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Showing 19 open source projects for "paper"
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DomainBed
DomainBed is a suite to test domain generalization algorithms
DomainBed is a PyTorch-based research suite created by Facebook Research for benchmarking and evaluating domain generalization algorithms. It provides a unified framework for comparing methods that aim to train models capable of performing well across unseen domains, as introduced in the paper In Search of Lost Domain Generalization. The library includes a wide range of well-known domain generalization algorithms, from classical baselines such as Empirical Risk Minimization (ERM) and Invariant Risk Minimization (IRM) to more advanced techniques like Domain Adversarial Neural Networks (DANN), Adaptive Risk Minimization (ARM), and Invariance Principle Meets Information Bottleneck (IB-ERM/IB-IRM). ... -
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GFPGAN
GFPGAN aims at developing Practical Algorithms
GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration. Colab Demo for GFPGAN; (Another Colab Demo for the original paper model) Online demo: Huggingface (return only the cropped face) Online demo: Replicate.ai (may need to sign in, return the whole image). Online demo: Baseten.co (backed by GPU, returns the whole image). We provide a clean version of GFPGAN, which can run without CUDA extensions. So that it can run in Windows or on CPU mode. GFPGAN aims at developing a Practical Algorithm for Real-world Face Restoration. ... -
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DifferenceKit
A fast and flexible O(n) difference algorithm framework
...The algorithm is optimized based on the Paul Heckel’s algorithm. This is a diffing algorithm developed for Carbon, works stand alone. The algorithm optimized based on the Paul Heckel’s algorithm. See also his paper A technique for isolating differences between files released in 1978. It allows all kind of diffs to be calculated in linear time O(n). RxDataSources and IGListKit are also implemented based on his algorithm. The type of the element that to take diffs must be conform to the Differentiable protocol. -
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TopDeg
Program for topological degree calculation.
The program calculates the degree deg(f,B,0) where B is a product of n nontrivial intervals in R^n and f is a continuous function from B to R^n. The algorithm is described in the following paper: http://www.ams.org/journals/mcom/2015-84-293/S0025-5718-2014-02877-9/ -
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Supervised Reptile
Code for the paper "On First-Order Meta-Learning Algorithms"
The supervised-reptile repository contains code associated with the paper “On First-Order Meta-Learning Algorithms”, which introduces Reptile, a meta-learning algorithm for learning model parameter initializations that adapt quickly to new tasks. The implementation here is aimed at supervised few-shot learning settings (e.g. Omniglot, Mini-ImageNet), not reinforcement learning, and includes scripts to run training and evaluation for few-shot classification. -
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MADDPG
Code for the MADDPG algorithm from a paper
...The code is built on top of TensorFlow and integrates with the Multiagent Particle Environments (MPE) for benchmarking. Researchers can use it to reproduce the experiments presented in the paper, which demonstrate how agents learn behaviors such as coordination, competition, and communication. Although archived, MADDPG remains a widely cited baseline in multi-agent reinforcement learning research and has inspired further algorithmic developments. -
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TOL
Total Order Labeling for Reachability Queries on Large Dynamic Graphs
This is the code used in the following paper: Andy Diwen Zhu, Wenqing Lin, Sibo Wang, and Xiaokui Xiao. Reachability queries on large dynamic graphs: a total order approach. In Proceedings of the 2014 ACM SIGMOD international conference on Management of data (SIGMOD '14). Please cite the paper if you choose to use the code. -
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Jafaran
Fast and more random implementations of java.util.Random.
...The names of implementations contain "Conc" (for concurrent) if they are thread-safe and non-blocking, or "Seq" (for sequential) if they are not thread-safe. Also provides an implementation of Ziggurat algorithm (based on J. A. Doornik paper, 2005), used by nextGaussian() methods of the provided implementations. Requires Java 5 or later. Also available on github since 2015/12/13: https://github.com/jeffhain/jafaran Principal classes: - Implementations using Mersenne-Twister algorithm (good pseudo-randomness): MTSyncRNG MTSeqRNG - Implementations using Marsaglia Xor-Shift (fast): MXSIntSeqRNG (32 bits) MXSLongSeqRNG (64 bits) (nextLong() faster, larger period) - Ziggurat: Random-based implementation of Ziggurat algorithm. -
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The library for utilization of Minimal Quadtree format for storing very large sparse matrices. Please see the paper referenced below for the description of a problem. Corresponding papers: Tree-based Space Efficient Formats for Storing the Structure of Sparse Matrices (I. Šimeček, D. Langr, P. Tvrdík), In Scalable Computing: Practice and Experience, volume 15, 2014. Space Efficient Formats for Structure of Sparse Matrices Based on Tree Structures (I.
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Mr.FSM
Large-Scale Frequent Subgraph Mining in MapReduce
This is the program used in the following paper: Wenqing Lin, Xiaokui Xiao, and Gabriel Ghinita. Large-Scale Frequent Subgraph Mining in MapReduce. In Proceedings of the 30th IEEE International Conference on Data Engineering (ICDE), pages 844-855, 2014. Please cite the paper if you choose to use the program. If having any problems, please report to {wlin1 at ntu dot edu dot sg}. -
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ProximityForest
Efficient Approximate Nearest Neighbors for General Metric Spaces
A proximity forest is a data structure that allows for efficient computation of approximate nearest neighbors of arbitrary data elements in a metric space. See: O'Hara and Draper, "Are You Using the Right Approximate Nearest Neighbor Algorithm?", WACV 2013 (best student paper award). One application of a ProximityForest is given in the following CVPR publication: Stephen O'Hara and Bruce A. Draper, "Scalable Action Recognition with a Subspace Forest," IEEE Conference on Computer Vision and Pattern Recognition, 2012. This source code is provided without warranty and is available under the GPL license. ... -
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Approximate Subgraph Matching Algorithm
Approximate Subgraph Matching Algorithm for Dependency Graphs
...This Java implementation implements our ASM algorithm. See README file: https://sourceforge.net/projects/asmalgorithm/files/ If you use our ASM implementation to support academic research, please cite the following paper: Haibin Liu, Lawrence Hunter, Vlado Keselj, and Karin Verspoor. Approximate Subgraph Matching-based Literature Mining for Biomedical Events and Relations. PLOS ONE, 8:4 e60954, 2013. -
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Exact Subgraph Matching Algorithm
Exact Subgraph Matching Algorithm for Dependency Graphs
...This Java implementation implements our ESM algorithm. See README file: https://sourceforge.net/projects/esmalgorithm/files/ If you use our ESM implementation to support academic research, please cite the following paper: Haibin Liu, Vlado Keselj, and Christian Blouin. Exploring a Subgraph Matching Approach for Extracting Biological Events from Literature. Computational Intelligence, 2013. -
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niklyaev-recursion-paper
статья о рекурсии
Статья "Способ реализации линейной рекурсии в условиях ограниченного объема памяти" -
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EGA
A novel and effictive GA algorithm to solve optimization problem
...The high efficiency of the proposed method results from the fitness estimation and the schema discovery of partial individuals in current population and. Specifically, the clustering method used in this paper is affinity propagation. The numerical experiments demonstrate that the proposed method performs promisingly for well-known benchmark problems in the term of optimization accuracy. -
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...Event extraction includes finding events and the parameters for an event in a text. The method is based on SVM but other ML algorithms can be adopted. The method details are explained in the following paper: Ehsan Emadzadeh, Azadeh Nikfarjam, and Graciela Gonzalez. 2011. Double Layered Learning for Biological Event Extraction from Text. In Proceedings of the BioNLP 2011 Workshop Companion Volume for Shared Task, Portland, Oregon, June. Association for Computational Linguistic
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An implementation of Bruhn et al.'s fast variational optical flow algorithm using the OpenCV image processing library. The code calculates dense flow fields with a user-specified level of precision.
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This is an Ada implementation of the Dancing Links X Algorithm as in Donald E. Knuth paper. AdaDLX is used to solve the Eight-Queens puzzle, Sudoku grids and Blocs in Box puzzle.
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The library currently consists of an implementation of collision detection of static/moving ellipsoids that exploits frame-coherence using separating planes. Please visit http://www.cs.hku.hk/GraphicsGroup/cd.html for paper references.
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