10
10.0
Jun 29, 2018
06/18
by
Cornelia Fermüller; Fang Wang; Yezhou Yang; Konstantinos Zampogiannis; Yi Zhang; Francisco Barranco; Michael Pfeiffer
texts
eye 10
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Looking at a person's hands one often can tell what the person is going to do next, how his/her hands are moving and where they will be, because an actor's intentions shape his/her movement kinematics during action execution. Similarly, active systems with real-time constraints must not simply rely on passive video-segment classification, but they have to continuously update their estimates and predict future actions. In this paper, we study the prediction of dexterous actions. We recorded from...
Topics: Computer Vision and Pattern Recognition, Computing Research Repository
Source: http://arxiv.org/abs/1610.00759
9
9.0
Jun 29, 2018
06/18
by
Daniel Neil; Michael Pfeiffer; Shih-Chii Liu
texts
eye 9
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comment 0
Recurrent Neural Networks (RNNs) have become the state-of-the-art choice for extracting patterns from temporal sequences. However, current RNN models are ill-suited to process irregularly sampled data triggered by events generated in continuous time by sensors or other neurons. Such data can occur, for example, when the input comes from novel event-driven artificial sensors that generate sparse, asynchronous streams of events or from multiple conventional sensors with different update...
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1610.09513
8
8.0
Nov 22, 2019
11/19
by
Charles Gilibert; Michael Pfeiffer
audio
eye 8
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comment 0
The size of the stylii used to transfer this record is 40. This record was digitized at 80 revolutions per minute.
7
7.0
Jun 28, 2018
06/18
by
Jonathan Binas; Giacomo Indiveri; Michael Pfeiffer
texts
eye 7
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comment 0
Solving constraint satisfaction problems (CSPs) is a notoriously expensive computational task. Recently, it has been proposed that efficient stochastic solvers can be obtained through appropriately configured spiking neural networks performing Markov Chain Monte Carlo (MCMC) sampling. The possibility to run such models on massively parallel, low-power neuromorphic hardware holds great promise; however, previously proposed networks are based on probabilistically spiking neurons, and thus rely on...
Topics: Neural and Evolutionary Computing, Computing Research Repository
Source: http://arxiv.org/abs/1511.00540
5
5.0
Jun 29, 2018
06/18
by
Jun Haeng Lee; Tobi Delbruck; Michael Pfeiffer
texts
eye 5
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Deep spiking neural networks (SNNs) hold great potential for improving the latency and energy efficiency of deep neural networks through event-based computation. However, training such networks is difficult due to the non-differentiable nature of asynchronous spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are only considered as noise. This enables an error...
Topics: Neural and Evolutionary Computing, Computing Research Repository
Source: http://arxiv.org/abs/1608.08782
3
3.0
Jun 29, 2018
06/18
by
Jonathan Binas; Giacomo Indiveri; Michael Pfeiffer
texts
eye 3
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comment 0
Despite their advantages in terms of computational resources, latency, and power consumption, event-based implementations of neural networks have not been able to achieve the same performance figures as their equivalent state-of-the-art deep network models. We propose counter neurons as minimal spiking neuron models which only require addition and comparison operations, thus avoiding costly multiplications. We show how inference carried out in deep counter networks converges to the same...
Topics: Neural and Evolutionary Computing, Computing Research Repository, Learning
Source: http://arxiv.org/abs/1611.00710
3
3.0
Jun 29, 2018
06/18
by
Jonathan Binas; Daniel Neil; Giacomo Indiveri; Shih-Chii Liu; Michael Pfeiffer
texts
eye 3
favorite 0
comment 0
There is an urgent need for compact, fast, and power-efficient hardware implementations of state-of-the-art artificial intelligence. Here we propose a power-efficient approach for real-time inference, in which deep neural networks (DNNs) are implemented through low-power analog circuits. Although analog implementations can be extremely compact, they have been largely supplanted by digital designs, partly because of device mismatch effects due to fabrication. We propose a framework that exploits...
Topics: Neural and Evolutionary Computing, Computing Research Repository, Learning
Source: http://arxiv.org/abs/1606.07786
2
2.0
Jun 29, 2018
06/18
by
Bodo Rueckauer; Iulia-Alexandra Lungu; Yuhuang Hu; Michael Pfeiffer
texts
eye 2
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comment 0
Deep convolutional neural networks (CNNs) have shown great potential for numerous real-world machine learning applications, but performing inference in large CNNs in real-time remains a challenge. We have previously demonstrated that traditional CNNs can be converted into deep spiking neural networks (SNNs), which exhibit similar accuracy while reducing both latency and computational load as a consequence of their data-driven, event-based style of computing. Here we provide a novel theory that...
Topics: Computer Vision and Pattern Recognition, Machine Learning, Learning, Statistics, Neural and...
Source: http://arxiv.org/abs/1612.04052
2
2.0
Jun 28, 2018
06/18
by
Philipp Kainz; Michael Pfeiffer; Martin Urschler
texts
eye 2
favorite 0
comment 0
Segmentation of histopathology sections is an ubiquitous requirement in digital pathology and due to the large variability of biological tissue, machine learning techniques have shown superior performance over standard image processing methods. As part of the GlaS@MICCAI2015 colon gland segmentation challenge, we present a learning-based algorithm to segment glands in tissue of benign and malignant colorectal cancer. Images are preprocessed according to the Hematoxylin-Eosin staining protocol...
Topics: Computing Research Repository, Computer Vision and Pattern Recognition
Source: http://arxiv.org/abs/1511.06919
2
2.0
Jun 29, 2018
06/18
by
Korsuk Sirinukunwattana; Josien P. W. Pluim; Hao Chen; Xiaojuan Qi; Pheng-Ann Heng; Yun Bo Guo; Li Yang Wang; Bogdan J. Matuszewski; Elia Bruni; Urko Sanchez; Anton Böhm; Olaf Ronneberger; Bassem Ben Cheikh; Daniel Racoceanu; Philipp Kainz; Michael Pfeiffer; Martin Urschler; David R. J. Snead; Nasir M. Rajpoot
texts
eye 2
favorite 0
comment 0
Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which...
Topics: Computer Vision and Pattern Recognition, Computing Research Repository
Source: http://arxiv.org/abs/1603.00275
