We focus on solving integer linear programs, and ground our approach in the large neighborhood search (LNS) paradigm, which iteratively chooses a subset of variables to optimize while leaving the remainder fixed. The appeal of LNS is that it can easily use any existing solver as a subroutine, and thus can inherit the benefits of carefully engineered heuristic or complete approaches and their software implementations.

There has been an increased interest in discovering heuristics for combinatorial problems on graphs through machine learning. While existing techniques have primarily focused onobtaining high-quality solutions, scalability tobillion-sized graphs has not been adequately addressed.

Inthesenetworks,attentionalignsencoderand decoder states and isoften used for visualizing network behavior.

In this section, we ablate the kernel size used to train DISCO on ImageNet. TableIshowsthats=3 achieves the best performance, which degrades fors = 5 by a significant margin (3.26%). This is consistent with the well known complexity of synthesizing images withglobalmodels, suchasGANs. For a single ImageNet image of size 224, STL requires 23.71 seconds while DISCO (K=1) only requires0.027. In this section, we list the url links that are used for training and evaluating DISCO.

The problem of adversarial defenses for image classification, where the goal is to robustify a classifier against adversarial examples, is considered.

We first take a theoretical approach to analyzing debate and provide a framework through which debate can be mathematically examined. Building on this framework, we provide several theoretical results for multi-agent debate.

As a crucial step for analyzing human actions, hand-object interaction understanding is researchworthy in a broad range of applications related to virtual or augmented reality. Current works largely focus on hand-object pose estimation (HOPE) [16, 19, 21], which aims to capture the pose configuration of the given hand-object image.