Filters








934,132 Hits in 3.6 sec

Learning Representations by Humans, for Humans [article]

Sophie Hilgard, Nir Rosenfeld, Mahzarin R. Banaji, Jack Cao, David C. Parkes
2021 arXiv   pre-print
Inspired by the success of representation learning in improving performance of machine predictors, our framework learns human-facing representations optimized for human performance.  ...  This "Mind Composed with Machine" framework incorporates a human decision-making model directly into the representation learning paradigm and is trained with a novel human-in-the-loop training procedure  ...  By solving (4), we learn representations that promote good decisions by the human user. See Figure 1 (left). Training procedure, and human proxy.  ... 
arXiv:1905.12686v4 fatcat:3rto3t3qbvegjevfr6kvyqi6ci

Towards Disentangled Representations for Human Retargeting by Multi-view Learning [article]

Chao Yang, Xiaofeng Liu, Qingming Tang, C.-C. Jay Kuo
2019 arXiv   pre-print
We study the problem of learning disentangled representations for data across multiple domains and its applications in human retargeting.  ...  better human retargeting results.  ...  By this way, for the unseen images without domain labels in the future, our inference is still capable to learn representations. 2.  ... 
arXiv:1912.06265v1 fatcat:o2ah4eih6bahhdkn37iygcmjxy

Engineering Deep Representations for Modeling Aesthetic Perception [article]

Yanxiang Chen, Yuxing Hu, Luming Zhang, Ping Li, Chao Zhang
2019 arXiv   pre-print
sunset) for each image.  ...  To remedy these problems, we develop a deep architecture to learn aesthetically-relevant visual attributes from Flickr1, which are localized by multiple textual attributes in a weakly-supervised setting  ...  Aesthlet-normalized CNN for Aesthetic Modeling We integrate aesthlets into a deep architecture which learns patch-normalized representations to model visual attributes.  ... 
arXiv:1605.07699v2 fatcat:keft3kn7bncxxkblqhsu3eww54

Contextualized and Generalized Sentence Representations by Contrastive Self-Supervised Learning: A Case Study on Discourse Relation Analysis

Hirokazu Kiyomaru, Sadao Kurohashi
2021 Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies   unpublished
We propose a method to learn contextualized and generalized sentence representations using contrastive self-supervised learning.  ...  Simultaneously, the model minimize the similarity between the latter representation and the representation of a random sentence with the same context.  ...  We propose a method to learn contextualized and generalized sentence representations by contrastive self-supervised learning (van den Oord et al., 2019; Chen et al., 2020) .  ... 
doi:10.18653/v1/2021.naacl-main.442 fatcat:vfqye6yk4nho3lur26p2iaqefi

Handcrafted vs. learned representations for human action recognition

Xiantong Zhen, Ling Shao, Stephen J. Maybank, Rama Chellappa
2016 Image and Vision Computing  
Acknowledgement We would like to thank all the authors for their contributions to this special issue, and reviewers for their timely and insightful reviews. We thank Professors J.-M. Frahm and M.  ...  Pantic, Editor-in-Chief of the Image and Vision Computing Journal for giving us the opportunity to guest edit this special issue, and the Elsevier staff, Yanhong Zhai for her great support to this special  ...  Deep learning While handcrafted features are still widely used for human action representation, deep learning based feature representation has also started to be explored for human action recognition.  ... 
doi:10.1016/j.imavis.2016.10.002 fatcat:j4c2txj3g5glra67qvzab5mmke

Human-Centered Prior-Guided and Task-Dependent Multi-Task Representation Learning for Action Recognition Pre-Training [article]

Guanhong Wang, Keyu Lu, Yang Zhou, Zhanhao He, Gaoang Wang
2022 arXiv   pre-print
between multiple tasks by using task-dependent representations.  ...  However, two main issues remain for existing pre-training methods: 1) the learned representation is neutral and not informative for a specific task; 2) multi-task learning-based pre-training sometimes  ...  This work was supported by the National Natural Science Foundation of China under Grant 62106219.  ... 
arXiv:2204.12729v1 fatcat:geuzodfiqzhefbhnd3l6twdwzm

Learning from Observations Using a Single Video Demonstration and Human Feedback [article]

Sunil Gandhi, Tim Oates, Tinoosh Mohsenin, Nicholas Waytowich
2019 arXiv   pre-print
In this paper, we present a method for learning from video demonstrations by using human feedback to construct a mapping between the standard representation of the agent and the visual representation of  ...  In this way, we leverage the advantages of both these representations, i.e., we learn the policy using standard state representations, but are able to specify the expected behavior using video demonstration  ...  In this paper, we present a method for learning from video demonstrations by using human feedback to construct a mapping between the standard representation of the agent and the visual representation of  ... 
arXiv:1909.13392v1 fatcat:ij2liqtpt5b7hjosbdcic5npni

Evaluating (and improving) the correspondence between deep neural networks and human representations [article]

Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths
2018 arXiv   pre-print
These networks learn representations of real-world stimuli that can potentially be leveraged to capture psychological representations.  ...  by people.  ...  Figure 8 . 8 Average human categorization performance for each of five learning blocks.  ... 
arXiv:1706.02417v3 fatcat:af3j4pm33jaa5hyvthoqigc7ly

Adapting Deep Network Features to Capture Psychological Representations: An Abridged Report

Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
In this abridged report of Peterson et al. [2016], we examine the relationship between the image representations learned by these networks and those of humans.  ...  We find that deep features learned in service of object classification account for a significant amount of the variance in human similarity judgments for a set of animal images.  ...  Acknowledgments This work was supported by grant number FA9550-13-1-0170 from the Air Force Office of Scientific Research. We thank Alex Huth for help with image selection.  ... 
doi:10.24963/ijcai.2017/697 dblp:conf/ijcai/PetersonAG17 fatcat:zmquysonwjf6ndjf7g2eqbpyxu

Editorial

Amir Aly, Odest Chadwicke Jenkins, Selma Sabanovic
2019 ACM Transactions on Human-Robot Interaction (THRI)  
REPRESENTATION LEARNING FOR HUMAN AND ROBOT COGNITION Intelligent robots are rapidly moving to the center of human environments.  ...  This issue of ACM THRI would not be possible without the substantial efforts given by the authors, reviewers, and editorial team for Representation Learning.  ... 
doi:10.1145/3366621 fatcat:esvdia7bgjf5fgr2utgwwv6ct4

Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos [article]

Haoyu Xiong, Quanzhou Li, Yun-Chun Chen, Homanga Bharadhwaj, Samarth Sinha, Animesh Garg
2021 arXiv   pre-print
In this paper, we present Learning by Watching (LbW), an algorithmic framework for policy learning through imitation from a single video specifying the task.  ...  The detected keypoints form a structured representation that contains semantically meaningful information and can be used directly for computing reward and policy learning.  ...  To achieve physical imitation from human videos, our key intuition is to learn a structured representation captured by keypoints that provides semantically meaningful information for robot manipulation  ... 
arXiv:2101.07241v2 fatcat:nvqthzyfnfhzhhsx42o3yi65wu

What does the mind learn? A comparison of human and machine learning representations

Jake Spicer, Adam N Sanborn
2019 Current Opinion in Neurobiology  
We present a brief review of modern machine learning techniques and their use in models of human mental representations, detailing three notable branches: spatial methods, logical methods and artificial  ...  Each of these branches contains an extensive set of systems, and demonstrate accurate emulations of human learning of categories, concepts and language, despite substantial differences in operation.  ...  Acknowledgements This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declarations of interest: none.  ... 
doi:10.1016/j.conb.2019.02.004 pmid:30870615 fatcat:rjt647hyyzdotmdpiw7cfdo2ke

Improving Human Motion Prediction Through Continual Learning [article]

Mohammad Samin Yasar, Tariq Iqbal
2021 arXiv   pre-print
These variables make it challenging for learning algorithms to obtain a general representation that is robust to the diverse spatio-temporal patterns of human motion.  ...  Human motion prediction is an essential component for enabling closer human-robot collaboration. The task of accurately predicting human motion is non-trivial.  ...  For human motion prediction, this can be posed as a continual learning problem whereby a motion prediction model acquires prior knowledge by observing a large range of human activities.  ... 
arXiv:2107.00544v1 fatcat:fbtndn5tqrat7fbbe6mozgb22e

Adapting Deep Network Features to Capture Psychological Representations [article]

Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths
2016 arXiv   pre-print
We examine the relationship between the representations learned by these networks and human psychological representations recovered from similarity judgments.  ...  We find that deep features learned in service of object classification account for a significant amount of the variance in human similarity judgments for a set of animal images.  ...  This work was supported by grant number FA9550-13-1-0170 from the Air Force Office of Scientific Research. We thank Alex Huth for help with image selection.  ... 
arXiv:1608.02164v1 fatcat:y4ereyfhrzhldpeia63s7gp6ky

Predicate learning in neural systems: Discovering latent generative structures [article]

Andrea E. Martin, Leonidas A. A. Doumas
2018 arXiv   pre-print
In cognitive science and cognitive neuroscience, models that infer higher-order structures from sensory or first-order representations have been proposed to account for the complexity and flexibility of  ...  To answer this question, we explain how a system can learn latent representational structures (i.e., predicates) from experience with wholly unstructured data.  ...  Three requirements for structured representations It is important to be clear about what we mean by representations and representational systems.  ... 
arXiv:1810.01127v1 fatcat:3ueslp3wqjcdjb4gajwptgbayy
« Previous Showing results 1 — 15 out of 934,132 results