Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
http://arxiv.org/abs/1609.08144
を読んでみたので、簡単にまとめました。間違い等は是非ご指摘ください。
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
http://arxiv.org/abs/1609.08144
を読んでみたので、簡単にまとめました。間違い等は是非ご指摘ください。
Introduction to NLP with some practical exercises (tokenization, keyword extraction, topic modelling) using Python libraries like NLTK, Gensim and TextBlob, plus a general overview of the field.
Introduction to NLP with some practical exercises (tokenization, keyword extraction, topic modelling) using Python libraries like NLTK, Gensim and TextBlob, plus a general overview of the field.
“Automatically learning multiple levels of representations of the underlying distribution of the data to be modelled”
Deep learning algorithms have shown superior learning and classification performance.
In areas such as transfer learning, speech and handwritten character recognition, face recognition among others.
(I have referred many articles and experimental results provided by Stanford University)
Beyond the Symbols: A 30-minute Overview of NLPMENGSAYLOEM1
This presentation delves into the world of Natural Language Processing (NLP), exploring its goal to make human language understandable to machines. The complexities of language, such as ambiguity and complex structures, are highlighted as major challenges. The talk underscores the evolution of NLP through deep learning methodologies, leading to a new era defined by large-scale language models. However, obstacles like low-resource languages and ethical issues including bias and hallucination are acknowledged as enduring challenges in the field. Overall, the presentation provides a condensed, yet comprehensive view of NLP's accomplishments and ongoing hurdles.
Deep Content Learning in Traffic Prediction and Text ClassificationHPCC Systems
As part of the 2018 HPCC Systems Community Day event:
In this talk, Jingqing will introduce recent advances at the Data Science Institute, Imperial College London, and focus on a general framework named Deep Content Learning. Two recent projects will be discussed as examples. In the traffic prediction project, we released a new large-scale traffic dataset with auxiliary information including search queries from Baidu Map app and proposed hybrid models to achieve state-of-the-art prediction accuracy. The other project on zero-shot text classification integrated semantic knowledge and used a two-phase architecture to tackle the challenging zero-shot learning in textual data. The integration of TensorLayer and HPCC Systems will be discussed in the talk.
Jingqing Zhang is a 1st-year PhD (HiPEDS) at Data Science Institute, Imperial College London under the supervision of Prof. Yi-Ke Guo. His research interest includes Text Mining, Data Mining, Deep Learning and their applications. He received his MRes degree in Computing from Imperial College with Distinction in 2017 and BEng in Computer Science and Technology from Tsinghua University in 2016.
Deep Learning for Natural Language ProcessingJonathan Mugan
Deep Learning represents a significant advance in artificial intelligence because it enables computers to represent concepts using vectors instead of symbols. Representing concepts using vectors is particularly useful in natural language processing, and this talk will elucidate those benefits and provide an understandable introduction to the technologies that make up deep learning. The talk will outline ways to get started in deep learning, and it will conclude with a discussion of the gaps that remain between our current technologies and true computer understanding.
This presentation on Recurrent Neural Network will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case implementation of LSTM (Long short term memory). Neural networks used in Deep Learning consists of different layers connected to each other and work on the structure and functions of the human brain. It learns from huge volumes of data and used complex algorithms to train a neural net. The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this presentation and understand what is RNN and how does it actually work.
Below topics are explained in this recurrent neural networks tutorial:
1. What is a neural network?
2. Popular neural networks?
3. Why recurrent neural network?
4. What is a recurrent neural network?
5. How does an RNN work?
6. Vanishing and exploding gradient problem
7. Long short term memory (LSTM)
8. Use case implementation of LSTM
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
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Learn more at: https://www.simplilearn.com/
발표자: 조경현 (NYU 교수)
Kyunghyun Cho is an assistant professor of computer science and data science at New York University.
He was a postdoctoral fellow at University of Montreal until summer 2015, and received PhD and MSc degrees from Aalto University early 2014.
He tries best to find a balance among machine learning, natural language processing and life, but often fails to do so.
개요:
There are three axes along which advances in machine learning and deep learning happen. They are (1) network architectures, (2) learning algorithms and (3) spatio-temporal abstraction.
In this talk, I will describe a set of research topics I’ve pursued in each of these axes.
- For network architectures, I will describe how recurrent neural networks, which were largely forgotten during 90s and early 2000s, have evolved over time and have finally become a de facto standard in machine translation.
- I continue on to discussing various learning paradigms, how they related to each other, and how they are combined in order to build a strong learning system. Along this line, I briefly discuss my latest research on designing a query-efficient imitation learning algorithm for autonomous driving.
- Lastly, I present my view on what it means to be a higher-level learning system. Under this view each and every end-to-end trainable neural network serves as a module, regardless of how they were trained, and interacts with each other in order to solve a higher-level task.
I will describe my latest research on trainable decoding algorithm as a first step toward building such a framework.
발표영상: https://youtu.be/soZXAH3leeQ (본 발표는 영어로 진행됩니다.)
Introductory seminar on NLP for CS sophomores. Presented to Texas A&M's Fall 2022 CSCE181 class. Slides are a bit redundant due to compatibility issues :\
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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/
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
11. People think ……
l Transcribe spoken content into text by speech recognition
Speech
Recognition Models
Text
Retrieval
Result
Text
Retrieval
Query
learner
l Use text retrieval approach to search the transcriptions
Spoken
Content
Black Box
13. • Good spoken content retrieval needs good speech recognition
system.
• In real application, such high quality recognition models are
not available
• Ex, YouTube
• Different languages/accents
• Different recording environments
• Hope for spoken content retrieval
• Don’t completely rely on accurate speech recognition
• Accurate spoken content retrieval, even under poor speech
recognition
Problem?
15. Beyond Cascading Speech
Recogni1on and Text Retrieval
• 5 direc4ons
• Modified Speech Recogni4on for Retrieval Purposes
• Exploi4ng Informa4on not present in ASR outputs
• Directly Matching on Acous4c Level without ASR
• Seman4c Retrieval of Spoken Content
• Interac4ve Retrieval and Efficient Presenta4on of
Retrieved Objects
Overview paper "Spoken Content Retrieval —Beyond
Cascading Speech Recogni4on with Text Retrieval"
http://speech.ee.ntu.edu.tw/~tlkagk/paper/Overview.pdf
24. Speech Summariza1on
Retrieved
Audio File
Summary
Select the most informative
segments to form a compact version
1 hour long
10 minutes
Extrac've Summaries
Ref: http://speech.ee.ntu.edu.tw/
~tlkagk/courses/MLDS_2015/
Structured%20Lecture/Summarization
%20Hidden_2.ecm.mp4/index.html
25. Speech Summariza1on
• 用自己的話寫 summary (Abstrac4ve Summaries)
• Machine learns to do abstrac4ve summariza4on
from 2,000,000 training examples
,
, , , ,
; ……
Human
Machine
台大電機系 盧柏儒、徐翊祥
台大資工系 葉正杰、周儒杰
(助教:余朗祺)
29. Speech Ques1on Answering
• Machine answers ques4ons based on the
informa4on in spoken content
What is a possible origin
of Venus’ clouds?
……… answer
30. Speech Ques1on Answering
• TOEFL Listening Comprehension Test by Machine
• Example:
Ques4on: “ What is a possible origin of Venus’ clouds? ”
Audio Story:
Choices:
(A) gases released as a result of volcanic activity
(B) chemical reactions caused by high surface temperatures
(C) bursts of radio energy from the plane's surface
(D) strong winds that blow dust into the atmosphere
(The original story is 5 min long.)
31. Simple Baselines
Accuracy (%)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Naive Approaches
random
(4) 選 seman4c 和其他
選項最像的選項
(2) select the shortest
choice as answer
Experimental setup:
717 for training,
124 for validation, 122 for
testing
51. Audio Word to Vector
• Consider audio segment corresponding to an
unknown word
Deep
Learning
with
(助教:沈家豪)
52. Audio Word to Vector
• The audio segments corresponding to words with
similar pronuncia4ons are close to each other.
Deep
Learning
53. Audio Word to Vector
• The audio segments corresponding to words with
similar pronuncia4ons are close to each other.
ever
ever
never
never
never
dog
dog
dogs
Deep
Learning
58. Spoken Content Retrieval without
Speech Recognition
user
“US President”
spoken query
[Hazen, ASRU 09]
[Zhang Glass, ASRU 09]
[Chan Lee, Interspeech 10]
[Zhang Glass, ICASSP 11]
[Gupta, Interspeech 11]
[Zhang Glass, Interspeech 11]
[Zhang Glass, ASRU 09]
[Huijbregts, ICASSP 11]
[Chan Lee, Interspeech 11]
Computing similarity between spoken queries and audio
files on signal level
Spoken Content
Handheld
device
59. Spoken Content Retrieval without
Speech Recognition
• Why spoken content retrieval without speech
recognition?
• Lots of audio files in different languages on the
Internet
• Most languages have little annotated data for
training speech recognition systems.
• Some audio files are produced in several different
of languages
• Some languages even do not have text