NeurIPS 2019 CMT Site; AC and SAC Guidelines; Reviewer Guidelines; Subject Areas; Style Files; Organizers. adversarial noise leading to erroneous decisions.
Discovering the compositional structure of vector representations with Role Learning Networks. endobj x�cbd�g`b`8 $�*@�\ ��H0qˀ_�x$t��� We then used the neural representation similarity to regularize CNNs trained on image classification by penalizing intermediate representations that deviated from neural ones. NeurIPS Reproducibility Challenge 2019 Aditya Singh Zebra Technologies aditya.singh@zebra.com Alessandro Bay Zebra Technologies alessandro.bay@zebra.com Abstract Miscalibration of a model is defined as the mismatch between predicting probability estimates and the true correctness likelihood. Decisions are available on OpenReview. BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance. stream to�G��M)�#�y���E��M0hf g ����Aa�$��G��Hz��oˀqsӀLD�h���*LbDRE/ R�z$M2"�$\�P��H`1����Q�4it��w���H=S�"�HH&���Ji4HHL��9�O�_ w�p����ZYNu-RZ1+�0�\���\P�]d(ׁ�v���)��1é��&�k,�9cX*�\K,[�T�'� aj��%5~�q������G�LK�y�A��C���>�Zn�8�o�~+^��~f��鬙��E�SES��u��t\��R��$�����Eۑ�~����,?�V���g��U]���,�
stream The Conference and Workshop on Neural Information Processing Systems was held in Vancouver from December 8th through the 14th. Paul Soulos, R. Thomas McCoy, Tal Linzen, and Paul Smolensky. Links to video recordings and slides are now available below.
The complete list of videos are available on SlidesLive. Moreover, the models regularized with cortical representations also improved model robustness in terms of adversarial attacks. << /Linearized 1 /L 267891 /H [ 4201 197 ] /O 29 /E 83841 /N 8 /T 267478 >> Abstract: Despite impressive performance on numerous visual tasks, Convolutional Neural Networks (CNNs) --- unlike brains --- are often highly sensitive to small perturbations of their input, e.g. 27 0 obj Auguest 5, 2020: Invited Seminar Talk@RIKEN AIP Center, Japan .
Summary. Next, we denoised the notoriously variable neural activity using strong predictive models trained on this large corpus of responses from the mouse visual system, and calculated the representational similarity for millions of pairs of images from the model's predictions. This preserved performance of baseline models when classifying images under standard benchmarks, while maintaining substantially higher performance compared to baseline or control models when classifying noisy images.
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Login × Eligibility traces provide a data-inspired alternative to backpropagation through time ... Sep 11, 2019 Blind Submission readers: everyone.
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After my previous story about BERT at EMNLP 2019, it would be a crime not to make a short recap of papers about transformers at NeurIPS 2019. Sep 06, 2019 NeurIPS 2019 readers: everyone. Aaron Schein Aaron Klein Adam White Adam Lelkes Aditi Raghunathan Aditya Modi Aditya Kusupati Aditya Menon Aditya Deshpande Adrian Dalca . News.
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NeurIPS 2019 workshop at Graph Representation Learning (oral) [project page] Hongyu Ren*, ... (NerIPS 2019-2020, ICML 2020, ECML-PKDD 2020, ISIT 2020) Workshop Reviewer (GRL+ workshop and ILRGNN workshop at ICML 2020) Talk. 24 0 obj arXiv preprint. Enter your feedback below and we'll get back to you as soon as possible.Despite impressive performance on numerous visual tasks, Convolutional Neural Networks (CNNs) --- unlike brains --- are often highly sensitive to small perturbations of their input, e.g.
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endobj This demonstrates that regularizing with neural data can be an effective tool to create an inductive bias towards more robust inference.https://cloud.docker.com/repository/docker/zheli18/neural-reg, docker pull zheli18/neural-reg:neurips19 adversarial noise leading to erroneous decisions. Machine learning researchers often express complex models as a program, relying on program transformations to add functionality. We presented natural images to mice and measured the responses of thousands of neurons from cortical visual areas.
NeurIPS 2019 Reproducibility Challenge: Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation Zhi Wen Shih-Chieh Fuh Andrei Romascanu McGill University School of Computer Science fzhi.wen, shih-chieh.fuh, andrei.romascanug@mail.mcgill.ca Abstract Adversarial editing is a common technique used for attribute transfer. 2019. << /Type /XRef /Length 78 /Filter /FlateDecode /DecodeParms << /Columns 4 /Predictor 12 >> /W [ 1 2 1 ] /Index [ 24 154 ] /Info 22 0 R /Root 26 0 R /Size 178 /Prev 267479 /ID [<0c2266bb9252b7d460e85f2606edac11><1404934d6ee508b83dbd2a95b24b3755>] >> NeurIPS Board; Organizing Committee; Program Committee; Reviewers; NeurIPS Foundation; Best Reviewers. H��~ �zHX�b`bd�R��8J�e�@��� �� Auguest 24, 2020: Invited Short Talk@KDD Deep Learning Day. Toggle navigation OpenReview.net.
TL;DR: We present eligibility propagation an alternative to BPTT that is compatible with experimental data on synaptic plasticity and competes with BPTT on machine learning benchmarks.
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