Russ Salakhutdinov

@rsalakhu

UPMC Professor of Computer Science at Carnegie Mellon University

Vrijeme pridruživanja: siječanj 2015.

Medijski sadržaj

  1. 10. pro 2019.

    New work on Geometric Capsules: Learning to group 3D points into parts & parts into the whole object in unsupervised way. Each capsule represents a visual entity consisting of a pose & feature representing "where" & ''what'' it is. w/t &

  2. 12. stu 2019.

    paper from our Apple group on Worst Cases Policy Gradients: Learning more robust policies by minimizing long-tail risks, reducing the likelihoods of bad outcomes. with Charlie Tang and Jian Zhang.

  3. 5. stu 2019.

    paper on Multiple Futures Prediction: Sequential generative model that learns multi-step future motions/interactions of agents directly from multi-agent trajectory data, while remaining scalable to a large number of agents w/t C. Tang @ Apple

  4. 22. kol 2019.

    Check out workshop on Sets and Partitions, focusing on models with set-based inputs/outputs, models of partitions and novel clustering methodology:

  5. 10. kol 2019.

    Congratulations to Zhilin Yang for successfully defending his PhD thesis at CMU in just 4 years! Zhilin introduced XL-Net, Transformer-XL, Mixture of Softmaxes High-Rank LM, HotpotQA, GLoMo Unsupervised Learning of Relational Graphs, just to name a few:

  6. 4. srp 2019.

    Participate in competition on sample-efficient reinforcement learning using human priors: with , Brandon Houghtonm et. al, and with sponsoring the compute:

  7. 20. lip 2019.

    It is interesting to see that Transformer-XL can already generate coherent, novel text articles with thousands of tokens, see below. Code, pretrained models, paper XLNet will likely improve over Transformer-XL and we will make those models available soon.

  8. 16. lip 2019.

    Congratulations to , Saurabh Gupta for taking the 1st place in RGB-D track & a joint 1st place in RGB track at Habitat Challenge - Autonomous Navigation Challenge in Embodied AI Code and paper coming up very soon (with Abhinav Gupta)

  9. 15. lip 2019.

    From your ICML2019 Program Chairs with . We are done my friends! We hope you enjoyed ICML this year! And big thanks to all members of Organizing Committee and our workflow chairs for making it a successful conference!

  10. 31. svi 2019.
  11. 6. svi 2019.

    New CMU ML blog entry: Your 2 is My 1, Your 3 is My 9: Handling Crazy Miscalibrations in Ratings from People

  12. 2. ožu 2019.

    Slides from my talk on Integrating Domain Knowledge into Deep Learning at the New York Academy of Sciences . Special shoutout to and Bhuwan Dhingra for leading this amazing work and helping me with the slides:

  13. 13. sij 2019.

    (1/3) ICML 2019 Call for Papers (with ) Key points: 1. Abstract submission deadline is on January 18, 2019, 3:59 p.m. Pacific, 23:59 Universal time. 2. Full papers are due on January 23, 2019, 3:59 p.m. Pacific, 23:59 Universal time.

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  14. 10. sij 2019.

    New paper: Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context: Learning long-term dependency without disrupting temporal coherence, SOTA on 5 datasets w/t Zihang Dai, Zhilin Yang et al. Code, pretrained models:

  15. 21. pro 2018.

    The nerdiest holiday greeting...

  16. 21. pro 2018.

    New paper on Point Cloud GAN: Learning to generate point clouds using ideas from hierarchical Bayes and implicit generative models: Open review: w/t Li, Zaheer, Zhang, Póczos

  17. 28. stu 2018.

    The Machine Learning Department at CMU has multiple tenure track and multiple teaching track positions. Join the best place to do ML and AI.

  18. 11. lis 2018.

    Excited to co-chair ICML 2019 with . Call for Papers Major change this year: Abstract submission deadline is on Jan 18; Full papers are due on Jan 23, 2019.

  19. 6. lis 2018.

    Deep Generative Models with Learnable Knowledge Constraints - Establishing mathematical connection between posterior regularization (PR) and RL, while expanding PR to learn constraints as the extrinsic reward in RL w/t Z. Hu, Z. Yang, et al.

  20. 28. ruj 2018.

    paper: GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations: Learning generic latent relational graphs between words, pixels from unlabeled data & transferring the graphs to downstream tasks: w/t Z. Yang, J. Zhao et al.

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