zhu, Jianmin Wang +1
The recent success of deep neural networks relies on massive amounts of labeled data. For a target task where labeled data is unavailable, domain adaptation can transfer a learner…
DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks
Jie Fu, Hongyin Luo, Jiashi Feng +2
The performance of deep neural networks is well-known to be sensitive to the setting of their hyperparameters. Recent advances in reverse-mode automatic differentiation allow for o…