Rezultati pretraživanja
  1. 10. svi 2019.
  2. 6. svi 2019.

    What makes for a Lottery Ticket () in neural networks? We provide answers in our new paper, Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask! Read about it on our blog. With , ,

  3. 20. pro 2018.

    Check out our paper on "Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs" (now accepted at ) with my amazing advisor Prof. Yulia Tsvetkov:

  4. 13. svi 2019.

    Top trends I saw at include the rise of unsupervised representation learning, RNN losing its luster, GANs still dominating, RL moving towards meta-learning, the return of old school ideas. Great conference for not only ideas but also motivation

  5. 20. pro 2018.

    My work with and on Adversarial reprogramming of neural networks () is now accepted at

  6. 4. pro 2019.

    "Learning Representations Using Causal Invariance" Leon Bottou. Invited Talk It describes: "Invariant Risk Minimization" 🔥

  7. 5. svi 2019.

    I will be at to present our work: Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation w/

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  8. 20. pro 2018.

    Our paper "How powerful are graph neural networks?" was accepted as ICLR oral! Joint work with Keyulu Xu, Jure Leskovec and Stefanie Jegelka. Check out our paper here:

  9. 2. svi 2019.

    is almost here- check out our 2nd Learning from Limited Labeled Data (LLD) workshop on Monday 5/6. We are grateful to have a really exciting lineup including Luna Dong !

  10. 9. svi 2019.

    done. opinions: 1. RL and GANs are obscenely over-represented 2. Empirical/querying based interpretation and understanding is hip now which is nice 3. The citation game is really biased, lots of papers from sub-disciplines that should've been cited and weren't

  11. 4. svi 2019.

    Excited to organize a workshop on structure & priors in RL at on Monday with a great set of talks and posters! If you are a junior researcher, please consider submitting a challenge question to our speakers!

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  12. 28. sij 2019.

    Excited to preview our list of invited speakers for in New Orleans: Léon Bottou, Cynthia Dwork, Ian Goodfellow, Noah Goodman, Mirella Lapata, Pierre-Yves Oudeyer, Emily Shuckburgh, and, Zeynep Tufekci Tickets go on sale tomorrow @ 4pm PST at

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  13. 20. pro 2018.

    New paper by lead author from my group in collaboration with JSALT workshop members just accepted to ICLR!

  14. Posters from , all nicely accessible in one place.

  15. 15. svi 2019.

    Here's the talk I gave on 'Deterministic variational inference for robust Bayesian neural networks' in ICLR2019 paper link: Tensorflow by MSR: Tensor2Tensor by Google:

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  16. 6. svi 2019.

    Deep Generative Models for Graphs: Methods & Applications. Slides from my talk at workshop on Representation Learning on Graphs and Manifolds.

  17. 29. sij 2019.

    Happy to announce that a paper "Distributional Concavity Regularization For GANs" is accepted for poster presentation in from Preferred Networks. Congrats to Shoichiro Yamaguchi and Masanori Koyama!!!

  18. 21. sij 2019.

    Graph Wavelet Neural Network Compared to graph Fourier transform (1) Graph wavelets are local & sparse (2) Graph wavelet transform is computationally efficient (3) Convolution is localized in vertex domain. Paper PyTorch

  19. My Relativistic GANs paper was accepted to !

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  20. 21. pro 2018.

    Excited to announce that our paper, "A Statistical Approach to Assessing Neural Network Robustness" has been accepted to publication at ! 🥳 Joint work with , , and M. Pawan Kumar.

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