Torch (machine learning)

Résumé

Torch is an open-source machine learning library, a scientific computing framework, and a scripting language based on Lua. It provides LuaJIT interfaces to deep learning algorithms implemented in C. It was created at IDIAP at EPFL. Torch development moved in 2017 to PyTorch, a port of the library to Python. torch The core package of Torch is torch. It provides a flexible N-dimensional array or Tensor, which supports basic routines for indexing, slicing, transposing, type-casting, resizing, sharing storage and cloning. This object is used by most other packages and thus forms the core object of the library. The Tensor also supports mathematical operations like max, min, sum, statistical distributions like uniform, normal and multinomial, and BLAS operations like dot product, matrix–vector multiplication, matrix–matrix multiplication and matrix product. The following exemplifies using torch via its REPL interpreter:

a = torch.randn(3,4)

=a -0.2381 -0.3401 -1.7844 -0.26

Source officielle

https://en.wikipedia.org/wiki/Torch_(machine_learning)

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Publications associées (12)

A comparative study of two state-of-the-art sequence processing techniques for hand gesture recognition

Agnès Just, Sébastien Marcel

In this paper, we address the problem of the recognition of isolated, complex, dynamic hand gestures. The goal of this paper is to provide an empirical comparison of two state-of-the-art techniques for temporal event modeling combined with specific features on two different databases. The models proposed are the Hidden Markov Model (HMM) and Input/Output Hidden Markov Model (IOHMM), implemented within the framework of an open source machine learning library (www.torch.ch). There are very few hand gesture databases available to the research community; consequently, most of the algorithms and features proposed for hand gesture recognition are not evaluated on common data. We thus propose to use two publicly available databases for our comparison of hand gesture recognition techniques. The first database contains both one- and two-handed gestures, and the second only two-handed gestures. (C) 2008 Elsevier Inc. All rights reserved.

2009

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Recognition of Isolated Complex Mono- and Bi-Manual 3D Hand Gestures

Agnès Just, Sébastien Marcel

In this paper, we address the problem of the recognition of isolated complex mono- and bi-manual hand gestures. In the proposed system, hand gestures are represented by the 3D trajectories of blobs. Blobs are obtained by tracking colored body parts in real-time using the EM algorithm. In most of the studies on hand gestures, only small vocabularies have been used. In this paper, we study the results obtained on a more complex database of mono- and bi-manual gestures. These results are obtained by using a state-of-the-art sequence processing algorithm, namely Hidden Markov Models (HMMs), implemented within the framework of an open source machine learning library.

IDIAP2003

Chargement

HMM and IOHMM for the Recognition of Mono- and Bi-Manual 3D Hand Gestures

Agnès Just, Sébastien Marcel

In this paper, we address the problem of the recognition of isolated complex mono- and bi-manual hand gestures. In the proposed system, hand gestures are represented by the 3D trajectories of blobs obtained by tracking colored body parts. In this paper, we study the results obtained on a complex database of mono- and bi-manual gestures. These results are obtained by using Input/Output Hidden Markov Model (IOHMM), implemented within the framework of an open source machine learning library, and are compared to Hidden Markov Model (HMM).

IDIAP2004

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