Learnergy: Energy-based Machine Learners

• URL: http://arxiv.org/abs/2003.07443v2
• Date: Wed, 23 Sep 2020 15:39:03 GMT
• Title: Learnergy: Energy-based Machine Learners
• Authors: Mateus Roder, Gustavo Henrique de Rosa, Jo\~ao Paulo Papa
• Abstract summary: Machine learning techniques have been broadly encouraged in the context of deep learning architectures. An exciting algorithm denoted as Restricted Boltzmann Machine relies on energy- and probabilistic-based nature to tackle the most diverse applications, such as classification, reconstruction, and generation of images and signals.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Throughout the last years, machine learning techniques have been broadly encouraged in the context of deep learning architectures. An exciting algorithm denoted as Restricted Boltzmann Machine relies on energy- and probabilistic-based nature to tackle the most diverse applications, such as classification, reconstruction, and generation of images and signals. Nevertheless, one can see they are not adequately renowned compared to other well-known deep learning techniques, e.g., Convolutional Neural Networks. Such behavior promotes the lack of researches and implementations around the literature, coping with the challenge of sufficiently comprehending these energy-based systems. Therefore, in this paper, we propose a Python-inspired framework in the context of energy-based architectures, denoted as Learnergy. Essentially, Learnergy is built upon PyTorch to provide a more friendly environment and a faster prototyping workspace and possibly the usage of CUDA computations, speeding up their computational time.

Related papers

• From Computation to Consumption: Exploring the Compute-Energy Link for Training and Testing Neural Networks for SED Systems [9.658615045493734]
  We study several neural network architectures that are key components of sound event detection systems. We measure the energy consumption for training and testing small to large architectures. We establish complex relationships between the energy consumption, the number of floating-point operations, the number of parameters, and the GPU/memory utilization.
  arXiv  Detail & Related papers  (2024-09-08T12:51:34Z)
• The Potential of Combined Learning Strategies to Enhance Energy Efficiency of Spiking Neuromorphic Systems [0.0]
  This manuscript focuses on enhancing brain-inspired perceptual computing machines through a novel combined learning approach for Convolutional Spiking Neural Networks (CSNNs) CSNNs present a promising alternative to traditional power-intensive and complex machine learning methods like backpropagation, offering energy-efficient spiking neuron processing inspired by the human brain.
  arXiv  Detail & Related papers  (2024-08-13T18:40:50Z)
• Mechanistic Neural Networks for Scientific Machine Learning [58.99592521721158]
  We present Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences. It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations. Central to our approach is a novel Relaxed Linear Programming solver (NeuRLP) inspired by a technique that reduces solving linear ODEs to solving linear programs.
  arXiv  Detail & Related papers  (2024-02-20T15:23:24Z)
• Deep Photonic Reservoir Computer for Speech Recognition [49.1574468325115]
  Speech recognition is a critical task in the field of artificial intelligence and has witnessed remarkable advancements. Deep reservoir computing is energy efficient but exhibits limitations in performance when compared to more resource-intensive machine learning algorithms. We propose a photonic-based deep reservoir computer and evaluate its effectiveness on different speech recognition tasks.
  arXiv  Detail & Related papers  (2023-12-11T17:43:58Z)
• Energy Estimates Across Layers of Computing: From Devices to Large-Scale Applications in Machine Learning for Natural Language Processing, Scientific Computing, and Cryptocurrency Mining [0.0]
  Estimates of energy usage in layers of computing from devices to algorithms have been determined and analyzed. Three large-scale computing applications such as Artificial Intelligence (AI)/Machine Learning for Natural Language Processing, Scientific Simulations, and Cryptocurrency Mining have been estimated.
  arXiv  Detail & Related papers  (2023-10-11T14:14:05Z)
• Energy Transformer [64.22957136952725]
  Our work combines aspects of three promising paradigms in machine learning, namely, attention mechanism, energy-based models, and associative memory. We propose a novel architecture, called the Energy Transformer (or ET for short), that uses a sequence of attention layers that are purposely designed to minimize a specifically engineered energy function.
  arXiv  Detail & Related papers  (2023-02-14T18:51:22Z)
• Energy Consumption of Neural Networks on NVIDIA Edge Boards: an Empirical Model [6.809944967863927]
  Recently, there has been a trend of shifting the execution of deep learning inference tasks toward the edge of the network, closer to the user, to reduce latency and preserve data privacy. In this work, we aim at profiling the energetic consumption of inference tasks for some modern edge nodes. We have then distilled a simple, practical model that can provide an estimate of the energy consumption of a certain inference task on the considered boards.
  arXiv  Detail & Related papers  (2022-10-04T14:12:59Z)
• Flashlight: Enabling Innovation in Tools for Machine Learning [50.63188263773778]
  We introduce Flashlight, an open-source library built to spur innovation in machine learning tools and systems. We see Flashlight as a tool enabling research that can benefit widely used libraries downstream and bring machine learning and systems researchers closer together.
  arXiv  Detail & Related papers  (2022-01-29T01:03:29Z)
• Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges [50.22269760171131]
  The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods. This text is concerned with exposing pre-defined regularities through unified geometric principles. It provides a common mathematical framework to study the most successful neural network architectures, such as CNNs, RNNs, GNNs, and Transformers.
  arXiv  Detail & Related papers  (2021-04-27T21:09:51Z)
• Spiking Neural Networks Hardware Implementations and Challenges: a Survey [53.429871539789445]
  Spiking Neural Networks are cognitive algorithms mimicking neuron and synapse operational principles. We present the state of the art of hardware implementations of spiking neural networks. We discuss the strategies employed to leverage the characteristics of these event-driven algorithms at the hardware level.
  arXiv  Detail & Related papers  (2020-05-04T13:24:00Z)
• Resource-Efficient Neural Networks for Embedded Systems [23.532396005466627]
  We provide an overview of the current state of the art of machine learning techniques. We focus on resource-efficient inference based on deep neural networks (DNNs), the predominant machine learning models of the past decade. We substantiate our discussion with experiments on well-known benchmark data sets using compression techniques.
  arXiv  Detail & Related papers  (2020-01-07T14:17:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.