Structural matching by discrete relaxation

This paper describes a Bayesian framework for performing relational graph matching by discrete relaxation. Our basic aim is to draw on this framework to provide a comparative evaluation of a number of contrasting approaches to relational matching. Broadly speaking there are two main aspects to this study. Firstly we locus on the issue of how relational inexactness may be quantified. We illustrate that several popular relational distance measures can be recovered as specific limiting cases of the Bayesian consistency measure. The second aspect of our comparison concerns the way in which structural inexactness is controlled. We investigate three different realizations ai the matching process which draw on contrasting control models. The main conclusion of our study is that the active process of graph-editing outperforms the alternatives in terms of its ability to effectively control a large population of contaminating clutter

In search of dispersed memories: Generative diffusion models are associative memory networks

Uncovering the mechanisms behind long-term memory is one of the most fascinating open problems in neuroscience and artificial intelligence. Artificial associative memory networks have been used to formalize important aspects of biological memory. Generative diffusion models are a type of generative machine learning techniques that have shown great performance in many tasks. Like associative memory systems, these networks define a dynamical system that converges to a set of target states. In this work we show that generative diffusion models can be interpreted as energy-based models and that, when trained on discrete patterns, their energy function is (asymptotically) identical to that of modern Hopfield networks. This equivalence allows us to interpret the supervised training of diffusion models as a synaptic learning process that encodes the associative dynamics of a modern Hopfield network in the weight structure of a deep neural network. Leveraging this connection, we formulate a generalized framework for understanding the formation of long-term memory, where creative generation and memory recall can be seen as parts of a unified continuum

Deep Learning in Image Analysis

Import 03/11/2016Tématem této diplomové práce je deep learning v analýze obrazu. V práci jsou popisovány principy a některé typy (umělých) neuronových sítí, následuje praktické seznámení s nimi a~jejich možné využití v praxi pro detekci objektů v obraze. Experimentovalo se s konvolučními neuronovými sítěmi na detekci volných a zaplněných parkovacích míst na parkovišti. Využil se k tomu objektově-orientovaný skriptovací programovací jazyk Python a framework Caffe, který běží i na platformě CUDA.Theme of this master's thesis is deep learning in image analysis. The thesis describes principles of (artificial) neural networks and describes several types of neural networks followed by familiarization and practical use for detection of objects in image. Convolution neural networks are chosen to demonstrate detection of free and occupied parking places. This work is based on object-oriented and scripting programming language Python and framework Caffe. The framework Caffe also works on CUDA platform.460 - Katedra informatikyvýborn