Improving Neural Architecture Search Image Classifiers via Ensemble Learning - implementation -

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Improving Neural Architecture Search Image Classifiers via Ensemble Learning by Vladimir Macko, Charles Weill, Hanna Mazzawi and Javier Gonzalvo‎

AdaNAS is an algorithm for learning an ensemble that improves the performance of neural architecture search models while having a similar parameter count as single large model. Our experiments demonstrate that these ensembles improve accuracy upon a single neural network of the same size. Our models achieve comparable results with the state-of-the-art on CIFAR-10 and set a new state-of-the-art on CIFAR-100.

An implementation is ehre: https://github.com/tensorflow/adanet/tree/master/research/improve_nas

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Degrees of Freedom Analysis of Unrolled Neural Networks

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Studying the great convergence !

Degrees of Freedom Analysis of Unrolled Neural Networks by Morteza Mardani, Qingyun Sun, Vardan Papyan, Shreyas Vasanawala, John Pauly, David Donoho

Unrolled neural networks emerged recently as an effective model for learning inverse maps appearing in image restoration tasks. However, their generalization risk (i.e., test mean-squared-error) and its link to network design and train sample size remains mysterious. Leveraging the Stein's Unbiased Risk Estimator (SURE), this paper analyzes the generalization risk with its bias and variance components for recurrent unrolled networks. We particularly investigate the degrees-of-freedom (DOF) component of SURE, trace of the end-to-end network Jacobian, to quantify the prediction variance. We prove that DOF is well-approximated by the weighted \textit{path sparsity} of the network under incoherence conditions on the trained weights. Empirically, we examine the SURE components as a function of train sample size for both recurrent and non-recurrent (with many more parameters) unrolled networks. Our key observations indicate that: 1) DOF increases with train sample size and converges to the generalization risk for both recurrent and non-recurrent schemes; 2) recurrent network converges significantly faster (with less train samples) compared with non-recurrent scheme, hence recurrence serves as a regularization for low sample size regimes.

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Distributed Learning with Random Features

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Distributed Learning with Random Features by Jian Li, Yong Liu, Weiping Wang

Distributed learning and random projections are the most common techniques in large scale nonparametric statistical learning. In this paper, we study the generalization properties of kernel ridge regression using both distributed methods and random features. Theoretical analysis shows the combination remarkably reduces computational cost while preserving the optimal generalization accuracy under standard assumptions. In a benign case, O(N−−√)partitions and O(N−−√) random features are sufficient to achieve O(1/N) learning rate, where N is the labeled sample size. Further, we derive more refined results by using additional unlabeled data to enlarge the number of partitions and by generating features in a data-dependent way to reduce the number of random features.

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NAS-Bench-101: Towards Reproducible Neural Architecture Search - implementation -

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NAS-Bench-101: Towards Reproducible Neural Architecture Search by Chris Ying, Aaron Klein, Esteban Real, Eric Christiansen, Kevin Murphy, Frank Hutter

Recent advances in neural architecture search (NAS) demand tremendous computational resources, which makes it difficult to reproduce experiments and imposes a barrier-to-entry to researchers without access to large-scale computation. We aim to ameliorate these problems by introducing NAS-Bench-101, the first public architecture dataset for NAS research. To build NAS-Bench-101, we carefully constructed a compact, yet expressive, search space, exploiting graph isomorphisms to identify 423k unique convolutional architectures. We trained and evaluated all of these architectures multiple times on CIFAR-10 and compiled the results into a large dataset of over 5 million trained models. This allows researchers to evaluate the quality of a diverse range of models in milliseconds by querying the pre-computed dataset. We demonstrate its utility by analyzing the dataset as a whole and by benchmarking a range of architecture optimization algorithms.

Data and code for NAS-Bench-101 is here: https://github.com/google-research/nasbench

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CfP: The 2019 Conference on Mathematical Theory of Deep Neural Networks (DeepMath 2019) Princeton Club, New York City, Oct 31-Nov 1 2019.

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Adam let me know of the following:

Dear Igor
I hope this email finds you well. Thank you for posing our conference on your blog earlier this year. As the deadline for submission is fast approaching (June 28th), I was hoping you would post once again. We have an amazing lineup of speakers (below) and I expect the subject matter is very much in line with your readership!
Cheers!

-Adam

Here is the announcement:

ANNOUNCEMENT
The 2019 Conference on Mathematical Theory of Deep Neural Networks (DeepMath 2019)
Princeton Club, New York City, Oct 31-Nov 1 2019.
Web: https://www.deepmath-conference.com/
======= Important Dates =======
Submission deadline for 1-page abstracts: June 28, 2019Notification: TBA.
Conference: Oct 31-Nov 1 2019.
======= Speakers =======
Sanjeev Arora (Princeton University, Keynote Speaker), Anima Anandkumar (CalTech), Yasaman Bahri (Google), Minmin Chen (Google), Michael Elad (Technion), Surya Ganguli (Stanford), Tomaso Poggio (MIT), David Schwab (CUNY), Shai Shalev-Shwartz (Hebrew University), Haim Sompolinsky (Hebrew University and Harvard), and Naftali Tishby (Hebrew University).
======= Call for Abstracts =======
In addition to these high-profile invited speakers, we invite 1-page non-archival abstract submissions. Abstracts will be reviewed double-blind and presented as posters.
To complement the wealth of conferences focused on applications, all submissions for DeepMath 2019 must target theoretical and mechanistic understanding of the underlying properties of neural networks.
Insights may come from any discipline and we encourage submissions from researchers working in computer science, engineering, mathematics, neuroscience, physics, psychology, statistics, or related fields.
Topics may address any area of deep learning theory, including architectures, computation, expressivity, generalization, optimization, representations, and may apply to any or all network types including fully connected, recurrent, convolutional, randomly connected, or other network topologies.
======= Conference Topic =======
Recent advances in deep neural networks (DNNs), combined with open, easily-accessible implementations, have made DNNs a powerful, versatile method used widely in both machine learning and neuroscience. These advances in practical results, however, have far outpaced a formal understanding of these networks and their training. Recently, long-past-due theoretical results have begun to emerge, shedding light on the properties of large, adaptive, distributed learning architectures.
Following the success of the 2018 IAS-Princeton joint symposium on the same topic (https://sites.google.com/site/princetondeepmath/home), the 2019 meeting is more centrally located and broader in scope, but remains focused on rigorous theoretical understanding of deep neural networks.

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Meta-learning of textual representations - implementation -

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Meta-learning of textual representations by Jorge Madrid and Hugo Escalante

Recent progress in AutoML has lead to state-of-the-art methods (e.g., AutoSKLearn) that can be readily used by non-experts to approach any supervised learning problem. Whereas these methods are quite effective, they are still limited in the sense that they work for tabular (matrix formatted) data only. This paper describes one step forward in trying to automate the design of supervised learning methods in the context of text mining. We introduce a meta learning methodology for automatically obtaining a representation for text mining tasks starting from raw text. We report experiments considering 60 different textual representations and more than 80 text mining datasets associated to a wide variety of tasks. Experimental results show the proposed methodology is a promising solution to obtain highly effective off the shell text classification pipelines.

 An implementation is here: https://github.com/jorgegus/autotext

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One-Shot Neural Architecture Search via Compressive Sensing

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Using sparse recovery in network architecture search, there has to be a meta-thread somewhere here !

One-Shot Neural Architecture Search via Compressive Sensing by Minsu Cho, Mohammadreza Soltani, Chinmay Hegde

Neural architecture search (NAS), or automated design of neural network models, remains a very challenging meta-learning problem. Several recent works (called "one-shot" approaches) have focused on dramatically reducing NAS running time by leveraging proxy models that still provide architectures with competitive performance. In our work, we propose a new meta-learning algorithm that we call CoNAS, or Compressive sensing-based Neural Architecture Search. Our approach merges ideas from one-shot approaches with iterative techniques for learning low-degree sparse Boolean polynomial functions. We validate our approach on several standard test datasets, discover novel architectures hitherto unreported, and achieve competitive (or better) results in both performance and search time compared to existing NAS approaches. Further, we support our algorithm with a theoretical analysis, providing upper bounds on the number of measurements needed to perform reliable meta-learning; to our knowledge, these analysis tools are novel to the NAS literature and may be of independent interest.

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An Open Source AutoML Benchmark - implementation -

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An Open Source AutoML Benchmark by Erin Ledell, Pieter Gijsbers, Joaquin Vanschoren, Janek Thomas, Bernd Bischl and Sebastien Poirier

In recent years, an active field of research has developed around automated machine learning (AutoML). Unfortunately, comparing different AutoML systems is hard and often done incorrectly. We introduce an open, ongoing, and extensible benchmark framework which follows best practices and avoids common mistakes. The framework is open-source, uses public datasets and has a website with up-to-date results. We use the framework to conduct a thorough comparison of 4 AutoML systems across 39 datasets and analyze the results. 

An implementation of the benchmark is here: https://github.com/openml/automlbenchmark/

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Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings - implementation -

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Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings by Matilde Gargiani, Aaron Klein, Stefan Falkner and Frank Hutter

We propose probabilistic models that can extrapolate learning curves of iterative machine learning algorithms, such as stochastic gradient descent for training deep networks, based on training data with variable-length learning curves. We study instantiations of this framework based on random forests and Bayesian recurrent neural networks. Our experiments show that these models yield better predictions than state-of-the-art models from the hyperparameter optimization literature when extrapolating the performance of neural networks trained with different hyperparameter settings.

An implementation is here: https://github.com/gmatilde/vdrnn

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Improving Automated Variational Inference with Normalizing Flows - implementation -

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Improving Automated Variational Inference with Normalizing Flows by Stefan Webb, J. P. Chen, Martin Jankowiak and Noah Goodman

We describe a framework for performing automatic Bayesian inference in probabilistic programs with fixed structure. Our framework takes a probabilistic program with fixed structure as input and outputs a learnt variational distribution approximating the posterior. For this purpose, we exploit recent advances in representing distributions with neural networks. We implement our approach in the Pyro probabilistic programming language, and validate it on a diverse collection of Bayesian regression models translated from Stan, showing improved inference and predictive performance relative to the existing state-of-the-art in automated inference for this class of models.

 An implementation is here: https://github.com/stefanwebb/autoguides

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Efficient Forward Architecture Search - implementation -

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Efficient Forward Architecture Search by Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz and Debadeepta Dey

In this work, we propose a neural architecture search (NAS) algorithm that iteratively augments existing networks by adding shortcut connections and layers. At each iteration, we greedily select among the most cost-efficient models a parent model, and insert into it a number of candidate layers. To learn which combination of additional layers to keep, we simultaneously train their parameters and use feature selection techniques to extract the most promising candidates which are then jointly trained with the parent model. The result of this process is excellent statistical performance with relatively low computational cost. Furthermore, unlike recent studies of NAS that almost exclusively focus on the small search space of repeatable network modules (cells), this approach also allows direct search among the more general (macro) network structures to find cost-effective models when macro search starts with the same initial models as cell search does. Source code is available at https://github.com/microsoft/petridishnn. 

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Bayesian Optimization over Sets - implementation -

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Bayesian Optimization over Sets by Jungtaek Kim, Michael McCourt, Saehoon Kim, Tackgeun You and Seungjin Choi

We propose a Bayesian optimization method over sets, to minimize a black-box function that can take a set as single input. Because set inputs are permutation-invariant and variable-length, traditional Gaussian process-based Bayesian optimization strategies which assume vector inputs can fall short. To address this, we develop a Bayesian optimization method with set kernel that is used to build surrogate functions. This kernel accumulates similarity over set elements to enforce permutation-invariance and permit sets of variable size, but this comes at a greater computational cost. To reduce this burden, we propose a more efficient probabilistic approximation which we prove is still positive definite and is an unbiased estimator of the true set kernel. Finally, we present several numerical experiments which demonstrate that our method outperforms other methods in various applications. 

The attendant implementation is here: https://github.com/jungtaekkim/bayeso

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Graduated Optimisation of Black-Box Functions - implementation -

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Graduated Optimisation of Black-Box Functions by Weijia Shao, Christian Geißler and Fikret Sivrikaya

Motivated by the problem of tuning hyperparameters in machine learning, we present a new approach for gradually and adaptively optimizing an unknown function using estimated gradients. We validate the empirical performance of the proposed idea on both low and high dimensional problems. The experimental results demonstrate the advantages of our approach for tuning high dimensional hyperparameters in machine learning. 

 The attendant implementation is here: https://github.com/christiangeissler/gradoptbenchmark

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Accelerating the Nelder - Mead Method with Predictive Parallel Evaluation - implementation -

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Accelerating the Nelder - Mead Method with Predictive Parallel Evaluation by Yoshihiko Ozaki, Shuhei Watanabe and Masaki Onishi

The Nelder–Mead (NM) method has been recently proposed for application in hyperparameter optimization (HPO) of deep neural networks. However, the NM method is not suitable for parallelization, which is a serious drawback for its practical application in HPO. In this study, we propose a novel approach to accelerate the NM method with respect to the parallel computing resources. The numerical results indicate that the proposed method is significantly faster and more efficient when compared with the previous naive approaches with respect to the HPO tabular benchmarks.

 The attendant implementaiton is here.

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Toward Instance-aware Neural Architecture Search - implementation -

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Toward Instance-aware Neural Architecture Search by An-Chieh Cheng, Chieh Hubert Lin, Da-Cheng Juan, Wei Wei and Min Sun

Recent advancements in Neural Architecture Search (NAS) have achieved significant improvements in both single and multiple objectives settings. However, current lines of research only consider searching for a single best architecture within a search space. Such an assumption restricts the model from capturing the high diversity and variety of real-world data. With this observation, we propose InstaNAS, an instance-ware NAS framework that aims to search for a distribution of architectures. Intuitively, we assume that real-world data consists of many domains (e.g., different difficulties or structural characteristics), and each domain can have one or multiple experts that have relatively more preferable performance. The controller of InstaNAS is not only responsible for sampling architectures during its search phase, but also needs to identify which down-stream expert architecture to use for each input instance during the inference phase. We demonstrate the effectiveness of InstaNAS in a multiple-objective NAS setting that considers the trade-offs between accuracy and latency. Within a search space inspired by MobileNetV2 on a series of datasets, experiments show that InstaNAS can achieve either higher accuracy with same latency or significant latency reduction without compromising accuracy against MobileNetV2.

The attendant implementation is here: https://github.com/AnjieZheng/InstaNAS

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