Papers
Minimax Gaussian Classification & Clustering
Tianyang Li, Xinyang Yi, Constantine Carmanis et al.
NeuralPower: Predict and Deploy Energy-Efficient Convolutional Neural Networks
Ermao Cai, Da-Cheng Juan, Dimitrios Stamoulis et al.
A Covariance Matrix Adaptation Evolution Strategy for Direct Policy Search in Reproducing Kernel Hilbert Space
Ngo Anh Vien, Viet-Hung Dang, TaeChoong Chung
Attentive Path Combination for Knowledge Graph Completion
Xiaotian Jiang, Quan Wang, Baoyuan Qi et al.
Radical-level Ideograph Encoder for RNN-based Sentiment Analysis of Chinese and Japanese
Yuanzhi Ke, Masafumi Hagiwara
On the Flatness of Loss Surface for Two-layered ReLU Networks
Jiezhang Cao, Qingyao Wu, Yuguang Yan et al.
Nested LSTMs
Joel Ruben Antony Moniz, David Krueger
Multi-Task Structured Prediction for Entity Analysis: Search-Based Learning Algorithms
Chao Ma, Janardhan Rao Doppa, Prasad Tadepalli et al.
Learning RBM with a DC programming Approach
Vidyadhar Upadhya, P. S. Sastry
Pyramid Person Matching Network for Person Re-identification
Chaojie Mao, Yingming Li, Zhongfei Zhang et al.
Learning Deep Semantic Embeddings for Cross-Modal Retrieval
Cuicui Kang, Shengcai Liao, Zhen Li et al.
A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings
Sami Remes, Markus Heinonen, Samuel Kaski
Accumulated Gradient Normalization
Joeri R. Hermans, Gerasimos Spanakis, Rico Möckel
A Word Embeddings Informed Focused Topic Model
He Zhao, Lan Du, Wray Buntine
Magnitude-Preserving Ranking for Structured Outputs
Céline Brouard, Eric Bach, Sebastian Böcker et al.
Semi-supervised Convolutional Neural Networks for Identifying Wi-Fi Interference Sources
Krista Longi, Teemu Pulkkinen, Arto Klami
Whitening-Free Least-Squares Non-Gaussian Component Analysis
Hiroaki Shiino, Hiroaki Sasaki, Gang Niu et al.
Computer Assisted Composition with Recurrent Neural Networks
Christian Walder, Dongwoo Kim
One Class Splitting Criteria for Random Forests
Nicolas Goix, Nicolas Drougard, Romain Brault et al.
Recognizing Art Style Automatically in Painting with Deep Learning
Adrian Lecoutre, Benjamin Negrevergne, Florian Yger
Using Deep Neural Networks to Automate Large Scale Statistical Analysis for Big Data Applications
Rongrong Zhang, Wei Deng, Michael Yu Zhu
Scale-Invariant Recognition by Weight-Shared CNNs in Parallel
Ryo Takahashi, Takashi Matsubara, Kuniaki Uehara
Regret for Expected Improvement over the Best-Observed Value and Stopping Condition
Vu Nguyen, Sunil Gupta, Santu Rana et al.
Deep Competitive Pathway Networks
Jia-Ren Chang, Yong-Sheng Chen