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Ruslan Salakhutdinov — all works

354 works · page 4 of 4 · 301–354 · ← Ruslan Salakhutdinov

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  1. MyPCBench: A Benchmark for Personally Intelligent Computer-Use Agents ↗arXiv (Cornell University) · no summary yetLawrence Jang et al.2026 · 1 citation
  2. Tail-Likelihood Reinforcement Learning ↗arXiv (Cornell University) · no summary yetShrinivas Ramasubramanian et al.2026 · 1 citation
  3. Joint Distillation for Fast Likelihood Evaluation and Sampling in Flow-based Models ↗arXiv (Cornell University) · no summary yetAi, Xinyue et al.2025 · 1 citation
  4. Self-Regulation and Requesting Interventions ↗arXiv (Cornell University) · no summary yetMin, So Yeon et al.2025 · 1 citation
  5. A Connection between One-Step Regularization and Critic Regularization in Reinforcement Learning ↗arXiv (Cornell University) · no summary yetBenjamin Eysenbach et al.2023 · 1 citation
  6. MultiZoo & MultiBench: A Standardized Toolkit for Multimodal Deep Learning ↗arXiv (Cornell University) · no summary yetPaul Pu Liang et al.2023 · 1 citation
  7. Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator ↗arXiv (Cornell University) · no summary yetMakoto Yamada et al.2018 · 1 citation
  8. Discovering Order in Unordered Datasets: Generative Markov Networks ↗arXiv (Cornell University) · no summary yetYao-Hung Hubert Tsai et al.2017 · 1 citation
  9. Deep learning models for brain imaging: model depth enhances discovery power ↗F1000Research · no summary yetSergey Plis et al.2014 · 1 citation
  10. Fast Inference and Learning for Modeling Documents with a Deep Boltzmann Machine ↗no summary yetNitish Srivastava, Ruslan Salakhutdinov, Geoffrey E. Hinton2013 · 1 citation
  11. Domain Adaptation: Overfitting and Small Sample Statistics ↗arXiv (Cornell University) · no summary yetDean P. Foster, Sham M. Kakade, Ruslan Salakhutdinov2011 · 1 citation
  12. cua-speedrun: Standardized Benchmarking of the Speed of Computer-Use Agents ↗arXiv (Cornell University) · no summary yetPranjal Aggarwal et al.2026
  13. IsoCompute Playbook: Optimally Scaling Sampling Compute for LLM RL ↗arXiv (Cornell University) · no summary yetZhoujun Cheng et al.2026
  14. Thinking Before Thinking: Scaling Agentic Inference Through Meta-Reasoning ↗arXiv (Cornell University) · no summary yetParas Dahal et al.2026
  15. Tuning-Free Visual Effect Transfer Across Videos ↗Lecture notes in computer science · no summary yetMaxwell Jones et al.2026
  16. Multi-Agent Computer Use ↗arXiv (Cornell University) · no summary yetJing Yu Koh, Ruslan Salakhutdinov, Daniel Fried2026
  17. From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning ↗arXiv (Cornell University) · no summary yetLingjing Kong et al.2026
  18. Act2See: Emergent Active Visual Perception for Video Reasoning ↗arXiv (Cornell University) · no summary yetMartin Q. Ma et al.2026
  19. Video Active Perception: Effective Inference-Time Long-Form Video Understanding with Vision-Language Models ↗arXiv (Cornell University) · no summary yetMartin Q. Ma et al.2026
  20. FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning ↗arXiv (Cornell University) · no summary yetSteven Oh et al.2026
  21. GitSwarm: Decentralized Compounding Inference ↗arXiv (Cornell University) · no summary yetVedant Shah et al.2026
  22. Harness Learning Enables Generalizable Test-Time Adaptation ↗arXiv (Cornell University) · no summary yetAlvin Zhang et al.2026
  23. Response to Promises and Pitfalls of Deep Kernel Learning ↗arXiv (Cornell University) · no summary yetAndrew Gordon Wilson et al.2025
  24. Accelerating Diffusion Planners in Offline RL via Reward-Aware Consistency Trajectory Distillation ↗arXiv (Cornell University) · no summary yetDuan, Xintong et al.2025
  25. Blind Inverse Problem Solving Made Easy by Text-to-Image Latent Diffusion ↗arXiv (Cornell University) · no summary yetMichail Dontas et al.2024
  26. Understanding Visual Concepts Across Models ↗arXiv (Cornell University) · no summary yetBrandon Trabucco et al.2024
  27. Contrastive Example-Based Control ↗arXiv (Cornell University) · no summary yetKyle Hatch et al.2023
  28. Combining Programmable Potentials and Neural Networks for Materials Problems. ↗National Conference on Artificial Intelligence · no summary yetRyan Mohr et al.2021
  29. From Differentiable Reasoning to Self-supervised Embodied Active Learning ↗Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science · no summary yetRuslan Salakhutdinov2021
  30. Uncertainty Weighted Offline Reinforcement Learning ↗no summary yetYue Wu et al.2021
  31. 視覚ナビゲーションのためのニューラルトポロジーSLAM【JST・京大機械翻訳】 ↗IEEE Conference Proceedings · no summary yetSingh Chaplot Devendra et al.2020
  32. Planning with Submodular Objective Functions ↗arXiv (Cornell University) · no summary yetRuosong Wang et al.2020
  33. Close Category Generalization. ↗arXiv (Cornell University) · no summary yetYao-Yuan Yang et al.2020
  34. Cross-Task Knowledge Transfer for Visually-Grounded Navigation ↗no summary yetDevendra Singh Chaplot et al.2018
  35. Multi-task neural networks for QSAR predictions; 2014; http://arxiv.org/abs/1406.1231; arXiv:1406.1231 [stat.ML ↗no summary yetGeorge E. Dahl, Navdeep Jaitly, Ruslan Salakhutdinov2016
  36. Deep Learning & Feature Learning Methods for Vision ↗no summary yetRob Fergus (nyu et al.2016
  37. Journal of Machine Learning Research 15 (2014) 1- Submitted 6/13; Revised 3/14; Published 9/14 Multimodal Learning with Deep Boltzmann Machines ↗no summary yetNitish Srivastava, Ruslan Salakhutdinov, Aapo Hyvärinen2015
  38. A New Learning Algorithm for Stochastic Feedforward Neural Nets ↗no summary yetYichuan Tang, Ruslan Salakhutdinov2015
  39. Accurate and Conservative Estimates of MRF Log-likelihood using Reverse Annealing. arXiv:1412.8566 ↗no summary yetYuri Burda, Roger Grosse, Ruslan Salakhutdinov2014
  40. Reviewed by: Xi-Nian Zuo, Chinese Academy of ↗no summary yetSergey M. Plis et al.2014
  41. Deep Learning & Feature Learning Methods for Vision CVPR 2012 Tutorial: 9am-5:30pm ↗no summary yetRob Fergus (nyu et al.2013
  42. Workshop on Unsupervised and Transfer Learning One-Shot Learning with a Hierarchical Nonparametric Bayesian Model ↗no summary yetRuslan Salakhutdinov et al.2013
  43. Learning Generative Models with Visual Attention. ArXiv e-prints ↗no summary yetYichuan Tang, Nitish Srivastava, Ruslan Salakhutdinov2013
  44. Matrix reconstruction with the local max norm. ArXiv Report arXiv:1210.5196 ↗no summary yetRina Foygel, Nathan Srebro, Ruslan Salakhutdinov2012
  45. Domain Adaptation: A Small Sample Statistical Approach ↗International Conference on Artificial Intelligence and Statistics · no summary yetRuslan Salakhutdinov, Sham M. Kakade, Dean P. Foster2012
  46. Learning and Evaluaing Deep Bolztmann Machines ↗no summary yetRuslan Salakhutdinov, Geoffrey E. Hinton2012
  47. massachusetts institute of technology, cambridge, ma 02,139 usa — www.csail.mit.eduOne-Shot Learning with a Hierarchical Nonparametric Bayesian Model ↗no summary yetRuslan Salakhutdinov et al.2010
  48. This PDF file includes: Materials and Methods ↗no summary yetGeoffrey E. Hinton et al.2009
  49. Bayesian probabilistic matrix factorization using MCMC ↗no summary yetRuslan Salakhutdinov, Andriy Mnih2008
  50. DRAFT. A revised version will appear in NIPS 2007. Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes ↗no summary yetRuslan Salakhutdinov, Geoffrey E. Hinton2008
  51. DRAFT. A revised version will appear in NIPS 2007. Probabilistic Matrix Factorization ↗no summary yetRuslan Salakhutdinov, Andriy Mnihdepartment2008
  52. Simultaneous Localization and Surveying with Multiple Agents ↗no summary yetRuslan Salakhutdinov, Sam T. Roweis2008
  53. Relationship between gradient and EM steps in latent variable models. http://www.cs.toronto.edu/rsalakhu/ecg ↗no summary yetRuslan Salakhutdinov, Sam T. Roweis, Zoubin Ghahramani2003
  54. Relationship between gradient and EM steps in latent variable models ↗no summary yetRuslan Salakhutdinov, Sam T. Roweis, Zoubin Ghahramani2003

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