Traditional landscape analysis of deep neural networks aims to show that no suboptimal local minima exist in some appropriate sense. From this, one may be tempted to conclude that descent algorithm…
Deep learning has been the engine powering many successes of data science. However, the deep neural network (DNN), as the basic model of deep learning, is often excessively over-parameterized, caus…
In this work, we study a generic class of decentralized algorithms in which agents jointly optimize the nonconvex objective function , while only communicating with their neighbors. This class of…
Does a large width eliminate all suboptimal local minima for neural nets? An affirmative answer was given by a classic result published in 1995 for one-hidden-layer wide neural nets with a sigmoid …
Unverifiable messages abound on the Internet. Why do people share messages they cannot verify? This study develops an in-depth understanding of how messages containing unverifiable product informat…
Time-series forecasting is one of the most active research topics in artificial intelligence. It has the power to bring light to problems in several areas of knowledge, such as epidemiological stud…
Large-scale and multidimensional spatiotemporal data sets are becoming ubiquitous in many real-world applications such as monitoring urban traffic and air quality. Making predictions on these time …
In this paper, we propose a novel system named Disp R-CNN for 3D object detection from stereo images. Many recent works solve this problem by first recovering point clouds with disparity estimation…
The doubly nonnegative (DNN) cone, being the set of all positive semidefinite matrices whose elements are nonnegative, is a popular approximation of the computationally intractable completely posit…
Natural Language Video Localization (NLVL) aims to locate a target moment from an untrimmed video that semantically corresponds to a text query. Existing approaches mainly solve the NLVL problem fr…