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Using Unsupervised Artificial Neural Networks to Detect Sibling Species: A case in Myxomycetes
Available on Internet Archive....
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Computing the Stereo Matching Cost with a Convolutional Neural Network
We present a method for extracting depth information from a rectified image pair. We train a convolutional neural network to predict how well two image patches match and use it to compute the stereo m...
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Spectral classification using convolutional neural networks
There is a great need for accurate and autonomous spectral classification methods in astrophysics. This thesis is about training a convolutional neural network (ConvNet) to recognize an object class (...
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Machine Learning, Revised and Updated Edition
First published in 2021...
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Parallel combinatorial optimization
xvi, 330 p. : 25 cm...
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Shape, contour, and grouping in computer vision
First published in 1999...
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Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
This paper proposes a new hybrid architecture that consists of a deep Convolutional Network and a Markov Random Field. We show how this architecture is successfully applied to the challenging problem ...
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Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
We present a method for extracting depth information from a rectified image pair. Our approach focuses on the first stage of many stereo algorithms: the matching cost computation. We approach the prob...
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Very Deep Multilingual Convolutional Neural Networks for LVCSR
Convolutional neural networks (CNNs) are a standard component of many current state-of-the-art Large Vocabulary Continuous Speech Recognition (LVCSR) systems. However, CNNs in LVCSR have not kept pace...
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Dalla predizione deterministica all’inferenza variazionale: Var-T-JEPA, LLM-JEPA e le fondamenta p
Questo paper analizza tre sviluppi tecnici della famiglia JEPA (Joint Embedding Predictive Architecture) e le loro implicazioni teoriche, empiriche e cognitive. Il primo contributo è la reinterpretaz...
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Deep learning with Elastic Averaging SGD
We study the problem of stochastic optimization for deep learning in the parallel computing environment under communication constraints. A new algorithm is proposed in this setting where the communica...
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A mathematical motivation for complex-valued convolutional networks
A complex-valued convolutional network (convnet) implements the repeated application of the following composition of three operations, recursively applying the composition to an input vector of nonneg...
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Very Deep Convolutional Networks for Text Classification
The dominant approach for many NLP tasks are recurrent neural networks, in particular LSTMs, and convolutional neural networks. However, these architectures are rather shallow in comparison to the dee...
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20230514 Chat GPT and How to Generate A ' HER Poetry' As Instructed
ChatGPT, Rhyme Schemes and How to Generate a 'HER Poetry' as Instructed by Levri Ardiansyah I presented ChatGPT with one challenge (May 9, 2023). I asked it to write poetry in the form of 'HER Poet...
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Explorations on high dimensional landscapes
Finding minima of a real valued non-convex function over a high dimensional space is a major challenge in science. We provide evidence that some such functions that are defined on high dimensional dom...
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Character-level Convolutional Networks for Text Classification
This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character...
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Distributed stochastic optimization for deep learning (thesis)
We study the problem of how to distribute the training of large-scale deep learning models in the parallel computing environment. We propose a new distributed stochastic optimization method called Ela...
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Energy-based Generative Adversarial Network
We introduce the "Energy-based Generative Adversarial Network" model (EBGAN) which views the discriminator as an energy function that attributes low energies to the regions near the data manifold and ...
View Book →Selected Writings of Bolivar, Volume Two 1823 - 1830
First published in 1951...
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Pedestrian Detection with Unsupervised Multi-Stage Feature Learning
Pedestrian detection is a problem of considerable practical interest. Adding to the list of successful applications of deep learning methods to vision, we report state-of-the-art and competitive resul...
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Advances in neural information processing systems
First published in 2001...
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Fast Convolutional Nets With fbfft: A GPU Performance Evaluation
We examine the performance profile of Convolutional Neural Network training on the current generation of NVIDIA Graphics Processing Units. We introduce two new Fast Fourier Transform convolution imple...
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Entropy-SGD: Biasing Gradient Descent Into Wide Valleys
This paper proposes a new optimization algorithm called Entropy-SGD for training deep neural networks that is motivated by the local geometry of the energy landscape. Local extrema with low generaliza...
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