Papers
Trading robust representations for sample complexity through self-supervised visual experience
Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos
Traffic Light Scheduling, Value of Time, and Incentives
Argyrios Deligkas, Erez Karpas, Ron Lavi et al.
Trainable Calibration Measures for Neural Networks from Kernel Mean Embeddings
Aviral Kumar, Sunita Sarawagi, Ujjwal Jain
Training and Inference with Integers in Deep Neural Networks
Shuang Wu, Guoqi Li, Feng Chen et al.
Training and Prediction Data Discrepancies: Challenges of Text Classification with Noisy, Historical Data
R. Andrew Kreek, Emilia Apostolova
Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data
Michael A. Hedderich, Dietrich Klakow
Training a Ranking Function for Open-Domain Question Answering
Phu Mon Htut, Samuel Bowman, Kyunghyun Cho
Training Augmentation with Adversarial Examples for Robust Speech Recognition
Sining Sun, Ching-Feng Yeh, Mari Ostendorf et al.
Training Binary Weight Networks via Semi-Binary Decomposition
Qinghao Hu, Gang Li, Peisong Wang et al.
Training Classifiers with Natural Language Explanations
Braden Hancock, Paroma Varma, Stephanie Wang et al.
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
Kimin Lee, Honglak Lee, Kibok Lee et al.
Training Deeper Neural Machine Translation Models with Transparent Attention
Ankur Bapna, Mia Chen, Orhan Firat et al.
Training deep learning based denoisers without ground truth data
Shakarim Soltanayev, Se Young Chun
Training Deep Models Faster with Robust, Approximate Importance Sampling
Tyler B Johnson, Carlos Guestrin
Training Deep Neural Networks with 8-bit Floating Point Numbers
Naigang Wang, Jungwook Choi, Daniel Brand et al.
Training DNNs with Hybrid Block Floating Point
Mario Drumond, Tao LIN, Martin Jaggi et al.
Training for Diversity in Image Paragraph Captioning
Luke Melas-Kyriazi, Alexander Rush, George Han
Training GANs with Optimism
Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis et al.
Training Gaussian Mixture Models at Scale via Coresets
Mario Lucic, Matthew Faulkner, Andreas Krause et al.
TRAINING GENERATIVE ADVERSARIAL NETWORKS VIA PRIMAL-DUAL SUBGRADIENT METHODS: A LAGRANGIAN PERSPECTIVE ON GAN
Xu Chen, Jiang Wang, Hao Ge
Training Millions of Personalized Dialogue Agents
Pierre-Emmanuel Mazaré, Samuel Humeau, Martin Raison et al.
Training Neural Machines with Trace-Based Supervision
Matthew Mirman, Dimitar Dimitrov, Pavle Djordjevic et al.
Training Neural Networks Using Features Replay
Zhouyuan Huo, Bin Gu, Heng Huang
Training Recurrent Neural Network through Moment Matching for NLP Applications
Yue Deng, Yilin Shen, KaWai Chen et al.
Training Structured Prediction Energy Networks with Indirect Supervision
Amirmohammad Rooshenas, Aishwarya Kamath, Andrew McCallum