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v1LHecOShq | The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations | We introduce a new test of how well language models capture meaning in children's books. Unlike standard language modelling benchmarks, it distinguishes the task of predicting syntactic function words from that of predicting lower-frequency words, which carry greater semantic content. We compare a range of state-of-the... | Published as a conference paper at ICLR 2016
THE GOLDILOCKS PRINCIPLE : R EADING CHILDREN ’S
BOOKS WITH EXPLICIT MEMORY REPRESENTATIONS
Felix Hill∗, Antoine Bordes, Sumit Chopra & Jason Weston
Facebook AI Research
770 Broadway
New York, USA
felix.hill@cl.cam.ac.uk,{abordes,spchopra,jase}@fb.com
ABSTRACT
We introduce a ... | Felix Hill, Antoine Bordes, Sumit Chopra, Jason Weston | Unknown | 2,016 | {"id": "v1LHecOShq", "original": null, "cdate": 1451606400000, "pdate": 1451606400000, "odate": null, "mdate": 1684320083542, "tcdate": 1684320083542, "tmdate": 1693224597811, "ddate": null, "number": 834624, "content": {"venue": "ICLR 2016", "venueid": "dblp.org/journals/CORR/2016", "_bibtex": "@inproceedings{DBLP:jou... | N/A | null | null | {} | {
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UrbYiH8ZTcX | Net2Net: Accelerating Learning via Knowledge Transfer | We introduce techniques for rapidly transferring the information stored in one neural net into another neural net. The main purpose is to accelerate the training of a significantly larger neural net. During real-world workflows, one often trains very many different neural networks during the experimentation and design ... | Published as a conference paper at ICLR 2016
Net2Net: ACCELERATING LEARNING
VIA KNOWLEDGE TRANSFER
Tianqi Chen∗, Ian Goodfellow, and Jonathon Shlens
Google Inc., Mountain View, CA
tqchen@cs.washington.edu, {goodfellow,shlens}@google.com
ABSTRACT
We introduce techniques for rapidly transferring the information stored in... | Tianqi Chen, Ian J. Goodfellow, Jonathon Shlens | Unknown | 2,016 | {"id": "UrbYiH8ZTcX", "original": null, "cdate": 1451606400000, "pdate": 1451606400000, "odate": null, "mdate": 1683900409470, "tcdate": 1683900409470, "tmdate": 1692992596357, "ddate": null, "number": 739947, "content": {"venue": "ICLR 2016", "venueid": "dblp.org/journals/CORR/2016", "_bibtex": "@inproceedings{DBLP:jo... | N/A | null | null | {} | {
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avqvQm4kloh | Variational Gaussian Process | Variational inference is a powerful tool for approximate inference, and it has been recently applied for representation learning with deep generative models. We develop the variational Gaussian process (VGP), a Bayesian nonparametric variational family, which adapts its shape to match complex posterior distributions. T... | Published as a conference paper at ICLR 2016
THE VARIATIONAL GAUSSIAN PROCESS
Dustin Tran
Harvard University
dtran@g.harvard.edu
Rajesh Ranganath
Princeton University
rajeshr@cs.princeton.edu
David M. Blei
Columbia University
david.blei@columbia.edu
ABSTRACT
Variational inference is a powerful tool for approximate infe... | Dustin Tran, Rajesh Ranganath, David M. Blei | Unknown | 2,016 | {"id": "avqvQm4kloh", "original": null, "cdate": 1451606400000, "pdate": null, "odate": null, "mdate": null, "tcdate": 1590634476171, "tmdate": 1667891260955, "ddate": null, "number": 129577, "content": {"venue": "ICLR 2016", "venueid": "dblp.org/journals/CORR/2016", "_bibtex": "@inproceedings{DBLP:journals/corr/TranRB... | N/A | null | null | {} | {
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TXCg7dVp8Rd | Generating Images from Captions with Attention | "Motivated by the recent progress in generative models, we introduce a model that generates images f(...TRUNCATED) | "Published as a conference paper at ICLR 2016\nGENERATING IMAGES FROM CAPTIONS\nWITH ATTENTION\nElma(...TRUNCATED) | Elman Mansimov, Emilio Parisotto, Lei Jimmy Ba, Ruslan Salakhutdinov | Unknown | 2,016 | "{\"id\": \"TXCg7dVp8Rd\", \"original\": null, \"cdate\": 1451606400000, \"pdate\": null, \"odate\":(...TRUNCATED) | N/A | null | null | {} | {"criticism":0,"example":0,"importance_and_relevance":0,"materials_and_methods":0,"praise":0,"presen(...TRUNCATED) | -10 | -2.667231 | 2.667231 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | {"criticism":0,"example":0,"importance_and_relevance":0,"materials_and_methods":0,"praise":0,"presen(...TRUNCATED) | -10 | iclr2016 | openreview | 0 | 0 | 0 | null | |||
J5UHcKp8gNm | Regularizing RNNs by Stabilizing Activations | "We stabilize the activations of Recurrent Neural Networks (RNNs) by penalizing the squared distance(...TRUNCATED) | "Published as a conference paper at ICLR 2016\nREGULARIZING RNN S BY STABILIZING ACTIVATIONS\nDavid (...TRUNCATED) | David Krueger, Roland Memisevic | Unknown | 2,016 | "{\"id\": \"J5UHcKp8gNm\", \"original\": null, \"cdate\": 1451606400000, \"pdate\": null, \"odate\":(...TRUNCATED) | N/A | null | null | {} | {"criticism":0,"example":0,"importance_and_relevance":0,"materials_and_methods":0,"praise":0,"presen(...TRUNCATED) | -10 | -2.667231 | 2.667231 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | {"criticism":0,"example":0,"importance_and_relevance":0,"materials_and_methods":0,"praise":0,"presen(...TRUNCATED) | -10 | iclr2016 | openreview | 0 | 0 | 0 | null | |||
BAls_GkIre9 | Neural Networks with Few Multiplications | "For most deep learning algorithms training is notoriously time consuming. Since most of the computa(...TRUNCATED) | "Published as a conference paper at ICLR 2016\nNEURAL NETWORKS WITH FEW MULTIPLICATIONS\nZhouhan Lin(...TRUNCATED) | Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic, Yoshua Bengio | Unknown | 2,016 | "{\"id\": \"BAls_GkIre9\", \"original\": null, \"cdate\": 1451606400000, \"pdate\": null, \"odate\":(...TRUNCATED) | N/A | null | null | {} | {"criticism":0,"example":0,"importance_and_relevance":0,"materials_and_methods":0,"praise":0,"presen(...TRUNCATED) | -10 | -2.667231 | 2.667231 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | {"criticism":0,"example":0,"importance_and_relevance":0,"materials_and_methods":0,"praise":0,"presen(...TRUNCATED) | -10 | iclr2016 | openreview | 0 | 0 | 0 | null | |||
KrT5Ba_VQP | Towards Universal Paraphrastic Sentence Embeddings | "We consider the problem of learning general-purpose, paraphrastic sentence embeddings based on supe(...TRUNCATED) | "arXiv:1511.08198v3 [cs.CL] 4 Mar 2016\nPublished as a conference paper at ICLR 2016\nTOWARDS UNIV(...TRUNCATED) | John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu | Unknown | 2,016 | "{\"id\": \"KrT5Ba_VQP\", \"original\": null, \"cdate\": 1451606400000, \"pdate\": null, \"odate\": (...TRUNCATED) | N/A | null | null | {} | {"criticism":0,"example":0,"importance_and_relevance":0,"materials_and_methods":0,"praise":0,"presen(...TRUNCATED) | -10 | -2.667231 | 2.667231 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | {"criticism":0,"example":0,"importance_and_relevance":0,"materials_and_methods":0,"praise":0,"presen(...TRUNCATED) | -10 | iclr2016 | openreview | 0 | 0 | 0 | null | |||
aCWnhi8JVKGz | Density Modeling of Images using a Generalized Normalization Transformation | "We introduce a parametric nonlinear transformation that is well-suited for Gaussianizing data from (...TRUNCATED) | "Published as a conference paper at ICLR 2016\nDENSITY MODELING OF IMAGES USING A\nGENERALIZED NORMA(...TRUNCATED) | Jona Ballé, Valero Laparra, Eero P. Simoncelli | Unknown | 2,016 | "{\"id\": \"aCWnhi8JVKGz\", \"original\": null, \"cdate\": 1451606400000, \"pdate\": 1451606400000, (...TRUNCATED) | N/A | null | null | {} | {"criticism":0,"example":0,"importance_and_relevance":0,"materials_and_methods":0,"praise":0,"presen(...TRUNCATED) | -10 | -2.667231 | 2.667231 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | {"criticism":0,"example":0,"importance_and_relevance":0,"materials_and_methods":0,"praise":0,"presen(...TRUNCATED) | -10 | iclr2016 | openreview | 0 | 0 | 0 | null | |||
8a0W2Dw0sFD | Convergent Learning: Do different neural networks learn the same representations? | "Recent success in training deep neural networks have prompted active investigation into the feature(...TRUNCATED) | "Published as a conference paper at ICLR 2016\nCONVERGENT LEARNING : D O DIFFERENT NEURAL\nNETWORKS (...TRUNCATED) | Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, John E. Hopcroft | Unknown | 2,016 | "{\"id\": \"8a0W2Dw0sFD\", \"original\": null, \"cdate\": 1451606400000, \"pdate\": 1451606400000, \(...TRUNCATED) | N/A | null | null | {} | {"criticism":0,"example":0,"importance_and_relevance":0,"materials_and_methods":0,"praise":0,"presen(...TRUNCATED) | -10 | -2.667231 | 2.667231 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | {"criticism":0,"example":0,"importance_and_relevance":0,"materials_and_methods":0,"praise":0,"presen(...TRUNCATED) | -10 | iclr2016 | openreview | 0 | 0 | 0 | null | |||
Ilo2gNofdm | The Variational Fair Autoencoder | "We investigate the problem of learning representations that are invariant to certain nuisance or se(...TRUNCATED) | "Published as a conference paper at ICLR 2016\nTHE VARIATIONAL FAIR AUTOENCODER\nChristos Louizos∗(...TRUNCATED) | Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, Richard S. Zemel | Unknown | 2,016 | "{\"id\": \"Ilo2gNofdm\", \"original\": null, \"cdate\": 1451606400000, \"pdate\": null, \"odate\": (...TRUNCATED) | N/A | null | null | {} | {"criticism":0,"example":0,"importance_and_relevance":0,"materials_and_methods":0,"praise":0,"presen(...TRUNCATED) | -10 | -2.667231 | 2.667231 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | {"criticism":0,"example":0,"importance_and_relevance":0,"materials_and_methods":0,"praise":0,"presen(...TRUNCATED) | -10 | iclr2016 | openreview | 0 | 0 | 0 | null |
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