Regular expressions are a powerful and standardized way of searching, replacing, and parsing text with complex patterns of characters. String have methods of searching indexfindand countreplacing replaceand parsing splitbut they are limited to the simplest of cases:. Regular expressions are supported by re module.

You can get an overview of the available functions and their arguments by just reading the summary of the re module. This series of examples was inspired by a real-life problem I had in my day job several years ago, when I needed to scrub and standardize street addresses exported from a legacy system before importing them into a newer system.

To solve the problem of addresses with more than one 'ROAD' substring, you could resort to something like this:.

Not all addresses included a street designation at all; some just ended with the street name. What I really want was to match 'ROAD' when it was at the end of the string and it was its own whole word, not a part of some larger word. I soon found more cases that contradicted my logic. You may also have seen them in outlines and bibliographical references. IV means 4VI means 6. IIV would not mean 3and it is not even a valid Roman numeral.

For numbers and higher, the thousands are represented by a series of M characters. The hundreds place is more difficult than the thousands, because there are several mutually exclusive ways it would be expressed, depending on its value. In the previous section, you were dealing with a pattern where the same character could be repeated up to three times.

There is another way to express this in regular expression. Example 7. A verbose regular expression is different from a compact regular expression in two ways:. When a regular expression does match, you can pick out specific pieces of it. You can find out what matched where. In each of these case, I need to know that the area code wasthe trunk wasand the rest of the phone number was For those with an extension, I need to know that the extension was In this case, you defined three groups, one with three digits, one with three digits, and one with four digits.

This regular expression is almost identical to the previous one: you match the beginning of the string, then a remembered group of three digits, then a hyphen, then a remembered group of three digits, then a hyphen, then a remembered group of four digits,then match another hyphen, and a remembered group of one or more digits, then the end of the string.

Table Of Contents Regular Expression 7. Diving In 7. Case Study: Street Addresses 7.

Feng Zeyuan VS Liu Chuang - Joy Cup 2019 World Chinese Pool Masters China Classic

Case Study: Roman Numerals 7. Verbose Regular Expressions 7.

Case Study: Parsing Phone Numbers 7. Further Reading About this site About search this site. Those limitations are also true for replacing method and parsing method. Key Func re. Tip Always using raw strings when dealing with regular expressions.

Subtraction rule: A smaller number in front of a larger number means subtraction, all else means addition: IV means 4VI means 6 You would not put more than one smaller number in front of a larger number to subtract: IIV would not mean 3and it is not even a valid Roman numeral.

The number 90 is XCis CM. The fives characters cannot be repeated: Number 10 is always represented as Xnever as VV. Number is always Cnever LL.I work on the mathematical foundations of machine learning and optimization, and apply them to deep learning, theoretical computer science, operations research, and statistics.

I am also interested in the mathematical modeling for physical, social, economic, and biological systems. JMLR Math.

I would love to thank my wonderful collaborators without whom these results below would never have been accomplished. In inverse chronological order:. My middle name "Allen" was legally merged into my family name in Feburarybecoming "Allen-Zhu". How does a layer ResNet learn a high-complexity classifier using relatively few training examples and short training time? We present a theory towards explaining this in terms of hierarchical learning. We refer hierarchical learning as the learner learns to represent a complicated target function by decomposing it into a sequence of simpler functions to reduce sample and time complexity.

This paper formally analyzes how multi-layer neural networks can perform such hierarchical learning efficiently and automatically simply by applying stochastic gradient descent SGD. On the conceptual side, we present, to the best of our knowledge, the FIRST theory result indicating how very deep neural networks can still be sample and time efficient on certain hierarchical learning tasks, when NO KNOWN non-hierarchical algorithms such as kernel method, linear regression over feature mappings, tensor decomposition, sparse coding are efficient.

We establish a new principle called "backward feature correction", which we believe is the key to understand the hierarchical learning in multi-layer neural networks.

zeyuan hu (zeyuan)

Click here to show only equal first-author papers. Click here to show all papers. The experimental design problem concerns the selection of k points from a potentially large design pool of p-dimensional vectors, so as to maximize the statistical efficiency regressed on the selected k design points.

Except for the T-optimality, exact optimization is NP-hard.

Zeyuan Allen-Zhu

Can we more provide theoretical justifications for this gap? There is an influential line of work relating neural networks to kernels in the over-parameterized regime, proving that they can learn certain concept class that is also learnable by kernels, with similar test error. Yet, can we show neural networks provably learn some concept class better than kernels? We answer this positively in the PAC learning language. We prove neural networks can efficiently learn a notable class of functions, including those defined by three-layer residual networks with smooth activations, without any distributional assumption.

At the same time, we prove there are simple functions in this class that the test error obtained by neural networks can be much smaller than any "generic" kernel method, including neural tangent kernels, conjugate kernels, etc. The main intuition is that multi-layer neural networks can implicitly perform hierarchal learning using different layers, which reduces the sample complexity comparing to "one-shot" learning algorithms such as kernel methods.

The fundamental learning theory behind neural networks remains largely open. What classes of functions can neural networks actually learn?

Why doesn't the trained neural networks overfit when the it is overparameterized namely, having more parameters than statistically needed to overfit training data? In this work, we prove that overparameterized neural networks can learn some notable concept classes, including two and three-layer networks with fewer parameters and smooth activations. Moreover, the learning can be simply done by SGD stochastic gradient descent or its variants in polynomial time using polynomially many samples.

The sample complexity can also be almost independent of the number of parameters in the overparameterized network. Yet, in the foundational PAC learning language, what concept class can it learn? Moreover, how can the same recurrent unit simultaneously learn functions from different input tokens to different output tokens, without affecting each other?To protect your privacy, all features that rely on external API calls from your browser are turned off by default.

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While we did signal Twitter to not track our users by setting the "dnt" flagwe do not have any control over how Twitter uses your data. So please proceed with care and consider checking the Twitter privacy policy. Authors: no matches. Venues: no matches. Publications: no matches. Trier 1 Trier 2.How does a layer ResNet learn a high-complexity classifier using rel We propose a rank-k variant of the classical Frank-Wolfe algorithm to so Given a nonconvex function f x that is an average of n smooth functions The online problem of computing the top eigenvector is fundamental to ma We design a non-convex second-order optimization algorithm that is guara We solve principal component regression PCRup to a multiplicative ac We study streaming principal component analysis PCAthat is to find, We study k-GenEV, the problem of finding the top k generalized eigenvect We study k-SVD that is to obtain the first k singular vectors of a matri Nesterov's momentum trick is famously known for accelerating gradient de We consider the fundamental problem in non-convex optimization of effici The diverse world of machine learning applications has given rise to a p The amount of data available in the world is growing faster than our abi Accelerated coordinate descent is widely used in optimization due to its In this paper, we provide a novel construction of the linear-sized spect Many classical algorithms are found until several years later to outlive First-order methods play a central role in large-scale machine learning Given a subset S of vertices of an undirected graph G, the cut-improveme Motivated by applications of large-scale graph clustering, we study rand We propose a reduction for non-convex optimization that can 1 turn a s In convex stochastic optimization, convergence rates in terms of minimiz The problem of minimizing sum-of-nonconvex functions i.

This paper studies the problem of distributed stochastic optimization in The experimental design problem concerns the selection of k points from Model-free reinforcement learning RL algorithms, such as Q-learning, dModify char in another function. Generalized binary search. Cache, Lease, Consistency, Invalidation. State Machine Replication Approach. Lamport Clocks, Vector Clocks. Distributed System Reference Guide.

How to write binary search correctly. Introduction to Conditional Random Fields. MAW Chapter 8: Disjoint set. Understanding how function call works. Andrew Ng's ML Week 06, The tortoise and the hare.

Python case study: leetcode scraper. Solving recurrence relations part 2. Draw a Neural Network through Graphviz. Andrew Ng's ML Week 04 - Andrew Ng's ML Week 01 - Simple sorting algorithms. Introducing the "Andrew Ng's ML course study notes". MAW Chapter 7: Sorting writing questions. MAW: Chapter 6 Reflection. MAW Chapter 5: Hashing writing questions.Skip to search form Skip to main content You are currently offline.

Some features of the site may not work correctly. Zeyuan Hu. Follow Author Publications Citations Highly Influential Citations 1. Publications Influence. Claim Your Author Page. Ensure your research is discoverable on Semantic Scholar. Claiming your author page allows you to personalize the information displayed and manage publications all current information on this profile has been aggregated automatically from publisher and metadata sources.

Answering visual questions need acquire daily common knowledge and model the semantic connection among different parts in images, which is too difficult for VQA systems to learn from images with the … Continue Reading. Visual question answering VQA and image captioning require a shared body of general knowledge connecting language and vision. AbstractRegional sea surface temperature SST mode variabilities, especially the La Nina—like Pacific Ocean temperature pattern known as the negative phase of the interdecadal Pacific oscillation … Continue Reading.

The goal of our research is to contribute information about how useful the crowd is at anticipating stereotypes that may be biasing a data set without a researcher's knowledge. The results of the … Continue Reading. In recent years, deep Convolutional neural networks CNNs have made fantastic progress in static image recognition, but the ability to model motion information on behavioral video is weak.

Therefore, … Continue Reading. Log-structured Merge Tree LSM [17] is a data structure that is widely used in write-intensive storage system. However, it suffers from write amplifications, which can hinder the write throughput. Source code can be treated similar as corpus constructed by natural language Hindle et al. In this paper, we use the neural network model to study identifer naming convention problem.

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zeyuan hu (zeyuan)

So please proceed with care and consider checking the Twitter privacy policy. Authors: no matches. Venues: no matches.

Publications: no matches. Trier 2 Dagstuhl. Aditya Bhaskara. Wei Chen Zheng Chen Alessandro Chiesa. Rati Gelashvili. Michael I. Jonathan A. Silvio Lattanzi. Jerry Li Vahab S. Sasa Misailovic. Rafael Mendes de Oliveira.

zeyuan hu (zeyuan)

Lorenzo Orecchia. Ilya P.