Papers
UZH@CRAFT-ST: a Sequence-labeling Approach to Concept Recognition
Lenz Furrer, Joseph Cornelius, Fabio Rinaldi
VAE-Based Regularization for Deep Speaker Embedding
Yang Zhang, Lantian Li, Dong Wang
VAEGAN: A Collaborative Filtering Framework based on Adversarial Variational Autoencoders
Xianwen Yu, Xiaoning Zhang, Yang Cao et al.
Validating Causal Inference Models via Influence Functions
Ahmed Alaa, Mihaela Van Der Schaar
Validation of Growing Knowledge Graphs by Abductive Text Evidences
Jianfeng Du, Jeff Z. Pan, Sylvia Wang et al.
Validation of the Non-Intrusive Codebook-Based Short Time Objective Intelligibility Metric for Processed Speech
Charlotte Sørensen, Jesper B. Boldt, Mads G. Christensen
Value-based Search in Execution Space for Mapping Instructions to Programs
Dor Muhlgay, Jonathan Herzig, Jonathan Berant
Value Function in Frequency Domain and the Characteristic Value Iteration Algorithm
Amir-massoud Farahmand
Value Function Transfer for Deep Multi-Agent Reinforcement Learning Based on N-Step Returns
Yong Liu, Yujing Hu, Yang Gao et al.
Value Iteration Networks on Multiple Levels of Abstraction
Daniel Schleich, Tobias Klamt, Sven Behnke
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning
Chao Qu, Shie Mannor, Huan Xu et al.
Value Propagation Networks
Nantas Nardelli, Gabriel Synnaeve, Zeming Lin et al.
Variable beam search for generative neural parsing and its relevance for the analysis of neuro-imaging signal
Benoit Crabbé, Murielle Fabre, Christophe Pallier
Variable beam search for generative neural parsing and its relevance for the analysis of neuro-imaging signal
Benoit Crabbé, Murielle Fabre, Christophe Pallier
Variable Rate Deep Image Compression With a Conditional Autoencoder
Yoojin Choi, Mostafa El-Khamy, Jungwon Lee
Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution
Topi Paananen, Juho Piironen, Michael Riis Andersen et al.
Variance-based Regularization with Convex Objectives
John Duchi, Hongseok Namkoong
Variance Networks: When Expectation Does Not Meet Your Expectations
Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha et al.
Variance of Average Surprisal: A Better Predictor for Quality of Grammar from Unsupervised PCFG Induction
Lifeng Jin, William Schuler
Variance Reduced Policy Evaluation with Smooth Function Approximation
Hoi-To Wai, Mingyi Hong, Zhuoran Yang et al.
Variance Reduction for Matrix Games
Yair Carmon, Yujia Jin, Aaron Sidford et al.
Variance Reduction for Reinforcement Learning in Input-Driven Environments
Hongzi Mao, Shaileshh Bojja Venkatakrishnan, Malte Schwarzkopf et al.
Variance Reduction in Bipartite Experiments through Correlation Clustering
Jean Pouget-Abadie, Kevin Aydin, Warren Schudy et al.
Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games Using Baselines
Martin Schmid, Neil Burch, Marc Lanctot et al.
Variance reduction properties of the reparameterization trick
Ming Xu, Matias Quiroz, Robert Kohn et al.