We propose combinatorial cascading bandits, a class of partial monitoring problems where at each step a learning agent chooses a tuple of ground item…

We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images. We consider document seman…

Modern convolutional networks are not shift-invariant, as small input shifts or translations can cause drastic changes in the output. Commonly used d…

Parsing urban scene images benefits many applications, especially self-driving. Most of the current solutions employ generic image parsing models tha…

Attention networks show promise for both vision and language tasks, by emphasizing relationships between constituent elements through appropriate wei…

We develop an optimization model and corresponding algorithm for the management of a demand-side platform (DSP), whereby the DSP aims to maximize its…

Graph-structured data arise naturally in many different application domains. By representing data as graphs, we can capture entities (i.e., nodes) as…

Unsupervised clustering is one of the most fundamental challenges in machine learning. A popular hypothesis is that data are generated from a union o…

We describe a new method called t-ETE for finding a low-dimensional embedding of a set of objects in Euclidean space. We formulate the embedding prob…

We are the first to formalize the setting of stochastic online learning with probabilistic feedback graph. We derive asymptotic lower bounds for both one-step and cascade cases. The regret bounds of our designed algorithms match the lower bounds wit…

We consider a problem of stochastic online learning with general probabilistic graph feedback. Two cases are covered. (a) The one-step case where for…

We study the stochastic online problem of learning to influence in a social network with semi-bandit feedback, where we observe how users influence e…

As font is one of the core design concepts, automatic font identification and similar font suggestion from an image or photo has been on the wish lis…

In this paper, we propose a novel method for a sentence-level answer-selection task that is one of the fundamental problems in natural language proce…

Classifying large-scale image data into object categories is an important problem that has received increasing research attention. Given the huge amo…

This paper presents the first study on forecasting human dynamics from static images. The problem is to input a single RGB image and generate a seque…

Thompson sampling (TS) is a class of algorithms for sequential decision-making, which requires maintaining a posterior distribution over a model. How…

We aim to model the top-down attention of a Convolutional Neural Network (CNN) classifier for generating task-specific attention maps. Inspired by a …

Learning long-term spatial-temporal features are critical for many video analysis tasks. However, existing video segmentation methods predominantly r…

Mapper is an algorithm that summarizes the topological information contained in a dataset and provides an insightful visualization. It takes as input…

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