NIPS 2015
Twenty-ninth Conference on Neural Information Processing Systems NIPS 2015 Accepted Papers Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing Nihar Shah*, UC Berkeley; Dengyong Zhou, MSR Learning with Symmetric Label Noise: The Importance of Being Unhinged Brendan van Rooyen, NICTA; Aditya Menon*, NICTA; Robert Williamson, NICTA Algorithmic Stability and Uniform Generalizatio
NIPS 2016
Videos Videos from the Tutorials and Conference are now linked into the schedule. Also PDF's of papers are linked to posters. Follow Facebook/Twitter (below) for further video announcements. View Full Schedule » Awards We would like to congratulate the winners of the best paper, best student paper, best demonstration and best reviewers awards. View Awards »
NIPS : Conferences : 2014 : Program : NIPS 2014 Accepted Papers
Find an error with your paper? Please login to CMT to fix any errors. Fixes will eventually be propagated here. Orals | Spotlights | Posters Orals "How hard is my MDP?" The distribution-norm to the rescue In Reinforcement Learning (RL), state-of-the-art algorithms require a large number of samples per state-action pair to estimate the transition kernel $p$. In many problems, a good approximation o
NIPS読み会2013: One-shot learning by inverting a compositional causal process
This document summarizes research on one-shot learning using a hierarchical Bayesian program learning (HBPL) model. The key points are: - The HBPL model achieved human-level performance on one-shot classification and generation tasks, outperforming other deep learning models. - It used an Omniglot dataset of handwritten characters from different alphabets to learn concepts from a single example, a
【論文紹介】Approximate Bayesian Image Interpretation Using Generative Probabilistic Graphics Programs
2014/1/23に行われたNIPS2013読み会の発表資料です。 論文：V.Mansinghka, T.D,Kulkarni, Y.N.Perov, J.Tenenbaum: Approximate Bayesian Image Interpretation Using Generative Probabilistic Graphics Programs の解説です。Read less
NIPS2013読み会: More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server
NIPS2013読み会の発表資料です。 Qirong Ho et al, "More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server�", NIPS2013. http://media.nips.cc/nipsbooks/nipspapers/paper_files/nips26/631.pdfRead less
NIPS 2013 Proceedings
Advances in Neural Information Processing Systems 26 (NIPS 2013) The papers below appear in Advances in Neural Information Processing Systems 26 edited by C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger. They are proceedings from the conference, "Neural Information Processing Systems 2013." The Randomized Dependence Coefficient David Lopez-Paz, Philipp Hennig, Bern
Frontiers of Network Analysis: Methods, Models, and Applications
Lise Getoor (University of California, Santa Cruz) Entity-based Data Science Abstract: There is a growing interest in integrating, analyzing, visualizing and making sense of large collections structured, semi-structured and unstructured data. In the world of big data, data science provides tools to help with this process – tools for cleaning the data, tools for integrating and aligning the data, t
NIPS : Conferences : 2013 : Program : NIPS 2013 Accepted Papers
$k$-Prototype Learning for 3D Rigid Structures H. Ding, R. Berezney, J. Xu(More) Efficient Reinforcement Learning via Posterior Sampling I. Osband, D. Russo, B. Van Roy(Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings A. Guha Thakurta, A. SmithA* Lasso for Learning a Sparse Bayesian Network Structure J. Xiang, S. KimAccelerated Mini-Batch Stochastic Du
論文紹介:Practical Bayesian Optimization of Machine Learning Algorithms(NIPS2012)
はじめまして、宇土敬祐(@utotch)と申します。 普段の仕事では、機械学習やロボティックス、画像処理等の研究をやっていますが、趣味でHaskell や圏論の勉強をしたりしています。よろしく御願いします。 私は、先日 ICML2013に出張してきたのですが、Vanishing Component Analysis の話を 塚原と鈴木に紹介したら、一気に私より詳しくなってアクセル全開で解説を始めてしまいました。(もはや私の立場が形無し…(泣)) 私の方は、もっとゆったりと行きたいと思います。 NIPS2012 の論文紹介で、Gaussian Process を使って機械学習器のハイパーパラメータをチューニングするお話です。 私が素朴に伝えたいのは、この論文そのものというより、Gaussian Process の直感的なイメージと用途です。 私はPRMLでGaussian Process (
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NIPS 2014 Accepted Papers
NIPS2013読み会：Inverse Density as an Inverse Problem: The Fredholm Equation Approach
NIPS2013読み会: Distributed Representations of Words and Phrases and their Compositionality
NIPS2013読み会 DeViSE: A Deep Visual-Semantic Embedding Model
"Playing Atari with Deep Reinforcement Learning"
NIPS 2013 Accepted Papers
The Interplay of Machine Learning and Mechanism Design