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Sep 16, 2019
By 7D
Machine Learning

Deep Reinforcement Learning for Task-driven Discovery of Incomplete …

Complex networks are often either too large for full exploration, partially accessible or partially observed. Downstream learning tasks on incomplete…
Sep 16, 2019
By qid
Computer Vision

Learning Spatial Awareness to Improve Crowd Counting

The aim of crowd counting is to estimate the number of people in images by leveraging the annotation of center positions for pedestrians' heads. Prom…
Sep 15, 2019
Computer Vision
Machine Learning

Scaling Object Detection by Transferring Classification Weights

Large scale object detection datasets are constantly increasing their size in terms of the number of classes and annotations count. Yet, the number o…
Sep 14, 2019
Robotics
Machine Learning

Selfie Drone Stick: A Natural Interface for Quadcopter Photography

A physical selfie stick extends the user's reach, enabling the creation of personal photos that include more of the background scene. Conversely a qu…
Sep 13, 2019
By shiyue
Computation and Language
Artificial Intelligence
Machine Learning

Addressing Semantic Drift in Question Generation for Semi-Supervised …

Text-based Question Generation (QG) aims at generating natural and relevant questions that can be answered by a given answer in some context. Existin…
Sep 13, 2019
Computation and Language

simple but effective techniques to reduce biases

There have been several studies recently showing that strong natural language inference (NLI) models are prone to relying on unwanted dataset biases,…
Sep 13, 2019
Machine Learning
Computer Vision

Flow Models for Arbitrary Conditional Likelihoods

Understanding the dependencies among features of a dataset is at the core of most unsupervised learning tasks. However, a majority of generative mode…
Sep 13, 2019
Machine Learning
Distributed
Parallel
and Cluster Computing

DL2: A Deep Learning-driven Scheduler for Deep Learning Clusters

More and more companies have deployed machine learning (ML) clusters, where deep learning (DL) models are trained for providing various AI-driven ser…
Sep 13, 2019
By kaili
Computer Vision

Rethinking Zero-Shot Learning: A Conditional Visual Classification …

Zero-shot learning (ZSL) aims to recognize instances of unseen classes solely based on the semantic descriptions of the classes. Existing algorithms …
Sep 12, 2019
Computer Vision
Machine Learning

Transferable Adversarial Robustness using Adversarially Trained Aut…

Machine learning has proven to be an extremely useful tool for solving complex problems in many application domains. This prevalence makes it an attr…
Sep 12, 2019
Image and Video Processing
Computer Vision

Perceptual Image Anomaly Detection

We present a novel method for image anomaly detection, where algorithms that use samples drawn from some distribution of "normal" data, aim to detect…
Sep 12, 2019
Computation and Language
Machine Learning

Analyzing machine-learned representations: A natural language case st…

As modern deep networks become more complex, and get closer to human-like capabilities in certain domains, the question arises of how the representat…
Sep 12, 2019
Computation and Language

We presented the Schema-Guided Dialogue dataset to encourage scalable modeling approaches for virtual assistants. We also introduced the schema-guided paradigm for task-oriented dialogue that simplifies the integration of new services and APIs with …

Towards Scalable Multi-domain Conversational Agents: The Schema-Guide…

Virtual assistants such as Google Assistant, Alexa and Siri provide a conversational interface to a large number of services and APIs spanning multip…
Sep 12, 2019
Machine Learning
Optimization and Control

Communication-Efficient Distributed Optimization in Networks with G…

There is a growing interest in large-scale machine learning and optimization over decentralized networks, e.g. in the context of multi-agent learning…
Sep 12, 2019
Machine Learning
Computational Complexity

On the Hardness of Robust Classification

It is becoming increasingly important to understand the vulnerability of machine learning models to adversarial attacks. In this paper we study the f…
Sep 12, 2019
By mernst
Computer Vision
Machine Learning

Recurrent Connectivity Aids Recognition of Partly Occluded Objects

Feedforward convolutional neural networks are the prevalent model of core object recognition. For challenging conditions, such as occlusion, neurosci…
Sep 12, 2019
Computer Vision

I-SAFE: Instant Suspicious Activity identiFication at the Edge using …

Urban imagery usually serves as forensic analysis and by design is available for incident mitigation. As more imagery collected, it is harder to narr…
Sep 12, 2019
Image and Video Processing
Computer Vision
Medical Physics

PILOT: Physics-Informed Learned Optimal Trajectories for Accelerated …

Magnetic Resonance Imaging (MRI) has long been considered to be among "the gold standards" of diagnostic medical imaging. The long acquisition times,…
Sep 12, 2019
Computer Vision
Graphics
Machine Learning

CvxNets: Learnable Convex Decomposition

Any solid object can be decomposed into a collection of convex polytopes (in short, convexes). When a small number of convexes are used, such a decom…
Sep 12, 2019
Computer Vision
Robotics

VeREFINE: Integrating Object Pose Verification with Iterative Physi…

Precise object pose estimation for robotics applications and augmented reality relies on final refinement and verification steps. However, interactio…
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