Asia
(Probably) Concave Graph Matching
From acomputational point of view,this problem is equivalent to the quadratic assignment problem, and as such is an NP-hard problem (Burkard et al., 1998).
Tight Bounds for Collaborative PAC Learning via Multiplicative Weights
Jiecao Chen, Qin Zhang, Yuan Zhou
Neural Information Processing Systems http://nips.cc/
FastSpeech: Fast, Robust and Controllable Text to Speech
Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu
Prominent methods (e.g., Tacotron 2)usuallyfirst generate mel-spectrogram from text, and then synthesize speech from themel-spectrogram using vocoder such as WaveNet. Compared with traditionalconcatenative and statistical parametric approaches, neural network based end-to-end models suffer from slow inference speed, and the synthesized speech isusually not robust (i.e., some words are skipped or repeated) and lack of con-trollability (voice speed or prosody control).
Efficient Stochastic Gradient Hard Thresholding
Pan Zhou, Xiaotong Yuan, Jiashi Feng
Neural Information Processing Systems http://nips.cc/
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
Wenzheng Chen, Huan Ling, Jun Gao, Edward Smith, Jaakko Lehtinen, Alec Jacobson, Sanja Fidler
In this paper, we presentDIB-R, a differentiable rendering framework which allows gradients to be analytically computed for allpixels inanimage.
Deep Multimodal Multilinear Fusion with High-order Polynomial Pooling
Ming Hou, Jiajia Tang, Jianhai Zhang, Wanzeng Kong, Qibin Zhao
More importantly, simply fusing features all at once ignores the complex local intercorrelations, leading to the deterioration of prediction. In this work, we first propose a polynomial tensor pooling (PTP) block for integrating multimodal features by considering high-order moments, followed by a tensorized fully connected layer. Treating PTP as a building block, we further establish a hierarchical polynomial fusion network (HPFN) to recursively transmit local correlations into global ones.
