Deep Learning
AMOS: ALarge-ScaleAbdominalMulti-Organ BenchmarkforVersatileMedicalImageSegmentation
The significant heterogeneity poses higher requirements foralgorithm robustness.
Neural Voice Cloning with a Few Samples
Sercan Arik, Jitong Chen, Kainan Peng, Wei Ping, Yanqi Zhou
Neural Information Processing Systems http://nips.cc/
QATCH: Benchmarking SQL-centric tasks with Table Representation Learning Models on Y our Data
We present QATCH (Query-Aided TRL Checklist), a toolbox to highlight TRL models' strengths and weaknesses on relational tables unseen at training time. For an input table, QATCH automatically generates a testing checklist tailored to QA and SP .
Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods
Kevin Liang, Guoyin Wang, Yitong Li, Ricardo Henao, Lawrence Carin
Neural Information Processing Systems http://nips.cc/
Adversarial Fisher Vectors for Unsupervised Representation Learning
Shuangfei Zhai, Walter Talbott, Carlos Guestrin, Joshua Susskind
Weexamine GenerativeAdversarial Networks(GANs) through thelensofdeep Energy Based Models (EBMs), with the goal of exploiting the density model that follows from this formulation.
