5 accepted papers
A good teacher can adjust the curriculum based on students' learning history. By analogy, in this paper, we study the dynamics of a deep neural network's (DNN) performance on individual samples during its learning process. The observed properties allow us to develop an adaptive curriculum that leads…
We are motivated by large scale submodular optimization problems, where standard algorithms, which treat the submodular functions in the value oracle model, do not scale. In this paper, we present a new model called the pre-computational complexity model, along with a unifying memoization based fram…
In this paper, we investigate a class of submodular problems which in general are very hard. These include minimizing a submodular cost function under combinatorial constraints, which include cuts, matchings, paths, etc., optimizing a submodular function under submodular cover and submodular knapsac…
We address the problem of minimizing non-submodular functions where the supermodularity is restricted to tree-structured pairwise terms. We are motivated by several real world applications, which require submodularity along with structured supermodularity, and this forms a rich class of expressive m…
We introduce a class of discrete point processes that we call the \emphSubmodular Point Processes (SPPs). These processes are characterized via a submodular (or supermodular) function, and naturally model notions of \emphinformation, coverage and \emphdiversity, as well as \emphcooperation. Unlike…