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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.07542">arXiv:2206.07542</a> <span> [<a href="https://arxiv.org/pdf/2206.07542">pdf</a>, <a href="https://arxiv.org/format/2206.07542">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> A Deep Generative Model of Neonatal Cortical Surface Development </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Fawaz%2C+A">Abdulah Fawaz</a>, <a href="/search/cs?searchtype=author&query=Williams%2C+L+Z">Logan Z. Williams</a>, <a href="/search/cs?searchtype=author&query=Edwards%2C+A+D">A. David Edwards</a>, <a href="/search/cs?searchtype=author&query=Robinson%2C+E">Emma Robinson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2206.07542v2-abstract-short" style="display: inline;"> The neonatal cortical surface is known to be affected by preterm birth, and the subsequent changes to cortical organisation have been associated with poorer neurodevelopmental outcomes. Deep Generative models have the potential to lead to clinically interpretable models of disease, but developing these on the cortical surface is challenging since established techniques for learning convolutional f… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.07542v2-abstract-full').style.display = 'inline'; document.getElementById('2206.07542v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.07542v2-abstract-full" style="display: none;"> The neonatal cortical surface is known to be affected by preterm birth, and the subsequent changes to cortical organisation have been associated with poorer neurodevelopmental outcomes. Deep Generative models have the potential to lead to clinically interpretable models of disease, but developing these on the cortical surface is challenging since established techniques for learning convolutional filters are inappropriate on non-flat topologies. To close this gap, we implement a surface-based CycleGAN using mixture model CNNs (MoNet) to translate sphericalised neonatal cortical surface features (curvature and T1w/T2w cortical myelin) between different stages of cortical maturity. Results show our method is able to reliably predict changes in individual patterns of cortical organisation at later stages of gestation, validated by comparison to longitudinal data; and translate appearance between preterm and term gestation (> 37 weeks gestation), validated through comparison with a trained term/preterm classifier. Simulated differences in cortical maturation are consistent with observations in the literature. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.07542v2-abstract-full').style.display = 'none'; document.getElementById('2206.07542v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.08239">arXiv:2205.08239</a> <span> [<a href="https://arxiv.org/pdf/2205.08239">pdf</a>, <a href="https://arxiv.org/format/2205.08239">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+L">Liu Li</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+Q">Qiang Ma</a>, <a href="/search/cs?searchtype=author&query=Sinclair%2C+M">Matthew Sinclair</a>, <a href="/search/cs?searchtype=author&query=Makropoulos%2C+A">Antonios Makropoulos</a>, <a href="/search/cs?searchtype=author&query=Hajnal%2C+J">Joseph Hajnal</a>, <a href="/search/cs?searchtype=author&query=Edwards%2C+A+D">A. David Edwards</a>, <a href="/search/cs?searchtype=author&query=Kainz%2C+B">Bernhard Kainz</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a>, <a href="/search/cs?searchtype=author&query=Alansary%2C+A">Amir Alansary</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2205.08239v1-abstract-short" style="display: inline;"> Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development. Accurate segmentation of the different brain tissues is a vital step in several brain analysis tasks, such as cortical surface reconstruction and tissue thickness measurements. Fetal MRI scans, however, are prone to motion artifacts that can affect the correctness of both manual and automati… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.08239v1-abstract-full').style.display = 'inline'; document.getElementById('2205.08239v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.08239v1-abstract-full" style="display: none;"> Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development. Accurate segmentation of the different brain tissues is a vital step in several brain analysis tasks, such as cortical surface reconstruction and tissue thickness measurements. Fetal MRI scans, however, are prone to motion artifacts that can affect the correctness of both manual and automatic segmentation techniques. In this paper, we propose a novel network structure that can simultaneously generate conditional atlases and predict brain tissue segmentation, called CAS-Net. The conditional atlases provide anatomical priors that can constrain the segmentation connectivity, despite the heterogeneity of intensity values caused by motion or partial volume effects. The proposed method is trained and evaluated on 253 subjects from the developing Human Connectome Project (dHCP). The results demonstrate that the proposed method can generate conditional age-specific atlas with sharp boundary and shape variance. It also segment multi-category brain tissues for fetal MRI with a high overall Dice similarity coefficient (DSC) of $85.2\%$ for the selected 9 tissue labels. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.08239v1-abstract-full').style.display = 'none'; document.getElementById('2205.08239v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2204.03408">arXiv:2204.03408</a> <span> [<a href="https://arxiv.org/pdf/2204.03408">pdf</a>, <a href="https://arxiv.org/format/2204.03408">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dahan%2C+S">Simon Dahan</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+H">Hao Xu</a>, <a href="/search/cs?searchtype=author&query=Williams%2C+L+Z+J">Logan Z. J. Williams</a>, <a href="/search/cs?searchtype=author&query=Fawaz%2C+A">Abdulah Fawaz</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+C">Chunhui Yang</a>, <a href="/search/cs?searchtype=author&query=Coalson%2C+T+S">Timothy S. Coalson</a>, <a href="/search/cs?searchtype=author&query=Williams%2C+M+C">Michelle C. Williams</a>, <a href="/search/cs?searchtype=author&query=Newby%2C+D+E">David E. Newby</a>, <a href="/search/cs?searchtype=author&query=Edwards%2C+A+D">A. David Edwards</a>, <a href="/search/cs?searchtype=author&query=Glasser%2C+M+F">Matthew F. Glasser</a>, <a href="/search/cs?searchtype=author&query=Young%2C+A+A">Alistair A. Young</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a>, <a href="/search/cs?searchtype=author&query=Robinson%2C+E+C">Emma C. Robinson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2204.03408v1-abstract-short" style="display: inline;"> Recent state-of-the-art performances of Vision Transformers (ViT) in computer vision tasks demonstrate that a general-purpose architecture, which implements long-range self-attention, could replace the local feature learning operations of convolutional neural networks. In this paper, we extend ViTs to surfaces by reformulating the task of surface learning as a sequence-to-sequence learning problem… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.03408v1-abstract-full').style.display = 'inline'; document.getElementById('2204.03408v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.03408v1-abstract-full" style="display: none;"> Recent state-of-the-art performances of Vision Transformers (ViT) in computer vision tasks demonstrate that a general-purpose architecture, which implements long-range self-attention, could replace the local feature learning operations of convolutional neural networks. In this paper, we extend ViTs to surfaces by reformulating the task of surface learning as a sequence-to-sequence learning problem, by proposing patching mechanisms for general surface meshes. Sequences of patches are then processed by a transformer encoder and used for classification or regression. We validate our method on a range of different biomedical surface domains and tasks: brain age prediction in the developing Human Connectome Project (dHCP), fluid intelligence prediction in the Human Connectome Project (HCP), and coronary artery calcium score classification using surfaces from the Scottish Computed Tomography of the Heart (SCOT-HEART) dataset, and investigate the impact of pretraining and data augmentation on model performance. Results suggest that Surface Vision Transformers (SiT) demonstrate consistent improvement over geometric deep learning methods for brain age and fluid intelligence prediction and achieve comparable performance on calcium score classification to standard metrics used in clinical practice. Furthermore, analysis of transformer attention maps offers clear and individualised predictions of the features driving each task. Code is available on Github: https://github.com/metrics-lab/surface-vision-transformers <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.03408v1-abstract-full').style.display = 'none'; document.getElementById('2204.03408v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 3 figures, Submitted to IEEE Transactions on Medical Imaging</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.16414">arXiv:2203.16414</a> <span> [<a href="https://arxiv.org/pdf/2203.16414">pdf</a>, <a href="https://arxiv.org/format/2203.16414">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Surface Vision Transformers: Attention-Based Modelling applied to Cortical Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dahan%2C+S">Simon Dahan</a>, <a href="/search/cs?searchtype=author&query=Fawaz%2C+A">Abdulah Fawaz</a>, <a href="/search/cs?searchtype=author&query=Williams%2C+L+Z+J">Logan Z. J. Williams</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+C">Chunhui Yang</a>, <a href="/search/cs?searchtype=author&query=Coalson%2C+T+S">Timothy S. Coalson</a>, <a href="/search/cs?searchtype=author&query=Glasser%2C+M+F">Matthew F. Glasser</a>, <a href="/search/cs?searchtype=author&query=Edwards%2C+A+D">A. David Edwards</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a>, <a href="/search/cs?searchtype=author&query=Robinson%2C+E+C">Emma C. Robinson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2203.16414v1-abstract-short" style="display: inline;"> The extension of convolutional neural networks (CNNs) to non-Euclidean geometries has led to multiple frameworks for studying manifolds. Many of those methods have shown design limitations resulting in poor modelling of long-range associations, as the generalisation of convolutions to irregular surfaces is non-trivial. Motivated by the success of attention-modelling in computer vision, we translat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.16414v1-abstract-full').style.display = 'inline'; document.getElementById('2203.16414v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.16414v1-abstract-full" style="display: none;"> The extension of convolutional neural networks (CNNs) to non-Euclidean geometries has led to multiple frameworks for studying manifolds. Many of those methods have shown design limitations resulting in poor modelling of long-range associations, as the generalisation of convolutions to irregular surfaces is non-trivial. Motivated by the success of attention-modelling in computer vision, we translate convolution-free vision transformer approaches to surface data, to introduce a domain-agnostic architecture to study any surface data projected onto a spherical manifold. Here, surface patching is achieved by representing spherical data as a sequence of triangular patches, extracted from a subdivided icosphere. A transformer model encodes the sequence of patches via successive multi-head self-attention layers while preserving the sequence resolution. We validate the performance of the proposed Surface Vision Transformer (SiT) on the task of phenotype regression from cortical surface metrics derived from the Developing Human Connectome Project (dHCP). Experiments show that the SiT generally outperforms surface CNNs, while performing comparably on registered and unregistered data. Analysis of transformer attention maps offers strong potential to characterise subtle cognitive developmental patterns. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.16414v1-abstract-full').style.display = 'none'; document.getElementById('2203.16414v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">22 pages, 6 figures, Accepted to MIDL 2022, OpenReview link https://openreview.net/forum?id=mpp843Bsf-</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Proceedings of Machine Learning Research. 172 (2022) 282-303 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2002.09505">arXiv:2002.09505</a> <span> [<a href="https://arxiv.org/pdf/2002.09505">pdf</a>, <a href="https://arxiv.org/format/2002.09505">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Estimating Q(s,s') with Deep Deterministic Dynamics Gradients </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Edwards%2C+A+D">Ashley D. Edwards</a>, <a href="/search/cs?searchtype=author&query=Sahni%2C+H">Himanshu Sahni</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+R">Rosanne Liu</a>, <a href="/search/cs?searchtype=author&query=Hung%2C+J">Jane Hung</a>, <a href="/search/cs?searchtype=author&query=Jain%2C+A">Ankit Jain</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+R">Rui Wang</a>, <a href="/search/cs?searchtype=author&query=Ecoffet%2C+A">Adrien Ecoffet</a>, <a href="/search/cs?searchtype=author&query=Miconi%2C+T">Thomas Miconi</a>, <a href="/search/cs?searchtype=author&query=Isbell%2C+C">Charles Isbell</a>, <a href="/search/cs?searchtype=author&query=Yosinski%2C+J">Jason Yosinski</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2002.09505v2-abstract-short" style="display: inline;"> In this paper, we introduce a novel form of value function, $Q(s, s')$, that expresses the utility of transitioning from a state $s$ to a neighboring state $s'$ and then acting optimally thereafter. In order to derive an optimal policy, we develop a forward dynamics model that learns to make next-state predictions that maximize this value. This formulation decouples actions from values while still… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.09505v2-abstract-full').style.display = 'inline'; document.getElementById('2002.09505v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.09505v2-abstract-full" style="display: none;"> In this paper, we introduce a novel form of value function, $Q(s, s')$, that expresses the utility of transitioning from a state $s$ to a neighboring state $s'$ and then acting optimally thereafter. In order to derive an optimal policy, we develop a forward dynamics model that learns to make next-state predictions that maximize this value. This formulation decouples actions from values while still learning off-policy. We highlight the benefits of this approach in terms of value function transfer, learning within redundant action spaces, and learning off-policy from state observations generated by sub-optimal or completely random policies. Code and videos are available at http://sites.google.com/view/qss-paper. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.09505v2-abstract-full').style.display = 'none'; document.getElementById('2002.09505v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted into ICML 2020</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1905.07861">arXiv:1905.07861</a> <span> [<a href="https://arxiv.org/pdf/1905.07861">pdf</a>, <a href="https://arxiv.org/format/1905.07861">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Perceptual Values from Observation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Edwards%2C+A+D">Ashley D. Edwards</a>, <a href="/search/cs?searchtype=author&query=Isbell%2C+C+L">Charles L. Isbell</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1905.07861v1-abstract-short" style="display: inline;"> Imitation by observation is an approach for learning from expert demonstrations that lack action information, such as videos. Recent approaches to this problem can be placed into two broad categories: training dynamics models that aim to predict the actions taken between states, and learning rewards or features for computing them for Reinforcement Learning (RL). In this paper, we introduce a novel… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.07861v1-abstract-full').style.display = 'inline'; document.getElementById('1905.07861v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1905.07861v1-abstract-full" style="display: none;"> Imitation by observation is an approach for learning from expert demonstrations that lack action information, such as videos. Recent approaches to this problem can be placed into two broad categories: training dynamics models that aim to predict the actions taken between states, and learning rewards or features for computing them for Reinforcement Learning (RL). In this paper, we introduce a novel approach that learns values, rather than rewards, directly from observations. We show that by using values, we can significantly speed up RL by removing the need to bootstrap action-values, as compared to sparse-reward specifications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.07861v1-abstract-full').style.display = 'none'; document.getElementById('1905.07861v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 May, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted into the Workshop on Self-Supervised Learning at ICML 2019</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1805.07914">arXiv:1805.07914</a> <span> [<a href="https://arxiv.org/pdf/1805.07914">pdf</a>, <a href="https://arxiv.org/format/1805.07914">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Imitating Latent Policies from Observation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Edwards%2C+A+D">Ashley D. Edwards</a>, <a href="/search/cs?searchtype=author&query=Sahni%2C+H">Himanshu Sahni</a>, <a href="/search/cs?searchtype=author&query=Schroecker%2C+Y">Yannick Schroecker</a>, <a href="/search/cs?searchtype=author&query=Isbell%2C+C+L">Charles L. Isbell</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1805.07914v3-abstract-short" style="display: inline;"> In this paper, we describe a novel approach to imitation learning that infers latent policies directly from state observations. We introduce a method that characterizes the causal effects of latent actions on observations while simultaneously predicting their likelihood. We then outline an action alignment procedure that leverages a small amount of environment interactions to determine a mapping b… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1805.07914v3-abstract-full').style.display = 'inline'; document.getElementById('1805.07914v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1805.07914v3-abstract-full" style="display: none;"> In this paper, we describe a novel approach to imitation learning that infers latent policies directly from state observations. We introduce a method that characterizes the causal effects of latent actions on observations while simultaneously predicting their likelihood. We then outline an action alignment procedure that leverages a small amount of environment interactions to determine a mapping between the latent and real-world actions. We show that this corrected labeling can be used for imitating the observed behavior, even though no expert actions are given. We evaluate our approach within classic control environments and a platform game and demonstrate that it performs better than standard approaches. Code for this work is available at https://github.com/ashedwards/ILPO. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1805.07914v3-abstract-full').style.display = 'none'; document.getElementById('1805.07914v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 May, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 May, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to ICML 2019</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1803.10227">arXiv:1803.10227</a> <span> [<a href="https://arxiv.org/pdf/1803.10227">pdf</a>, <a href="https://arxiv.org/format/1803.10227">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Forward-Backward Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Edwards%2C+A+D">Ashley D. Edwards</a>, <a href="/search/cs?searchtype=author&query=Downs%2C+L">Laura Downs</a>, <a href="/search/cs?searchtype=author&query=Davidson%2C+J+C">James C. Davidson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1803.10227v1-abstract-short" style="display: inline;"> Goals for reinforcement learning problems are typically defined through hand-specified rewards. To design such problems, developers of learning algorithms must inherently be aware of what the task goals are, yet we often require agents to discover them on their own without any supervision beyond these sparse rewards. While much of the power of reinforcement learning derives from the concept that a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.10227v1-abstract-full').style.display = 'inline'; document.getElementById('1803.10227v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1803.10227v1-abstract-full" style="display: none;"> Goals for reinforcement learning problems are typically defined through hand-specified rewards. To design such problems, developers of learning algorithms must inherently be aware of what the task goals are, yet we often require agents to discover them on their own without any supervision beyond these sparse rewards. While much of the power of reinforcement learning derives from the concept that agents can learn with little guidance, this requirement greatly burdens the training process. If we relax this one restriction and endow the agent with knowledge of the reward function, and in particular of the goal, we can leverage backwards induction to accelerate training. To achieve this, we propose training a model to learn to take imagined reversal steps from known goal states. Rather than training an agent exclusively to determine how to reach a goal while moving forwards in time, our approach travels backwards to jointly predict how we got there. We evaluate our work in Gridworld and Towers of Hanoi and empirically demonstrate that it yields better performance than standard DDQN. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.10227v1-abstract-full').style.display = 'none'; document.getElementById('1803.10227v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 March, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1711.07676">arXiv:1711.07676</a> <span> [<a href="https://arxiv.org/pdf/1711.07676">pdf</a>, <a href="https://arxiv.org/format/1711.07676">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Transferring Agent Behaviors from Videos via Motion GANs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Edwards%2C+A+D">Ashley D. Edwards</a>, <a href="/search/cs?searchtype=author&query=Isbell%2C+C+L">Charles L. Isbell Jr</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1711.07676v1-abstract-short" style="display: inline;"> A major bottleneck for developing general reinforcement learning agents is determining rewards that will yield desirable behaviors under various circumstances. We introduce a general mechanism for automatically specifying meaningful behaviors from raw pixels. In particular, we train a generative adversarial network to produce short sub-goals represented through motion templates. We demonstrate tha… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.07676v1-abstract-full').style.display = 'inline'; document.getElementById('1711.07676v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1711.07676v1-abstract-full" style="display: none;"> A major bottleneck for developing general reinforcement learning agents is determining rewards that will yield desirable behaviors under various circumstances. We introduce a general mechanism for automatically specifying meaningful behaviors from raw pixels. In particular, we train a generative adversarial network to produce short sub-goals represented through motion templates. We demonstrate that this approach generates visually meaningful behaviors in unknown environments with novel agents and describe how these motions can be used to train reinforcement learning agents. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.07676v1-abstract-full').style.display = 'none'; document.getElementById('1711.07676v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 November, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Deep Reinforcement Learning Symposium, NIPS 2017</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1705.09045">arXiv:1705.09045</a> <span> [<a href="https://arxiv.org/pdf/1705.09045">pdf</a>, <a href="https://arxiv.org/format/1705.09045">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Cross-Domain Perceptual Reward Functions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Edwards%2C+A+D">Ashley D. Edwards</a>, <a href="/search/cs?searchtype=author&query=Sood%2C+S">Srijan Sood</a>, <a href="/search/cs?searchtype=author&query=Isbell%2C+C+L">Charles L. Isbell Jr</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1705.09045v3-abstract-short" style="display: inline;"> In reinforcement learning, we often define goals by specifying rewards within desirable states. One problem with this approach is that we typically need to redefine the rewards each time the goal changes, which often requires some understanding of the solution in the agents environment. When humans are learning to complete tasks, we regularly utilize alternative sources that guide our understandin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.09045v3-abstract-full').style.display = 'inline'; document.getElementById('1705.09045v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1705.09045v3-abstract-full" style="display: none;"> In reinforcement learning, we often define goals by specifying rewards within desirable states. One problem with this approach is that we typically need to redefine the rewards each time the goal changes, which often requires some understanding of the solution in the agents environment. When humans are learning to complete tasks, we regularly utilize alternative sources that guide our understanding of the problem. Such task representations allow one to specify goals on their own terms, thus providing specifications that can be appropriately interpreted across various environments. This motivates our own work, in which we represent goals in environments that are different from the agents. We introduce Cross-Domain Perceptual Reward (CDPR) functions, learned rewards that represent the visual similarity between an agents state and a cross-domain goal image. We report results for learning the CDPRs with a deep neural network and using them to solve two tasks with deep reinforcement learning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.09045v3-abstract-full').style.display = 'none'; document.getElementById('1705.09045v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 July, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 May, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">A shorter version of this paper was accepted to RLDM (http://rldm.org/rldm2017/)</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 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