{"tddate": null, "replyto": null, "ddate": null, "original": null, "tmdate": 1525286671895, "tcdate": 1525286671895, "number": 1, "cdate": 1518407644133, "id": "HJuMvYPaM", "invitation": "ICLR.cc/2018/Workshop/-/Withdraw_Submission", "forum": "HJuMvYPaM", "signatures": ["~Mohammad_Taha_Bahadori1"], "readers": ["everyone"], "writers": ["ICLR.cc/2018/Workshop"], "content": {"title": "Spectral Capsule Networks", "abstract": "In search for more accurate predictive models, we customize capsule networks for the learning to diagnose problem. We also propose Spectral Capsule Networks, a novel variation of capsule networks, that converge faster than capsule network with EM routing. Spectral capsule networks  consist of spatial coincidence filters that detect entities based on the alignment of extracted features on a one-dimensional linear subspace. Experiments on a public benchmark learning to diagnose dataset not only shows the success of capsule networks on this task, but also confirm the faster convergence of the spectral capsule networks.", "pdf": "/pdf/2f49e086d1e77f54d2a3607e3e751bc3625809c4.pdf", "TL;DR": "A new capsule network that converges faster on our healthcare benchmark experiments.", "paperhash": "bahadori|spectral_capsule_networks", "authors": ["Mohammad Taha Bahadori"], "authorids": ["bahadori@gatech.edu"], "keywords": ["Capsule Networks", "Healthcare"]}, "nonreaders": [], "details": {"tags": [], "replyCount": 7, "invitation": {"rdate": null, "duedate": null, "tddate": null, "ddate": null, "multiReply": null, "taskCompletionCount": null, "tmdate": 1525286662601, "cdate": 1525286662601, "tcdate": 1525286662601, "id": "ICLR.cc/2018/Workshop/-/Withdraw_Submission", "writers": ["ICLR.cc/2018/Workshop"], "signatures": ["ICLR.cc/2018/Workshop"], "readers": ["everyone"], "invitees": ["~"], "reply": {"forum": null, "replyto": null, "writers": {"values": ["ICLR.cc/2018/Workshop"]}, "signatures": {"values-regex": "~.*|ICLR.cc/2018/Workshop", "description": "Your authorized identity to be associated with the above content."}, "readers": {"description": "The users who will be allowed to read the above content.", "values": ["everyone"]}, "content": {"pdf": {"required": true, "order": 9, "value-regex": "upload", "description": "Upload a PDF file that ends with .pdf"}, "title": {"required": true, "order": 1, "description": "Title of paper.", "value-regex": ".{1,250}"}, "abstract": {"required": true, "order": 8, "description": "Abstract of paper.", "value-regex": "[\\S\\s]{1,5000}"}, "authors": {"required": true, "order": 2, "values-regex": "[^;,\\n]+(,[^,\\n]+)*", "description": "Comma separated list of author names. Please provide real names; identities will be anonymized."}, "keywords": {"order": 6, "values-regex": "(^$)|[^;,\\n]+(,[^,\\n]+)*", "description": "Comma separated list of keywords."}, "TL;DR": {"required": false, "order": 7, "description": "\"Too Long; Didn't Read\": a short sentence describing your paper", "value-regex": "[^\\n]{0,500}"}, "authorids": {"required": true, "order": 3, "values-regex": "([a-z0-9_\\-\\.]{2,}@[a-z0-9_\\-\\.]{2,}\\.[a-z]{2,},){0,}([a-z0-9_\\-\\.]{2,}@[a-z0-9_\\-\\.]{2,}\\.[a-z]{2,})", "description": "Comma separated list of author email addresses, lowercased, in the same order as above. For authors with existing OpenReview accounts, please make sure that the provided email address(es) match those listed in the author's profile. Please provide real emails; identities will be anonymized."}}}, "nonreaders": [], "noninvitees": [], "type": "note"}}}