1593440910

**TL;DR** *This is the first in a [series of posts] where I will discuss the evolution and future trends in the field of deep learning on graphs.*

Deep learning on graphs, also known as Geometric deep learning (GDL) [1], Graph representation learning (GRL), or relational inductive biases [2], has recently become one of the hottest topics in machine learning. While early works on graph learning go back at least a decade [3] if not two [4], it is undoubtedly the past few years’ progress that has taken these methods from a niche into the spotlight of the ML community and even to the popular science press (with *Quanta Magazine* running a series of excellent articles on geometric deep learning for the study of manifolds, drug discovery, and protein science).

Graphs are powerful mathematical abstractions that can describe complex systems of relations and interactions in fields ranging from biology and high-energy physics to social science and economics. Since the amount of graph-structured data produced in some of these fields nowadays is enormous (prominent examples being social networks like Twitter and Facebook), it is very tempting to try to apply deep learning techniques that have been remarkably successful in other data-rich settings.

There are multiple flavours to graph learning problems that are largely application-dependent. One dichotomy is between *node-wise* and *graph-wise* problems, where in the former one tries to predict properties of individual nodes in the graph (e.g. identify malicious users in a social network), while in the latter one tries to make a prediction about the entire graph (e.g. predict solubility of a molecule). Furthermore, like in traditional ML problems, we can distinguish between *supervised* and *unsupervised* (or *self-supervised*) settings, as well as *transductive* and *inductive* problems.

Similarly to convolutional neural networks used in image analysis and computer vision, the key to efficient learning on graphs is designing local operations with shared weights that do message passing [5] between every node and its neighbours. A major difference compared to classical deep neural networks dealing with grid-structured data is that on graphs such operations are *permutation-invariant*, i.e. independent of the order of neighbour nodes, as there is usually no canonical way of ordering them.

Despite their promise and a series of success stories of graph representation learning (among which I can selfishly list the [acquisition by Twitter] of the graph-based fake news detection startup Fabula AI I have founded together with my students), we have not witnessed so far anything close to the smashing success convolutional networks have had in computer vision. In the following, I will try to outline my views on the possible reasons and how the field could progress in the next few years.

**Standardised benchmarks **like ImageNet were surely one of the key success factors of deep learning in computer vision, with some [6] even arguing that data was more important than algorithms for the deep learning revolution. We have nothing similar to ImageNet in scale and complexity in the graph learning community yet. The [Open Graph Benchmark] launched in 2019 is perhaps the first attempt toward this goal trying to introduce challenging graph learning tasks on interesting real-world graph-structured datasets. One of the hurdles is that tech companies producing diverse and rich graphs from their users’ activity are reluctant to share these data due to concerns over privacy laws such as GDPR. A notable exception is Twitter that made a dataset of 160 million tweets with corresponding user engagement graphs available to the research community under certain privacy-preserving restrictions as part of the [RecSys Challenge]. I hope that many companies will follow suit in the future.

**Software libraries **available in the public domain played a paramount role in “democratising” deep learning and making it a popular tool. If until recently, graph learning implementations were primarily a collection of poorly written and scarcely tested code, nowadays there are libraries such as [PyTorch Geometric] or [Deep Graph Library (DGL)] that are professionally written and maintained with the help of industry sponsorship. It is not uncommon to see an implementation of a new graph deep learning architecture weeks after it appears on arxiv.

**Scalability** is one of the key factors limiting industrial applications that often need to deal with very large graphs (think of Twitter social network with hundreds of millions of nodes and billions of edges) and low latency constraints. The academic research community has until recently almost ignored this aspect, with many models described in the literature completely inadequate for large-scale settings. Furthermore, graphics hardware (GPU), whose happy marriage with classical deep learning architectures was one of the primary forces driving their mutual success, is not necessarily the best fit for graph-structured data. In the long run, we might need specialised hardware for graphs [7].

**Dynamic graphs **are another aspect that is scarcely addressed in the literature. While graphs are a common way of modelling complex systems, such an abstraction is often too simplistic as real-world systems are dynamic and evolve in time. Sometimes it is the temporal behaviour that provides crucial insights about the system. Despite some recent progress, designing graph neural network models capable of efficiently dealing with continuous-time graphs represented as a stream of node- or edge-wise events is still an open research question.

#deep-learning #representation-learning #network-science #graph-neural-networks #geometric-deep-learning #deep learning

1593440910

**TL;DR** *This is the first in a [series of posts] where I will discuss the evolution and future trends in the field of deep learning on graphs.*

Deep learning on graphs, also known as Geometric deep learning (GDL) [1], Graph representation learning (GRL), or relational inductive biases [2], has recently become one of the hottest topics in machine learning. While early works on graph learning go back at least a decade [3] if not two [4], it is undoubtedly the past few years’ progress that has taken these methods from a niche into the spotlight of the ML community and even to the popular science press (with *Quanta Magazine* running a series of excellent articles on geometric deep learning for the study of manifolds, drug discovery, and protein science).

Graphs are powerful mathematical abstractions that can describe complex systems of relations and interactions in fields ranging from biology and high-energy physics to social science and economics. Since the amount of graph-structured data produced in some of these fields nowadays is enormous (prominent examples being social networks like Twitter and Facebook), it is very tempting to try to apply deep learning techniques that have been remarkably successful in other data-rich settings.

There are multiple flavours to graph learning problems that are largely application-dependent. One dichotomy is between *node-wise* and *graph-wise* problems, where in the former one tries to predict properties of individual nodes in the graph (e.g. identify malicious users in a social network), while in the latter one tries to make a prediction about the entire graph (e.g. predict solubility of a molecule). Furthermore, like in traditional ML problems, we can distinguish between *supervised* and *unsupervised* (or *self-supervised*) settings, as well as *transductive* and *inductive* problems.

Similarly to convolutional neural networks used in image analysis and computer vision, the key to efficient learning on graphs is designing local operations with shared weights that do message passing [5] between every node and its neighbours. A major difference compared to classical deep neural networks dealing with grid-structured data is that on graphs such operations are *permutation-invariant*, i.e. independent of the order of neighbour nodes, as there is usually no canonical way of ordering them.

Despite their promise and a series of success stories of graph representation learning (among which I can selfishly list the [acquisition by Twitter] of the graph-based fake news detection startup Fabula AI I have founded together with my students), we have not witnessed so far anything close to the smashing success convolutional networks have had in computer vision. In the following, I will try to outline my views on the possible reasons and how the field could progress in the next few years.

**Standardised benchmarks **like ImageNet were surely one of the key success factors of deep learning in computer vision, with some [6] even arguing that data was more important than algorithms for the deep learning revolution. We have nothing similar to ImageNet in scale and complexity in the graph learning community yet. The [Open Graph Benchmark] launched in 2019 is perhaps the first attempt toward this goal trying to introduce challenging graph learning tasks on interesting real-world graph-structured datasets. One of the hurdles is that tech companies producing diverse and rich graphs from their users’ activity are reluctant to share these data due to concerns over privacy laws such as GDPR. A notable exception is Twitter that made a dataset of 160 million tweets with corresponding user engagement graphs available to the research community under certain privacy-preserving restrictions as part of the [RecSys Challenge]. I hope that many companies will follow suit in the future.

**Software libraries **available in the public domain played a paramount role in “democratising” deep learning and making it a popular tool. If until recently, graph learning implementations were primarily a collection of poorly written and scarcely tested code, nowadays there are libraries such as [PyTorch Geometric] or [Deep Graph Library (DGL)] that are professionally written and maintained with the help of industry sponsorship. It is not uncommon to see an implementation of a new graph deep learning architecture weeks after it appears on arxiv.

**Scalability** is one of the key factors limiting industrial applications that often need to deal with very large graphs (think of Twitter social network with hundreds of millions of nodes and billions of edges) and low latency constraints. The academic research community has until recently almost ignored this aspect, with many models described in the literature completely inadequate for large-scale settings. Furthermore, graphics hardware (GPU), whose happy marriage with classical deep learning architectures was one of the primary forces driving their mutual success, is not necessarily the best fit for graph-structured data. In the long run, we might need specialised hardware for graphs [7].

**Dynamic graphs **are another aspect that is scarcely addressed in the literature. While graphs are a common way of modelling complex systems, such an abstraction is often too simplistic as real-world systems are dynamic and evolve in time. Sometimes it is the temporal behaviour that provides crucial insights about the system. Despite some recent progress, designing graph neural network models capable of efficiently dealing with continuous-time graphs represented as a stream of node- or edge-wise events is still an open research question.

#deep-learning #representation-learning #network-science #graph-neural-networks #geometric-deep-learning #deep learning

1618317562

View more: https://www.inexture.com/services/deep-learning-development/

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#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services

1603735200

The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.

Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:

**Also Read:** Why Deep Learning DevCon Comes At The Right Time

**By Dipanjan Sarkar**

**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.

Read an interview with Dipanjan Sarkar.

**By Divye Singh**

**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.

**By Dongsuk Hong**

**About:** This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.

#opinions #attend dldc 2020 #deep learning #deep learning sessions #deep learning talks #dldc 2020 #top deep learning sessions at dldc 2020 #top deep learning talks at dldc 2020

1593880440

**TL;DR:** *In this post, I discuss how to design local and computationally efficient provably powerful graph neural networks that are not based on the Weisfeiler-Lehman tests hierarchy. This is the second in the series of posts on the expressivity of graph neural networks. See Part 1 describing the relation between graph neural networks and the Weisfeiler-Lehman graph isomorphism test. In Part 3, I will argue why we should abandon the graph isomorphism problem altogether.*

Recent groundbreaking papers [1–2] established the connection between graph neural networks and the graph isomorphism tests, observing the analogy between the message passing mechanism and the Weisfeiler-Lehman (WL) test [3]. WL test is a general name for a hierarchy of graph-theoretical polynomial-time iterative algorithms for determining graph isomorphism. The *k*-WL test recolours *k*-tuples of vertices of a graph at each step according to some neighbourhood aggregation rules and stops upon reaching a stable colouring. If the histograms of colours of the two graphs are not the same, the graphs are deemed not isomorphic; otherwise, the graphs are possibly (but not necessarily) isomorphic.

Message passing neural networks are at most as powerful as the 1-WL test (also known as node colour refinement), and thus unable to distinguish between even very simple instances of non-isomorphic graphs. For example, message passing neural networks cannot count triangles [4], a motif known to play an important role in social networks where it is associated with the clustering coefficient indicative of how “tightly knit” the users are [5]. It is possible to design more expressive graph neural networks that replicate the increasingly more powerful *k*-WL tests [2,6]. However, such architectures result in high complexity and large number of parameters, but most importantly, typically require non-local operations that make them impractical.

Examples of non-isomorphic graphs that cannot be distinguished by 1-WL but can be distinguished by 3-WL due to its capability of counting triangles.

Thus, provably powerful graph neural networks based on the Weisfeiler-Lehman hierarchy are either not very powerful but practical, or powerful but impractical [7]. I argue that there is a different simple way to design efficient and provably powerful graph neural networks, which we proposed in a new paper with Giorgos Bouritsas and Fabrizio Frasca [8].

**Graph Substructure Networks. **The idea is actually very simple and conceptually similar to positional encoding or graphlet descriptors [9]: we make the message passing mechanism aware of the local graph structure, allowing for computing messages differently depending on the topological relationship between the endpoint nodes. This is done by passing to message passing functions additional structural descriptors associated with each node [10], which are constructed by subgraph isomorphism counting. In this way, we can partition the nodes of the graph into different equivalence classes reflecting topological characteristics that are shared both between nodes in each graph individually and across different graphs.

We call this architecture Graph Substructure Network (GSN). It has the same algorithmic design and memory and computational complexity as standard message passing neural networks, with an additional pre-computation step in which the structural descriptors are constructed. The choice of the substructures to count is crucial both to the expressive power of GSNs and the computational complexity of the pre-computation step.

The worst-case complexity of counting substructures of size *k* in a graph with *n* nodes is 𝒪(*nᵏ*). Thus, it is similar to high-order graph neural network models or Morris [2] and Maron [6]. However, GSN has several advantages over these methods. First, for some types of substructures such as paths and cycles the counting can be done with significantly lower complexity. Secondly, the computationally expensive step is done only once as preprocessing and thus does not affect network training and inference that remain linear, the same way as in message-passing neural networks. The memory complexity in training and inference is linear as well. Thirdly and most importantly, the expressive power of GSN is different from *k*-WL tests and in some cases is stronger.

**How powerful are GSNs?** The substructure counting endows GSN with more expressive power than the standard message-passing neural networks. First, it is important to clarify that the expressive power of GSN depends on the graph substructures used. Same way as we have a hierarchy of *k*-WL tests, we might have different variants of GSNs based on counting one or more structures. Using structures more complex than star graphs, GSNs can be made strictly more powerful than 1-WL (or the equivalent 2-WL) and thus also more powerful than standard message passing architectures. With 4-cliques, GSN is at least no less powerful than 3-WL, as shown by the following example of strongly regular graphs on which GSN succeeds while 3-WL fails:

Example of non-isomorphic strongly regular graphs with 16 vertices and node degree 6, where every two adjacent vertices have 2 mutual neighbours, and every two non-adjacent vertices also have 2 mutual neighbours. The 3-WL test fails on this example, while GSN with 4-clique structure can distinguish between them. In the graph on the left (known as the Rook’s graph) each node participates in exactly one 4-clique. The graph on the right (Shrikhande graph) has maximum cliques of size 3 (triangles). Figure from [8].

More generally speaking, for various substructures of 𝒪(1) size, as long as they cannot be counted by 3-WL, there exist graphs where GSN succeeds and 3-WL fails [11]. While we could not find examples to the contrary, they might in principle exist — that is why our statement about the power of GSN is of a weak form, “at least not less powerful”.

This holds for larger *k* as well; a generalisation of strongly regular graphs in the above figure, called *k*-*isoregular*, are instances on which the (*k*+1)-WL test fails [12]. These examples can also be distinguished by GSN with appropriate structures. The expressive power of GSNs can thus be captured by the following figure:

GSN is outside the Weisfeiler-Lehman hierarchy. With the appropriate structure (e.g. cliques or cycles of certain size), it is likely to be made at least not less powerful than k-WL.

How powerful can GSN be in principle? This is still an open question. The Graph Reconstruction Conjecture [13] postulates the possibility of recovering a graph from all its node-deleted substructures. Thus, if the Reconstruction Conjecture is correct, a GSN with substructures of size _n_−1 would be able to correctly test isomorphism of any graphs. However, the Reconstruction Conjecture is currently proven only for graphs of size _n≤_11 [14], and second, such large structures would be impractical.

The more interesting question is whether a similar result exists for “small” structures (of 𝒪(1) size independent of the number of nodes *n*). Our empirical results show that GSN with small substructures such as cycles, paths, and cliques work for strongly regular graphs, which are known to be a tough nut for the Weisfeiler-Lehman tests.

#geometric-deep-learning #deep-learning #graph-neural-networks #graph-theory #machine-learning #deep learning

1593529260

In the previous blog, we looked into the fact why Few Shot Learning is essential and what are the applications of it. In this article, I will be explaining the Relation Network for Few-Shot Classification (especially for image classification) in the simplest way possible. Moreover, I will be analyzing the Relation Network in terms of:

- Effectiveness of different architectures such as Residual and Inception Networks
- Effects of transfer learning via using pre-trained classifier on ImageNet dataset

Moreover, effectiveness will be evaluated on the accuracy, time required for training, and the number of required training parameters.

Please watch the GitHub repository to check out the implementations and keep updated with further experiments.

In few shot classification, our objective is to design a method which can identify any object images by analyzing few sample images of the same class. Let’s the take one example to understand this. Suppose Bob has a client project to design a 5 class classifier, where 5 classes can be anything and these 5 classes can even change with time. As discussed in previous blog, collecting the huge amount of data is very tedious task. Hence, in such cases, Bob will rely upon few shot classification methods where his client can give few set of example images for each classes and after that his system can perform classification young these examples with or without the need of additional training.

In general, in few shot classification four terminologies (N way, K shot, support set, and query set) are used.

**N way:**It means that there will be total N classes which we will be using for training/testing, like 5 classes in above example.**K shot:**Here, K means we have only K example images available for each classes during training/testing.**Support set:**It represents a collection of all available K examples images from each classes. Therefore, in support set we have total N*K images.**Query set:**This set will have all the images for which we want to predict the respective classes.

At this point, someone new to this concept will have doubt regarding the need of support and query set. So, let’s understand it intuitively. Whenever humans sees any object for the first time, we get the rough idea about that object. Now, in future if we see the same object second time then we will compare it with the image stored in memory from the when we see it for the first time. This applied to all of our surroundings things whether we see, read, or hear. Similarly, to recognise new images from query set, we will provide our model a set of examples i.e., support set to compare.

And this is the basic concept behind Relation Network as well. In next sections, I will be giving the rough idea behind Relation Network and I will be performing different experiments on 102-flower dataset.

The Core idea behind Relation Network is to learn the generalized image representations for each classes using support set such that we can compare lower dimensional representation of query images with each of the class representations. And based on this comparison decide the class of each query images. Relation Network has two modules which allows us to perform above two tasks:

**Embedding module:**This module will extract the required underlying representations from each input images irrespective of the their classes.**Relation Module:**This module will score the relation of embedding of query image with each class embedding.

**Training/Testing procedure:**

We can define the whole procedure in just 5 steps.

- Use the support set and get underlying representations of each images using embedding module.
- Take the average of between each class images and get the single underlying representation for each class.
- Then get the embedding for each query images and concatenate them with each class’ embedding.
- Use the relation module to get the scores. And class with highest score will be the label of respective query image.
- [Only during training] Use MSE loss functions to train both (embedding + relation) modules.

Few things to know during the training is that we will use only images from the set of selective class, and during the testing, we will be using images from unseen classes. For example, from the 102-flower dataset, we will use 50% classes for training, and rest will be used for validation and testing. Moreover, in each episode, we will randomly select 5 classes to create the support and query set and follow the above 5 steps.

That is all need to know about the implementation point of view. Although the whole process is simple and easy to understand, I’ll recommend reading the published research paper, Learning to Compare: Relation Network for Few-Shot Learning, for better understanding.

#deep-learning #few-shot-learning #computer-vision #machine-learning #deep learning #deep learning