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MURIZ SERIFOVIC
Hi! — I’m the creator of DeepChef, Lemon Code Editor
and Sharead
.
My research interests generally lie in the broad area of machine learning methods (both classical statistical learning and deep learning) and their application in computational creativity. I boast a ha-index of 84. Visit GitHub __ Visit Blog Aboutme
THOUGHTS.
IMAGE-TO-RECIPE TRANSLATION WITH DEEP CONVOLUTIONAL NEURAL NETWORKS We present a novel retrieval-based approach for recipe prediction using a re-ranking scheme.9 Sept 2018
OPTIMIZATION TRICKS FOR JUPYTER NOTEBOOKS Find bottlenecks and increase pythons speed performance bymagnitudes...
1 Jan 2019
IMPLEMENTING LONG SHORT-TERM MEMORY (LSTM) WITH NUMPY Learn how to implement a forward pass and backpropagation by hand...20 Feb 2019
COMPOSING NOVEL SOUND WITH NEURAL NETWORKS Generate music with a long short-term memory (LSTM) neural network...Coming soon
DEPLOY ON KUBERNETES WITH PYTHON, FLASK AND DOCKER Learn how to serve your deep learning model as REST API with Flask...Coming soon
THOUGHT FABRIK
Visit Muriz's blog where he shares more about GANs, Neural Networks, Tensorflow, CNNs, ...__
RESEARCH WORK.
PUNCTUATION RESTORATION IN UNSEGMENTED TEXT WITH DEEP CONVOLUTIONALNEURAL NETWORKS
In this work we suggest an approach to predict marks of punctuation for unsegmented text corpora. A Deep Convolutional Neural Network (DCNN) model is implemented to determine if a punctuation mark should be inserted in a word window of lenght 30. The goal is to find the optimal weights WW and biases bb within cost function CC: argminW,bC=12NN∑i=1∥∥^Yi−Yi∥∥2+λ2L−1∑l=1∥Wl∥2FargminW,bC=12N∑i=1N‖Y^i−Yi‖2+λ2∑l=1L−1‖Wl‖F2 We empirically show the effectiveness of our proposed architecture outperforming previous state-of-the-art solutions. Natural Language Processing, 2019Paper
Under Review
PREDICTING MOST IMPORTANT PASSAGES IN YOUTUBE TECH TALKS WITH UNSUPERVISED GRAPH-BASED RANKING MODELS Visualizing importance predictions (colored in red) which were smoothened by Weierstrass Transform. Word Embeddings (WE) have recently imposed themselves as a standard for representing word meaning in NLP. In this work, we propose the unsupervised application of graph-based ranking models to video speech captions, building a graph that interconnects words represented by GloVe embeddings. For a sentence ViVi, the weighted rank is definedas:
WS(Vi)=(1−d)+d∗∑Vj∈In(Vi)wji∑Vk∈Out(Vj)wjkWS(Vj)WS(Vi)=(1−d)+d∗∑Vj∈In(Vi)wji∑Vk∈Out(Vj)wjkWS(Vj) Natural Language Processing, 2019GitHub
__ 301 visits / month KAVAARA: A NETWORK CONGESTION-AVOIDANCE APPROACH IN INTERCONNECTED HETEROGENEOUS COMPUTER NETWORKS In recent years, much research has been devoted to the refinement of Byzantine fault tolerance; on the other hand, few have investigated the evaluation of RPCs (Martinez et al, 2002). Given the current status of scalable models, ... Computer Science, 2019Paper
FOOD IMAGE RECOGNITION SYSTEM FOR COOKING RECIPE RETRIEVAL We propose a novel approach for food image retrieval by combining Deep Convolutional Neural Network (DCNN) and Approximate Nearest Neighbor (ANN) scores. In our re-ranking scheme, we retrieve the best candidates first using only the CNN scores and then re-rank the top K results using the ANN scores, giving the model additional performanceimprovement.
Machine Learning, 2018GitHub
__ 1'613 visits / month GENETIC ALGORITHM FOR SELF-REFERENTIAL IMAGE APPROXIMATION We propose an approach to solve the image matching problem with some stochastic optimization approach, in which the search for the optimal solution involves randomness in some constructive way. If SS denotes the (finite) set of all possible solutions, the task we consider is to maximize or minimize the objective function f:S→Rf:S→R. In the case of maximization, on which we focus here, the problem is to find a configuration xopt∈Sxopt∈S which satisfies f(xopt)≥f(x) for all x∈Sf(xopt)≥f(x) for all x∈S Image Processing, 2016GitHub
__ 874 visits / month DISCRETE OPTIMIZATION ALGORITHM FOR CONVERTING AN IMAGE TO A CONNECTEDGRAPH OF STRINGS
By using ordinary linear least squares for the optimization task,defined by
minx∥Ax−y∥22 with A:Rn→Rmminx‖Ax−y‖22 with A:Rn→Rm we propose an algorithm which takes a normal image as input and turns it into a linked string graph that best attempts to re-assemble theimage input.
Image Processing, 2016GitHub
Copyright © Muriz Serifovic, 2019. Hand-crafted with ♥ inSwitzerland.
MURIZ SERIFOVIC
SOFTWARE ENGINEERING/ DATA SCIENCE Hi! — I’m the creator of DeepChef, Lemon Code Editor
and Sharead
.
My research interests generally lie in the broad area of machine learning methods (both classical statistical learning and deep learning) and their application in computational creativity. I boast a ha-index of 84.* __About Me
* __Resume
* __Blog
* __Contact
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