Projects that are tagged with scalable.


Logo RLPy 1.3a

by bobklein2 - August 28, 2014, 14:34:35 CET [ Project Homepage BibTeX Download ] 1374 views, 307 downloads, 1 subscription

About: RLPy is a framework for performing reinforcement learning (RL) experiments in Python. RLPy provides a large library of agent and domain components, and a suite of tools to aid in experiments (parallelization, hyperparameter optimization, code profiling, and plotting).

Changes:
  • Fixed bug where results using same random seed were different with visualization turned on/off
  • Created RLPy package on pypi (Available at https://pypi.python.org/pypi/rlpy)
  • Switched from custom logger class to python default
  • Added unit tests
  • Code readability improvements (formatting, variable names/ordering)
  • Restructured TD Learning heirarchy
  • Updated tutorials

Logo JMLR MLPACK 1.0.9

by rcurtin - July 28, 2014, 20:52:10 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 31038 views, 6238 downloads, 6 subscriptions

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About: A scalable, fast C++ machine learning library, with emphasis on usability.

Changes:
  • GMM initialization is now safer and provides a working GMM when constructed with only the dimensionality and number of Gaussians (#314).
  • Check for division by 0 in Forward-Backward Algorithm in HMMs (#314).
  • Fix MaxVarianceNewCluster (used when re-initializing clusters for k-means) (#314).
  • Fixed implementation of Viterbi algorithm in HMM::Predict() (#316).
  • Significant speedups for dual-tree algorithms using the cover tree (#243, #329) including a faster implementation of FastMKS.
  • Fix for LRSDP optimizer so that it compiles and can be used (#325).
  • CF (collaborative filtering) now expects users and items to be zero-indexed, not one-indexed (#324).
  • CF::GetRecommendations() API change: now requires the number of recommendations as the first parameter. The number of users in the local neighborhood should be specified with CF::NumUsersForSimilarity().
  • Removed incorrect PeriodicHRectBound (#30).
  • Refactor LRSDP into LRSDP class and standalone function to be optimized (#318).
  • Fix for centering in kernel PCA (#355).
  • Added simulated annealing (SA) optimizer, contributed by Zhihao Lou.
  • HMMs now support initial state probabilities; these can be set in the constructor, trained, or set manually with HMM::Initial() (#315).
  • Added Nyström method for kernel matrix approximation by Marcus Edel.
  • Kernel PCA now supports using Nyström method for approximation.
  • Ball trees now work with dual-tree algorithms, via the BallBound<> bound structure (#320); fixed by Yash Vadalia.
  • The NMF class is now AMF<>, and supports far more types of factorizations, by Sumedh Ghaisas.
  • A QUIC-SVD implementation has returned, written by Siddharth Agrawal and based on older code from Mudit Gupta.
  • Added perceptron and decision stump by Udit Saxena (these are weak learners for an eventual AdaBoost class).
  • Sparse autoencoder added by Siddharth Agrawal.