Slides presented during the Datascience Meetup @Sentiance. Based on the following paper:
"Improving Language Modeling using Densely Connected Recurrent Neural Networks".
See http://www.fredericgodin.com/publications/ for more info.
Transformer modality is an established architecture in natural language processing that utilizes a framework of self-attention with a deep learning approach.
This presentation was delivered under the mentorship of Mr. Mukunthan Tharmakulasingam (University of Surrey, UK), as a part of the ScholarX program from Sustainable Education Foundation.
This Edureka Recurrent Neural Networks tutorial will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs. The last section includes a use-case of LSTM to predict the next word using a sample short story
Below are the topics covered in this tutorial:
1. Why Not Feedforward Networks?
2. What Are Recurrent Neural Networks?
3. Training A Recurrent Neural Network
4. Issues With Recurrent Neural Networks - Vanishing And Exploding Gradient
5. Long Short-Term Memory Networks (LSTMs)
6. LSTM Use-Case
In this project, we propose methods for semantic segmentation with the deep learning state-of-the-art models. Moreover,
we want to filterize the segmentation to the specific object in specific application. Instead of concentrating on unnecessary objects we
can focus on special ones and make it more specialize and effecient for special purposes. Furtheromore, In this project, we leverage
models that are suitable for face segmentation. The models that are used in this project are Mask-RCNN and DeepLabv3. The
experimental results clearly indicate that how illustrated approach are efficient and robust in the segmentation task to the previous work
in the field of segmentation. These models are reached to 74.4 and 86.6 precision of Mean of Intersection over Union. The visual
Results of the models are shown in Appendix part.
#PR12 #PR366
안녕하세요 논문 읽기 모임 PR-12의 366번째 논문리뷰입니다.
올해가 AlexNet이 나온지 10주년이 되는 해네요.
AlexNet이 2012년에 혜성처럼 등장한 이후, Solve computer vision problem = Use CNN이 공식처럼 사용되던 2010년대가 가고
2020년대 들어서 ViT의 등장을 시작으로 Transformer 기반의 network들이 CNN의 자리를 위협하고 상당부분 이미 뺏어간 상황입니다.
2020년대에 CNN의 가야할 길은 어디일까요?
Inductive bias가 적은 Transformer가 대용량의 데이터로 학습하면 항상 CNN보다 더 낫다는 건 진실일까요?
이 논문에서는 2020년대를 위한 CNN이라는 제목으로 ConvNeXt라는 새로운(?) architecture를 제안합니다.
사실 새로운 건 없고 그동안 있었던 것들과 Transformer에서 적용한 것들을 copy해와서 CNN에 적용해보았는데요,
Transformer보다 성능도 좋고 속도도 빠른 결과가 나왔다고 합니다.
결과에 대해서 약간의 논란이 twitter 상에서 나오고 있는데 이 부분 포함해서 자세한 내용은 영상을 통해서 보실 수 있습니다.
늘 재밌게 봐주시고 좋아요 댓글 구독 해주시는 분들께 감사드립니다 :)
논문링크: https://arxiv.org/abs/2201.03545
영상링크: https://youtu.be/Mw7IhO2uBGc
Transformer modality is an established architecture in natural language processing that utilizes a framework of self-attention with a deep learning approach.
This presentation was delivered under the mentorship of Mr. Mukunthan Tharmakulasingam (University of Surrey, UK), as a part of the ScholarX program from Sustainable Education Foundation.
This Edureka Recurrent Neural Networks tutorial will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs. The last section includes a use-case of LSTM to predict the next word using a sample short story
Below are the topics covered in this tutorial:
1. Why Not Feedforward Networks?
2. What Are Recurrent Neural Networks?
3. Training A Recurrent Neural Network
4. Issues With Recurrent Neural Networks - Vanishing And Exploding Gradient
5. Long Short-Term Memory Networks (LSTMs)
6. LSTM Use-Case
In this project, we propose methods for semantic segmentation with the deep learning state-of-the-art models. Moreover,
we want to filterize the segmentation to the specific object in specific application. Instead of concentrating on unnecessary objects we
can focus on special ones and make it more specialize and effecient for special purposes. Furtheromore, In this project, we leverage
models that are suitable for face segmentation. The models that are used in this project are Mask-RCNN and DeepLabv3. The
experimental results clearly indicate that how illustrated approach are efficient and robust in the segmentation task to the previous work
in the field of segmentation. These models are reached to 74.4 and 86.6 precision of Mean of Intersection over Union. The visual
Results of the models are shown in Appendix part.
#PR12 #PR366
안녕하세요 논문 읽기 모임 PR-12의 366번째 논문리뷰입니다.
올해가 AlexNet이 나온지 10주년이 되는 해네요.
AlexNet이 2012년에 혜성처럼 등장한 이후, Solve computer vision problem = Use CNN이 공식처럼 사용되던 2010년대가 가고
2020년대 들어서 ViT의 등장을 시작으로 Transformer 기반의 network들이 CNN의 자리를 위협하고 상당부분 이미 뺏어간 상황입니다.
2020년대에 CNN의 가야할 길은 어디일까요?
Inductive bias가 적은 Transformer가 대용량의 데이터로 학습하면 항상 CNN보다 더 낫다는 건 진실일까요?
이 논문에서는 2020년대를 위한 CNN이라는 제목으로 ConvNeXt라는 새로운(?) architecture를 제안합니다.
사실 새로운 건 없고 그동안 있었던 것들과 Transformer에서 적용한 것들을 copy해와서 CNN에 적용해보았는데요,
Transformer보다 성능도 좋고 속도도 빠른 결과가 나왔다고 합니다.
결과에 대해서 약간의 논란이 twitter 상에서 나오고 있는데 이 부분 포함해서 자세한 내용은 영상을 통해서 보실 수 있습니다.
늘 재밌게 봐주시고 좋아요 댓글 구독 해주시는 분들께 감사드립니다 :)
논문링크: https://arxiv.org/abs/2201.03545
영상링크: https://youtu.be/Mw7IhO2uBGc
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Intro to selective search for object proposals, rcnn family and retinanet state of the art model deep dives for object detection along with MAP concept for evaluating model and how does anchor boxes make the model learn where to draw bounding boxes
Recurrent Neural Networks are popular Deep Learning models that have shown great promise to achieve state-of-the-art results in many tasks like Computer Vision, NLP, Finance and much more. Although being models proposed several years ago, RNN have gained popularity recently. In this talk, we will review how these models evolved over the years, dissection of RNN, current applications and its future.
ConvNeXt: A ConvNet for the 2020s explainedSushant Gautam
Explained here: https://youtu.be/aBvDPL1jFnI
In Nepali
A ConvNet for the 2020s (Zhuang Liu et al.)
ComvNeXt paper
Deep Learning for Visual Intelligence
Sushant Gautam
MSCIISE
Department of Electronics and Computer Engineering
Institute of Engineering, Thapathali Campus
13 March 2022
To all the authors (obviously!!)
1. Jinwon Lee's slides at https://www.slideshare.net/JinwonLee9/pr366-a-convnet-for-2020s?qid=274bc524-23ae-4c13-b03b-0d2416976ad5&v=&b=&from_search=1
2. Letitia from AI Coffee Break: https://www.youtube.com/watch?v=SndHALawoag
I even edited some of her hard visual works and put them as a slide. :(
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
NBDT : Neural-backed Decision Tree 2021 ICLRtaeseon ryu
안녕하세요 딥러닝 논문읽기 모임 입니다.
오늘 소개 드릴 논문은 2021년 ICLR 에 억셉된 NBDT : Neural-backed Decision Tree 라는 논문 입니다
초록 :
Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use. In response, previous work combines decision trees with deep learning, yielding models that (1) sacrifice interpretability for accuracy or (2) sacrifice accuracy for interpretability. We forgo this dilemma by jointly improving accuracy and interpretability using Neural-Backed Decision Trees (NBDTs). NBDTs replace a neural network's final linear layer with a differentiable sequence of decisions and a surrogate loss. This forces the model to learn high-level concepts and lessens reliance on highly-uncertain decisions, yielding (1) accuracy: NBDTs match or outperform modern neural networks on CIFAR, ImageNet and better generalize to unseen classes by up to 16%. Furthermore, our surrogate loss improves the original model's accuracy by up to 2%. NBDTs also afford (2) interpretability: improving human trustby clearly identifying model mistakes and assisting in dataset debugging. Code and pretrained NBDTs are at this https URL.
오늘 논문 리뷰를 이미지 처리팀 안종식님이 자세하고 친절한 리뷰 도와주셨습니다.
감사합니다
문의 : tfkeras@kakao.com
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Intro to selective search for object proposals, rcnn family and retinanet state of the art model deep dives for object detection along with MAP concept for evaluating model and how does anchor boxes make the model learn where to draw bounding boxes
Recurrent Neural Networks are popular Deep Learning models that have shown great promise to achieve state-of-the-art results in many tasks like Computer Vision, NLP, Finance and much more. Although being models proposed several years ago, RNN have gained popularity recently. In this talk, we will review how these models evolved over the years, dissection of RNN, current applications and its future.
ConvNeXt: A ConvNet for the 2020s explainedSushant Gautam
Explained here: https://youtu.be/aBvDPL1jFnI
In Nepali
A ConvNet for the 2020s (Zhuang Liu et al.)
ComvNeXt paper
Deep Learning for Visual Intelligence
Sushant Gautam
MSCIISE
Department of Electronics and Computer Engineering
Institute of Engineering, Thapathali Campus
13 March 2022
To all the authors (obviously!!)
1. Jinwon Lee's slides at https://www.slideshare.net/JinwonLee9/pr366-a-convnet-for-2020s?qid=274bc524-23ae-4c13-b03b-0d2416976ad5&v=&b=&from_search=1
2. Letitia from AI Coffee Break: https://www.youtube.com/watch?v=SndHALawoag
I even edited some of her hard visual works and put them as a slide. :(
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
NBDT : Neural-backed Decision Tree 2021 ICLRtaeseon ryu
안녕하세요 딥러닝 논문읽기 모임 입니다.
오늘 소개 드릴 논문은 2021년 ICLR 에 억셉된 NBDT : Neural-backed Decision Tree 라는 논문 입니다
초록 :
Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use. In response, previous work combines decision trees with deep learning, yielding models that (1) sacrifice interpretability for accuracy or (2) sacrifice accuracy for interpretability. We forgo this dilemma by jointly improving accuracy and interpretability using Neural-Backed Decision Trees (NBDTs). NBDTs replace a neural network's final linear layer with a differentiable sequence of decisions and a surrogate loss. This forces the model to learn high-level concepts and lessens reliance on highly-uncertain decisions, yielding (1) accuracy: NBDTs match or outperform modern neural networks on CIFAR, ImageNet and better generalize to unseen classes by up to 16%. Furthermore, our surrogate loss improves the original model's accuracy by up to 2%. NBDTs also afford (2) interpretability: improving human trustby clearly identifying model mistakes and assisting in dataset debugging. Code and pretrained NBDTs are at this https URL.
오늘 논문 리뷰를 이미지 처리팀 안종식님이 자세하고 친절한 리뷰 도와주셨습니다.
감사합니다
문의 : tfkeras@kakao.com
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Slides of Nathan Piasco ICRA 2019 oral presentation about the paper "Learning Scene Geometry for Visual Localization in Challenging Conditions". Best paper in Robot Vision Finalist
Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. However, traditionally machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph. In this talk I will discuss methods that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. I will provide a conceptual review of key advancements in this area of representation learning on graphs, including random-walk based algorithms, and graph convolutional networks.
HardNet: Convolutional Network for Local Image DescriptionDmytro Mishkin
We introduce a novel loss for learning local feature descriptors which is inspired by the Lowe's matching criterion for SIFT. We show that the proposed loss that maximizes the distance between the closest positive and closest negative patch in the batch is better than complex regularization methods; it works well for both shallow and deep convolution network architectures. Applying the novel loss to the L2Net CNN architecture results in a compact descriptor -- it has the same dimensionality as SIFT (128) that shows state-of-art performance in wide baseline stereo, patch verification and instance retrieval benchmarks. It is fast, computing a descriptor takes about 1 millisecond on a low-end GPU.
ResNet (short for Residual Network) is a deep neural network architecture that has achieved significant advancements in image recognition tasks. It was introduced by Kaiming He et al. in 2015.
The key innovation of ResNet is the use of residual connections, or skip connections, that enable the network to learn residual mappings instead of directly learning the desired underlying mappings. This addresses the problem of vanishing gradients that commonly occurs in very deep neural networks.
In a ResNet, the input data flows through a series of residual blocks. Each residual block consists of several convolutional layers followed by batch normalization and rectified linear unit (ReLU) activations. The original input to a residual block is passed through the block and added to the output of the block, creating a shortcut connection. This addition operation allows the network to learn residual mappings by computing the difference between the input and the output.
By using residual connections, the gradients can propagate more effectively through the network, enabling the training of deeper models. This enables the construction of extremely deep ResNet architectures with hundreds of layers, such as ResNet-101 or ResNet-152, while still maintaining good performance.
ResNet has become a widely adopted architecture in various computer vision tasks, including image classification, object detection, and image segmentation. Its ability to train very deep networks effectively has made it a fundamental building block in the field of deep learning.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
ResNet, short for "Residual Network," is a type of deep neural network architecture that was introduced by Microsoft researchers in 2015. ResNet is designed to address the problem of vanishing gradients, which can occur in deep neural networks that are many layers deep.
The main innovation in ResNet is the use of residual connections, also known as skip connections. These connections allow information from earlier layers of the network to bypass some of the later layers and be directly fed into the later layers. This helps to ensure that the gradient signal from the output can propagate back through the network during training, which can help to prevent the vanishing gradient problem.
ResNet has been shown to be very effective at image recognition and other computer vision tasks. It has achieved state-of-the-art performance on a number of benchmark datasets, such as ImageNet. Since its introduction, many variations and improvements to the original ResNet architecture have been proposed, including ResNeXt, Wide ResNet, and Residual Attention Network (RANet).
Recent Progress on Object Detection_20170331Jihong Kang
This slide provides a brief summary of recent progress on object detection using deep learning.
The concept of selected previous works(R-CNN series/YOLO/SSD) and 6 recent papers (uploaded to the Arxiv between Dec/2016 and Mar/2017) are introduced in this slide.
Most papers are focusing on improving the performance of small object detection.
Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They...fgodin
Slides of my talk at EMNLP 2018:
"Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules?"
Character-level features are currently used in different neural network-based natural language processing algorithms. However, little is known about the character-level patterns those models learn. Moreover, models are often compared only quantitatively while a qualitative analysis is missing. In this paper, we investigate which character-level patterns neural networks learn and if those patterns coincide with manually-defined word segmentations and annotations. To that end, we extend the contextual decomposition (Murdoch et al., 2018) technique to convolutional neural networks which allows us to compare convolutional neural networks and bidirectional long short-term memory networks. We evaluate and compare these models for the task of morphological tagging on three morphologically different languages and show that these models implicitly discover understandable linguistic rules.
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
7. Fréderic Godin - Skip, residual and densely connected RNN architectures
Stacking recurrent neural networks
7
t=1 t=2 t=3 t=4
word1 word2 word3 word4
Deep in time
...Deep
in height
8. Fréderic Godin - Skip, residual and densely connected RNN architectures
Vanishing gradients
- When updating the weights using backpropagation, the
gradient tends to vanish with every neuron it crosses
- Often caused by the activation function
8
9. Fréderic Godin - Skip, residual and densely connected RNN architectures
Backpropagating through stacked RNNs
9
t=1 t=2 t=3 t=4
word1 word2 word3 word4
Backpropagation in time
...
Back-
propagation
in height
10. Fréderic Godin - Skip, residual and densely connected RNN architectures
Mitigating the vanishing gradient problem
In time: Long Short-Term Memory (LSTM)
10
In height:
̶ Many techniques exist in convolutional neural networks
̶ This talk: can we apply them in RNNs?
Key equation to model
depth in time
12. Fréderic Godin - Skip, residual and densely connected RNN architectures
Skip connection
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Layer 2
Merge 1,2
Out 1
A direct connection between 2
non-consecutive layers
- No vanishing gradient
- 2 main flavors
- Concatenative skip
connections
- Additive skip connections
Layer 3
Layer 1
13. Fréderic Godin - Skip, residual and densely connected RNN architectures
(Concatenative) skip connection
13
Concatenate output of previous
layer and skip connection
Advantage:
Provides the output of first layer
to third layer without altering it
Disadvantage:
Doubles the input size
Layer 2
Out 2
Out 1
Layer 3
Layer 1
Out 1
14. Fréderic Godin - Skip, residual and densely connected RNN architectures
Additive skip connection (Residual connection)
Originates from image
classification domain
Residual connection is defined as:
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Layer 2
Out 1 + 2
Out 1
Layer 3
Layer 1
“Residue”
Out 1 + 2 Layer 2 Out 1
15. Fréderic Godin - Skip, residual and densely connected RNN architectures
Residual connections do not
make sense in RNNs
Layer 2 also depends on h(t-1)
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Layer 2
Out 1 + 2
Out 1
Layer 3
Layer 1
Additive skip connection (Residual connection)
in RNN
Additive skip connection
Out 1 + 2 Layer 2 Out 1
h(t-1) ht
y
x
16. Fréderic Godin - Skip, residual and densely connected RNN architectures 16
Layer 2
Out 1 + 2
Out 1
Layer 3
Layer 1
Additive skip connection
Sum output of previous layer and
skip connection
Advantage:
Input size to next layer does not
increase
Disadvantage:
Can create noisy input to next layer
17. Fréderic Godin - Skip, residual and densely connected RNN architectures
Densely connecting layers
Add a skip connection between every
output and every input of every layer
Advantage:
- Direct paths between every layer
- Hierarchy of features as input to
every layer
Disadvantage: (L-1)*L connections
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Layer 2
Out 2
Out 1
Layer 3
Layer 1
Out 1
Out 3
Layer 4
Out 2Out 1
19. Fréderic Godin - Skip, residual and densely connected RNN architectures
Language modeling
Building a model which captures statistical characteristics of
a language:
In practice: predicting next word in a sentence
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26. Fréderic Godin - Skip, residual and densely connected RNN architectures
Conclusion
Densely connecting all layers improves language modeling
performance
Avoids vanishing gradients
Creates hierarchy of features, available
to each layer
We use six times fewer parameters to obtain the same result
as a stacked LSTM
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27. Fréderic Godin - Skip, residual and densely connected RNN architectures
Q&A
Also more details in our publication:
Fréderic Godin, Joni Dambre & Wesley De Neve
“Improving Language Modeling using Densely Connected
Recurrent Neural Networks”
https://arxiv.org/abs/1707.06130
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28. Fréderic Godin
Ph.D. Researcher Deep Learning
IDLab
E frederic.godin@ugent.be
@frederic_godin
www.fredericgodin.com
idlab.technology / idlab.ugent.be
