Does Neuron Coverage Matter for Deep Reinforcement Learning? A preliminary studyUniversidad de los Andes
This work is presented at the 2020 Workshop on Testing for Deep Learning and Deep Learning for Testing (DeepTest) co-located with ICSE held virtually.
This work analyzes the possibility of using neuron coverage as a test adequacy metric for Deep Reinforcement Learning (DeepRL). This work spawns from the positive results in using neuron coverage to test (deep) neural networks. However, testing DeepRL systems, brings its own challenges. In the presentation, and associated paper, we discuss the characteristics of DeepRL that prevent promoting neuron coverage as an adequacy testing metric.
The paper is available at: https://deeptestconf.github.io/pdfs/2020-Trujillo-DeepTest.pdf
PR-258: From ImageNet to Image Classification: Contextualizing Progress on Be...Jinwon Lee
TensorFlow Korea 논문읽기모임 PR12 258번째 논문 review입니다.
이번 논문은 MIT에서 나온 From ImageNet to Image Classification: Contextualizing Progress on Benchmarks입니다.
Deep Learning 하시는 분들이면 ImageNet 모르시는 분들이 없을텐데요, 이 논문은 ImageNet의 labeling 방법의 한계와 문제점에 대해서 얘기하고 top-1 accuracy 기반의 평가 방법에도 문제가 있을 수 있음을 지적하고 있습니다.
ImageNet data의 20% 이상이 multi object를 포함하고 있지만 그 중에 하나만 정답으로 인정되는 문제가 있고, annotation 방법의 한계로 인하여 실제로 사람이 생각하는 것과 다른 class가 정답으로 labeling되어 있는 경우도 많았습니다. 또한 terrier만 20종이 넘는 등 전문가가 아니면 판단하기 어려운 label도 많다는 문제도 있었구요. 이 밖에도 다양한 실험을 통해서 정량적인 분석과 함께 human-in-the-loop을 이용한 평가로 현재 model들의 성능이 어디까지 와있는지, 그리고 앞으로 더 높은 성능을 내기 위해서 data labeling 측면에서 해결해야할 과제는 무엇인지에 대해서 이야기하고 있습니다. 논문이 양이 좀 많긴 하지만 기술적인 내용이 별로 없어서 쉽게 읽으실 수 있는데요, 자세한 내용이 궁금하신 분들은 영상을 참고해주세요!
논문링크: https://arxiv.org/abs/2005.11295
발표영상링크: https://youtu.be/CPMgX5ikL_8
One-shot learning is an object categorization problem in computer vision. Whereas most machine learning based object categorization algorithms require training on hundreds or thousands of images and very large datasets, one-shot learning aims to learn information about object categories from one, or only a few, training images
Provides a brief overview of what machine learning is, how it works (theory), how to prepare data for a machine learning problem, an example case study, and additional resources.
Neural Network and Artificial Intelligence.
Neural Network and Artificial Intelligence.
WHAT IS NEURAL NETWORK?
The method calculation is based on the interaction of plurality of processing elements inspired by biological nervous system called neurons.
It is a powerful technique to solve real world problem.
A neural network is composed of a number of nodes, or units[1], connected by links. Each linkhas a numeric weight[2]associated with it. .
Weights are the primary means of long-term storage in neural networks, and learning usually takes place by updating the weights.
Artificial neurons are the constitutive units in an artificial neural network.
WHY USE NEURAL NETWORKS?
It has ability to Learn from experience.
It can deal with incomplete information.
It can produce result on the basis of input, has not been taught to deal with.
It is used to extract useful pattern from given data i.e. pattern Recognition etc.
Biological Neurons
Four parts of a typical nerve cell :• DENDRITES: Accepts the inputs• SOMA : Process the inputs• AXON : Turns the processed inputs into outputs.• SYNAPSES : The electrochemical contactbetween the neurons.
ARTIFICIAL NEURONS MODEL
Inputs to the network arerepresented by the x1mathematical symbol, xn
Each of these inputs are multiplied by a connection weight , wn
sum = w1 x1 + ……+ wnxn
These products are simplysummed, fed through the transfer function, f( ) to generate a result and then output.
NEURON MODEL
Neuron Consist of:
Inputs (Synapses): inputsignal.Weights (Dendrites):determines the importance ofincoming value.Output (Axon): output toother neuron or of NN .
Does Neuron Coverage Matter for Deep Reinforcement Learning? A preliminary studyUniversidad de los Andes
This work is presented at the 2020 Workshop on Testing for Deep Learning and Deep Learning for Testing (DeepTest) co-located with ICSE held virtually.
This work analyzes the possibility of using neuron coverage as a test adequacy metric for Deep Reinforcement Learning (DeepRL). This work spawns from the positive results in using neuron coverage to test (deep) neural networks. However, testing DeepRL systems, brings its own challenges. In the presentation, and associated paper, we discuss the characteristics of DeepRL that prevent promoting neuron coverage as an adequacy testing metric.
The paper is available at: https://deeptestconf.github.io/pdfs/2020-Trujillo-DeepTest.pdf
PR-258: From ImageNet to Image Classification: Contextualizing Progress on Be...Jinwon Lee
TensorFlow Korea 논문읽기모임 PR12 258번째 논문 review입니다.
이번 논문은 MIT에서 나온 From ImageNet to Image Classification: Contextualizing Progress on Benchmarks입니다.
Deep Learning 하시는 분들이면 ImageNet 모르시는 분들이 없을텐데요, 이 논문은 ImageNet의 labeling 방법의 한계와 문제점에 대해서 얘기하고 top-1 accuracy 기반의 평가 방법에도 문제가 있을 수 있음을 지적하고 있습니다.
ImageNet data의 20% 이상이 multi object를 포함하고 있지만 그 중에 하나만 정답으로 인정되는 문제가 있고, annotation 방법의 한계로 인하여 실제로 사람이 생각하는 것과 다른 class가 정답으로 labeling되어 있는 경우도 많았습니다. 또한 terrier만 20종이 넘는 등 전문가가 아니면 판단하기 어려운 label도 많다는 문제도 있었구요. 이 밖에도 다양한 실험을 통해서 정량적인 분석과 함께 human-in-the-loop을 이용한 평가로 현재 model들의 성능이 어디까지 와있는지, 그리고 앞으로 더 높은 성능을 내기 위해서 data labeling 측면에서 해결해야할 과제는 무엇인지에 대해서 이야기하고 있습니다. 논문이 양이 좀 많긴 하지만 기술적인 내용이 별로 없어서 쉽게 읽으실 수 있는데요, 자세한 내용이 궁금하신 분들은 영상을 참고해주세요!
논문링크: https://arxiv.org/abs/2005.11295
발표영상링크: https://youtu.be/CPMgX5ikL_8
One-shot learning is an object categorization problem in computer vision. Whereas most machine learning based object categorization algorithms require training on hundreds or thousands of images and very large datasets, one-shot learning aims to learn information about object categories from one, or only a few, training images
Provides a brief overview of what machine learning is, how it works (theory), how to prepare data for a machine learning problem, an example case study, and additional resources.
Neural Network and Artificial Intelligence.
Neural Network and Artificial Intelligence.
WHAT IS NEURAL NETWORK?
The method calculation is based on the interaction of plurality of processing elements inspired by biological nervous system called neurons.
It is a powerful technique to solve real world problem.
A neural network is composed of a number of nodes, or units[1], connected by links. Each linkhas a numeric weight[2]associated with it. .
Weights are the primary means of long-term storage in neural networks, and learning usually takes place by updating the weights.
Artificial neurons are the constitutive units in an artificial neural network.
WHY USE NEURAL NETWORKS?
It has ability to Learn from experience.
It can deal with incomplete information.
It can produce result on the basis of input, has not been taught to deal with.
It is used to extract useful pattern from given data i.e. pattern Recognition etc.
Biological Neurons
Four parts of a typical nerve cell :• DENDRITES: Accepts the inputs• SOMA : Process the inputs• AXON : Turns the processed inputs into outputs.• SYNAPSES : The electrochemical contactbetween the neurons.
ARTIFICIAL NEURONS MODEL
Inputs to the network arerepresented by the x1mathematical symbol, xn
Each of these inputs are multiplied by a connection weight , wn
sum = w1 x1 + ……+ wnxn
These products are simplysummed, fed through the transfer function, f( ) to generate a result and then output.
NEURON MODEL
Neuron Consist of:
Inputs (Synapses): inputsignal.Weights (Dendrites):determines the importance ofincoming value.Output (Axon): output toother neuron or of NN .
Reflectivity Parameter Extraction from RADAR Images Using Back Propagation Al...IJECEIAES
Pattern recognition has been acknowledged as one of the promising research areas and it has drawn the awareness among many researchers since its existence at the beginning of the nineties. Multilayer Neural networks are used in pattern Recognition and classification based on the features derived from the input patterns. The Reflectivity information extracted from the Doppler Weather Radar (DWR) image helps in identifying the convective cloud type which has a strong relation to the precipitation rate. The reflectivity information is rooted in the DWR image with the help of colors and color bar is provided to distinguish among different reflectivity information. Artificial Neural network predicts the color based on the maximum likelihood estimation problem. This paper presents a best possible backpropagation algorithm for color identification in DWR images by comparing various backpropagation algorithms such as LevenbergMarquardt, Conjugate gradient, and Resilient back propagation etc.,. Pattern recognition using Neural networks presents better results compared to standard distance measures. It is observed that Levenberg-Marquardt backpropagation algorithm yields a regression value of 99% approximately and accuracy of 98%.
This slide set on convolutional neural networks is meant to be supplementary material to the slides from Andrej Karpathy's course. In this slide set we explain the motivation for CNN and also describe how to understand CNN coming from a standard feed forward neural networks perspective. For detailed architecture and discussions refer the original slides. I might post more detailed slides later.
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
Deep learning is receiving phenomenal attention due to breakthrough results in several AI tasks and significant research investment by top technology companies like Google, Facebook, Microsoft, IBM. For someone who has not been introduced to this technology, it may be daunting to learn several concepts such as feature learning, Restricted Boltzmann Machines, Autoencoders, etc all at once and start applying it to their own AI applications. This presentation is the first of several in this series that is intended at practitioners.
Random Valued Impulse Noise Elimination using Neural FilterEditor IJCATR
A neural filtering technique is proposed in this paper for restoring the images extremely corrupted with random valued impulse noise. The proposed intelligent filter is carried out in two stages. In first stage the corrupted image is filtered by applying an asymmetric trimmed median filter. An asymmetric trimmed median filtered output image is suitably combined with a feed forward neural network in the second stage. The internal parameters of the feed forward neural network are adaptively optimized by training of three well known images. This is quite effective in eliminating random valued impulse noise. Simulation results show that the proposed filter is superior in terms of eliminating impulse noise as well as preserving edges and fine details of digital images and results are compared with other existing nonlinear filters.
Semi supervised learning machine learning made simpleDevansh16
Video: https://youtu.be/65RV3O4UR3w
Semi-Supervised Learning is a technique that combines the benefits of supervised learning (performance, intuitiveness) with the ability to use cheap unlabeled data (unsupervised learning). With all the cheap data available, Semi Supervised Learning will get bigger in the coming months. This episode of Machine Learning Made Simple will go into SSL, how it works, transduction vs induction, the assumptions SSL algorithms make, and how SSL compares to human learning.
About Machine Learning Made Simple:
Machine Learning Made Simple is a playlist that aims to break down complex Machine Learning and AI topics into digestible videos. With this playlist, you can dive head first into the world of ML implementation and/or research. Feel free to drop any feedback you might have down below.
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
Reflectivity Parameter Extraction from RADAR Images Using Back Propagation Al...IJECEIAES
Pattern recognition has been acknowledged as one of the promising research areas and it has drawn the awareness among many researchers since its existence at the beginning of the nineties. Multilayer Neural networks are used in pattern Recognition and classification based on the features derived from the input patterns. The Reflectivity information extracted from the Doppler Weather Radar (DWR) image helps in identifying the convective cloud type which has a strong relation to the precipitation rate. The reflectivity information is rooted in the DWR image with the help of colors and color bar is provided to distinguish among different reflectivity information. Artificial Neural network predicts the color based on the maximum likelihood estimation problem. This paper presents a best possible backpropagation algorithm for color identification in DWR images by comparing various backpropagation algorithms such as LevenbergMarquardt, Conjugate gradient, and Resilient back propagation etc.,. Pattern recognition using Neural networks presents better results compared to standard distance measures. It is observed that Levenberg-Marquardt backpropagation algorithm yields a regression value of 99% approximately and accuracy of 98%.
This slide set on convolutional neural networks is meant to be supplementary material to the slides from Andrej Karpathy's course. In this slide set we explain the motivation for CNN and also describe how to understand CNN coming from a standard feed forward neural networks perspective. For detailed architecture and discussions refer the original slides. I might post more detailed slides later.
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
Deep learning is receiving phenomenal attention due to breakthrough results in several AI tasks and significant research investment by top technology companies like Google, Facebook, Microsoft, IBM. For someone who has not been introduced to this technology, it may be daunting to learn several concepts such as feature learning, Restricted Boltzmann Machines, Autoencoders, etc all at once and start applying it to their own AI applications. This presentation is the first of several in this series that is intended at practitioners.
Random Valued Impulse Noise Elimination using Neural FilterEditor IJCATR
A neural filtering technique is proposed in this paper for restoring the images extremely corrupted with random valued impulse noise. The proposed intelligent filter is carried out in two stages. In first stage the corrupted image is filtered by applying an asymmetric trimmed median filter. An asymmetric trimmed median filtered output image is suitably combined with a feed forward neural network in the second stage. The internal parameters of the feed forward neural network are adaptively optimized by training of three well known images. This is quite effective in eliminating random valued impulse noise. Simulation results show that the proposed filter is superior in terms of eliminating impulse noise as well as preserving edges and fine details of digital images and results are compared with other existing nonlinear filters.
Semi supervised learning machine learning made simpleDevansh16
Video: https://youtu.be/65RV3O4UR3w
Semi-Supervised Learning is a technique that combines the benefits of supervised learning (performance, intuitiveness) with the ability to use cheap unlabeled data (unsupervised learning). With all the cheap data available, Semi Supervised Learning will get bigger in the coming months. This episode of Machine Learning Made Simple will go into SSL, how it works, transduction vs induction, the assumptions SSL algorithms make, and how SSL compares to human learning.
About Machine Learning Made Simple:
Machine Learning Made Simple is a playlist that aims to break down complex Machine Learning and AI topics into digestible videos. With this playlist, you can dive head first into the world of ML implementation and/or research. Feel free to drop any feedback you might have down below.
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
IMAGE CLASSIFICATION USING DIFFERENT CLASSICAL APPROACHESVikash Kumar
IMAGE CLASSIFICATION USING KNN, RANDOM FOREST AND SVM ALGORITHM ON GLAUCOMA DATASETS AND EXPLAIN THE ACCURACY, SENSITIVITY, AND SPECIFICITY OF EACH AND EVERY ALGORITHMS
Lifelong Learning for Dynamically Expandable NetworksNAVER Engineering
발표자: 윤재홍(KAIST 박사과정)
발표일: 2018.7.
We propose a novel deep network architecture for lifelong learning which we refer to as Dynamically Expandable Network (DEN), that can dynamically decide its network capacity as it trains on a sequence of tasks, to learn a compact overlapping knowledge sharing structure among tasks. DEN is efficiently trained in an online manner by performing selective retraining, dynamically expands network capacity upon arrival of each task with only the necessary number of units, and effectively prevents semantic drift by splitting/duplicating units and timestamping them. We validate DEN on multiple public datasets under lifelong learning scenarios, on which it not only significantly outperforms existing lifelong learning methods for deep networks, but also achieves the same level of performance as the batch counterparts with substantially fewer number of parameters. Further, the obtained network fine-tuned on all tasks obtained significantly better performance over the batch models, which shows that it can be used to estimate the optimal network structure even when all tasks are available in the first place.
Deep Learning: concepts and use cases (October 2018)Julien SIMON
An introduction to Deep Learning theory
Neurons & Neural Networks
The Training Process
Backpropagation
Optimizers
Common network architectures and use cases
Convolutional Neural Networks
Recurrent Neural Networks
Long Short Term Memory Networks
Generative Adversarial Networks
Getting started
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
2. KBS Development Stage 1: analysis of the problem that produces a representation of the problem that can be manipulated by the reasoning system - this representation is often a set of attribute values. Stage 2: developing the reasoning mechanism that manipulates the problem representation to produce a solution.
23. ANN Processing Element (PE) Summation - gives PE’s activation level Transfer function - modifies the activation level to produce a reasonable output value (e.g. 0-1) .
24.
25.
26. Overfitting Training time Error In-sample error Generalisation error Too much training will result in a ( k -NN or ANN) model that makes minimal errors on the training data (memorises), but no longer generalises well. Beware.
27. ANN Development Collect data Separate into training and test sets Define a network structure Select a learning algorithm Set parameters, values, weights Transform data to network inputs Start training, revise weights Stop and test Use the network for new cases. Get more better data Reseparate Redefine structure Select another algorithm Reset Reset