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Hot Papers 2020-07-21

1. iNNk: A Multi-Player Game to Deceive a Neural Network

Jennifer Villareale, Ana Acosta-Ruiz, Samuel Arcaro, Thomas Fox, Evan Freed, Robert Gray, Mathias Löwe, Panote Nuchprayoon, Aleksanteri Sladek, Rush Weigelt, Yifu Li, Sebastian Risi, Jichen Zhu

  • retweets: 39, favorites: 141 (07/22/2020 10:01:27)
  • links: abs | pdf
  • cs.HC | cs.AI

This paper presents \textit{iNNK}, a multiplayer drawing game where human players team up against an NN. The players need to successfully communicate a secret code word to each other through drawings, without being deciphered by the NN. With this game, we aim to foster a playful environment where players can, in a small way, go from passive consumers of NN applications to creative thinkers and critical challengers.

2. An Overview of Natural Language State Representation for Reinforcement Learning

Brielen Madureira, David Schlangen

  • retweets: 13, favorites: 51 (07/22/2020 10:01:27)
  • links: abs | pdf
  • cs.CL

A suitable state representation is a fundamental part of the learning process in Reinforcement Learning. In various tasks, the state can either be described by natural language or be natural language itself. This survey outlines the strategies used in the literature to build natural language state representations. We appeal for more linguistically interpretable and grounded representations, careful justification of design decisions and evaluation of the effectiveness of different approaches.

3. ContactPose: A Dataset of Grasps with Object Contact and Hand Pose

Samarth Brahmbhatt, Chengcheng Tang, Christopher D. Twigg, Charles C. Kemp, James Hays

  • retweets: 7, favorites: 47 (07/22/2020 10:01:27)
  • links: abs | pdf
  • cs.CV

Grasping is natural for humans. However, it involves complex hand configurations and soft tissue deformation that can result in complicated regions of contact between the hand and the object. Understanding and modeling this contact can potentially improve hand models, AR/VR experiences, and robotic grasping. Yet, we currently lack datasets of hand-object contact paired with other data modalities, which is crucial for developing and evaluating contact modeling techniques. We introduce ContactPose, the first dataset of hand-object contact paired with hand pose, object pose, and RGB-D images. ContactPose has 2306 unique grasps of 25 household objects grasped with 2 functional intents by 50 participants, and more than 2.9 M RGB-D grasp images. Analysis of ContactPose data reveals interesting relationships between hand pose and contact. We use this data to rigorously evaluate various data representations, heuristics from the literature, and learning methods for contact modeling. Data, code, and trained models are available at https://contactpose.cc.gatech.edu.

4. Temporal Pointwise Convolutional Networks for Length of Stay Prediction in the Intensive Care Unit

Emma Rocheteau, Pietro Liò, Stephanie Hyland

The pressure of ever-increasing patient demand and budget restrictions make hospital bed management a daily challenge for clinical staff. Most critical is the efficient allocation of resource-heavy Intensive Care Unit (ICU) beds to the patients who need life support. Central to solving this problem is knowing for how long the current set of ICU patients are likely to stay in the unit. In this work, we propose a new deep learning model based on the combination of temporal convolution and pointwise (1x1) convolution, to solve the length of stay prediction task on the eICU critical care dataset. The model - which we refer to as Temporal Pointwise Convolution (TPC) - is specifically designed to mitigate for common challenges with Electronic Health Records, such as skewness, irregular sampling and missing data. In doing so, we have achieved significant performance benefits of 18-51% (metric dependent) over the commonly used Long-Short Term Memory (LSTM) network, and the multi-head self-attention network known as the Transformer.

Published 22 Jul 2020

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