pwc-archive/papers-with-abstracts huggingface dataset:
Learning Generative Models with Locally Disentangled Latent Factors
Authors: Brady Neal, Alex Lamb, Sherjil Ozair, Devon Hjelm, Aaron Courville, Yoshua Bengio, Ioannis Mitliagkas
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

One of the most successful techniques in generative models has been decomposing a complicated generation task into a series of simpler generation tasks.  For example, generating an image at a low resolution and then learning to refine that into a high resolution image often improves results substantially.  Here we explore a novel strategy for decomposing generation for complicated objects in which we first generate latent variables which describe a subset of the observed variables, and then map from these latent variables to the observed space.  We show that this allows us to achieve decoupled training of complicated generative models and present both theoretical and experimental results supporting the benefit of such an approach.
A Deep Predictive Coding Network for Learning Latent Representations
Authors: Shirin Dora, Cyriel Pennartz, Sander Bohte
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

It has been argued that the brain is a prediction machine that continuously learns how to make better predictions about the stimuli received from the external environment. For this purpose, it builds a model of the world around us and uses this model to infer the external stimulus. Predictive coding has been proposed as a mechanism through which the brain might be able to build such a model of the external environment. However, it is not clear how predictive coding can be used to build deep neural network models of the brain while complying with the architectural constraints imposed by the brain. In this paper, we describe an algorithm to build a deep generative model using predictive coding that can be used to infer latent representations about the stimuli received from external environment. Specifically, we used predictive coding to train a deep neural network on real-world images in a unsupervised learning paradigm. To understand the capacity of the network with regards to modeling the external environment, we studied the latent representations generated by the model on images of objects that are never presented to the model during training. Despite the novel features of these objects the model is able to infer the latent representations for them. Furthermore, the reconstructions of the original images obtained from these latent representations preserve the important details of these objects.
On Batch Adaptive Training for Deep Learning: Lower Loss and Larger Step Size
Authors: Runyao Chen, Kun Wu, Ping Luo
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: image-classification, Image Classification

Mini-batch gradient descent and its variants are commonly used in deep learning. The principle of mini-batch gradient descent is to use noisy gradient calculated on a batch to estimate the real gradient, thus balancing the computation cost per iteration and the uncertainty of noisy gradient. However, its batch size is a fixed hyper-parameter requiring manual setting before training the neural network. Yin et al. (2017) proposed a batch adaptive stochastic gradient descent (BA-SGD) that can dynamically choose a proper batch size as learning proceeds. We extend the BA-SGD to momentum algorithm and evaluate both the BA-SGD and the batch adaptive momentum (BA-Momentum) on two deep learning tasks from natural language processing to image classification. Experiments confirm that batch adaptive methods can achieve a lower loss compared with mini-batch methods after scanning the same epochs of data. Furthermore, our BA-Momentum is more robust against larger step sizes, in that it can dynamically enlarge the batch size to reduce the larger uncertainty brought by larger step sizes. We also identified an interesting phenomenon, batch size boom. The code implementing batch adaptive framework is now open source, applicable to any gradient-based optimization problems.
Training with Growing Sets: A Simple Alternative to Curriculum Learning and Self Paced Learning
Authors: Melike Nur Mermer, Mehmet Fatih Amasyali
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

Curriculum learning and Self paced learning are popular topics in the machine learning that suggest to put the training samples in order by considering their difficulty levels. Studies in these topics show that starting with a small training set and adding new samples according to difficulty levels improves the learning performance. In this paper we experimented that we can also obtain good results by adding the samples randomly without a meaningful order. We compared our method with classical training, Curriculum learning, Self paced learning and their reverse ordered versions. Results of the statistical tests show that the proposed method is better than classical method and similar with the others. These results point a new training regime that removes the process of difficulty level determination in Curriculum and Self paced learning and as successful as these methods.
Piecewise Linear Neural Networks verification: A comparative study
Authors: Rudy Bunel, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, M. Pawan Kumar
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

The success of Deep Learning and its potential use in many important safety-
critical applications has motivated research on formal verification of Neural Net-
work (NN) models. Despite the reputation of learned NN models to behave as
black boxes and theoretical hardness results of the problem of proving their prop-
erties, researchers have been successful in verifying some classes of models by
exploiting their piecewise linear structure. Unfortunately, most of these works
test their algorithms on their own models and do not offer any comparison with
other approaches. As a result, the advantages and downsides of the different al-
gorithms are not well understood. Motivated by the need of accelerating progress
in this very important area, we investigate the trade-offs of a number of different
approaches based on Mixed Integer Programming, Satisfiability Modulo Theory,
as well as a novel method based on the Branch-and-Bound framework. We also
propose a new data set of benchmarks, in addition to a collection of previously
released testcases that can be used to compare existing methods. Our analysis not
only allowed a comparison to be made between different strategies, the compar-
ision of results from different solvers also revealed implementation bugs in pub-
lished methods. We expect that the availability of our benchmark and the analysis
of the different approaches will allow researchers to invent and evaluate promising
approaches for making progress on this important topic.
WHAT ARE GANS USEFUL FOR?
Authors: Pablo M. Olmos, Briland Hitaj, Paolo Gasti, Giuseppe Ateniese, Fernando Perez-Cruz
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Density Estimation

GANs have shown how deep neural networks can be used for generative modeling, aiming at achieving the same impact that they brought for discriminative modeling. The first results were impressive, GANs were shown to be able to generate samples in high dimensional structured spaces, like images and text, that were no copies of the training data. But generative and discriminative learning are quite different. Discriminative learning has a clear end, while generative modeling is an intermediate step to understand the data or generate hypothesis. The quality of implicit density estimation is hard to evaluate, because we cannot tell how well a data is represented by the model. How can we certainly say that a generative process is generating natural images with the same distribution as we do? In this paper, we noticed that even though GANs might not be able to generate samples from the underlying distribution (or we cannot tell at least), they are capturing some structure of the data in that high dimensional space. It is therefore needed to address how we can leverage those estimates produced by GANs in the same way we are able to use other generative modeling algorithms.
Learning objects from pixels
Authors: David Saxton
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Atari Games, Object, Position, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We show how discrete objects can be learnt in an unsupervised fashion from pixels, and how to perform reinforcement learning using this object representation.

More precisely, we construct a differentiable mapping from an image to a discrete tabular list of objects, where each object consists of a differentiable position, feature vector, and scalar presence value that allows the representation to be learnt using an attention mechanism.

Applying this mapping to Atari games, together with an interaction net-style architecture for calculating quantities from objects, we construct agents that can play Atari games using objects learnt in an unsupervised fashion. During training, many natural objects emerge, such as the ball and paddles in Pong, and the submarine and fish in Seaquest.

This gives the first reinforcement learning agent for Atari with an interpretable object representation, and opens the avenue for agents that can conduct object-based exploration and generalization.
SIC-GAN: A Self-Improving Collaborative GAN for Decoding Sketch RNNs
Authors: Chi-Chun Chuang, Zheng-Xin Weng, Shan-Hung Wu
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

Variational RNNs are proposed to output “creative” sequences. Ideally, a collection of sequences produced by a variational RNN should be of both high quality and high variety. However, existing decoders for variational RNNs suffer from a trade-off between quality and variety. In this paper, we seek to learn a variational RNN that decodes high-quality and high-variety sequences. We propose the Self-Improving Collaborative GAN (SIC-GAN), where there are two generators (variational RNNs) collaborating with each other to output a sequence and aiming to trick the discriminator into believing the sequence is of good quality. By deliberately weakening one generator, we can make another stronger in balancing quality and variety. We conduct experiments using the QuickDraw dataset and the results demonstrate the effectiveness of SIC-GAN empirically.
On the Construction and Evaluation of Color Invariant Networks
Authors: Konrad Groh
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

This is an empirical paper which constructs color invariant networks and evaluates their performances on a realistic data set. The paper studies the simplest possible case of color invariance: invariance under pixel-wise permutation of the color channels. Thus the network is aware not of the specific color object, but its colorfulness. The data set introduced in the paper consists of images showing crashed cars from which ten classes were extracted. An additional annotation was done which labeled whether the car shown was red or non-red.  The networks were evaluated by their performance on the classification task. With the color annotation we altered the color ratios  in the training data and analyzed the generalization capabilities of the networks on the unaltered test data. We further split the test data in red and non-red cars and did a similar evaluation. It is shown in the paper that an pixel-wise ordering of the rgb-values of the images performs better or at least similarly for small deviations from the true color ratios. The limits of these networks are also discussed.
The Mutual Autoencoder: Controlling Information in Latent Code Representations
Authors: Mary Phuong, Max Welling, Nate Kushman, Ryota Tomioka, Sebastian Nowozin
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Decoder, Representation Learning

Variational autoencoders (VAE) learn probabilistic latent variable models by optimizing a bound on the marginal likelihood of the observed data. Beyond providing a good density model a VAE model assigns to each data instance a latent code. In many applications, this latent code provides a useful high-level summary of the observation. However, the VAE may fail to learn a useful representation when the decoder family is very expressive. This is because maximum likelihood does not explicitly encourage useful representations and the latent variable is used only if it helps model the marginal distribution. This makes representation learning with VAEs unreliable. To address this issue, we propose a method for explicitly controlling the amount of information stored in the latent code. Our method can learn codes ranging from independent to nearly deterministic while benefiting from decoder capacity. Thus, we decouple the choice of decoder capacity and the latent code dimensionality from the amount of information stored in the code.
Avoiding degradation in deep feed-forward networks by phasing out skip-connections
Authors: Ricardo Pio Monti, Sina Tootoonian, Robin Cao
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

A widely observed phenomenon in deep learning is the degradation problem: increasing
the depth of a network leads to a decrease in performance on both test and training data. Novel architectures such as ResNets and Highway networks have addressed this issue by introducing various flavors of skip-connections or gating mechanisms. However, the degradation problem persists in the context of plain feed-forward networks. In this work we propose a simple method to address this issue. The proposed method poses the learning of weights in deep networks as a constrained optimization problem where the presence of skip-connections is penalized by Lagrange multipliers. This allows for skip-connections to be introduced during the early stages of training and subsequently phased out in a principled manner. We demonstrate the benefits of such an approach with experiments on MNIST, fashion-MNIST, CIFAR-10 and CIFAR-100 where the proposed method is shown to greatly decrease the degradation effect (compared to plain networks) and is often competitive with ResNets.
Capturing Human Category Representations by Sampling in Deep Feature Spaces
Authors: Joshua Peterson, Krishan Aghi, Jordan Suchow, Alexander Ku, Tom Griffiths
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: BIG-bench Machine Learning

Understanding how people represent categories is a core problem in cognitive science, with the flexibility of human learning remaining a gold standard to which modern artificial intelligence and machine learning aspire. Decades of psychological research have yielded a variety of formal theories of categories, yet validating these theories with naturalistic stimuli remains a challenge. The problem is that human category representations cannot be directly observed and running informative experiments with naturalistic stimuli such as images requires having a workable representation of these stimuli. Deep neural networks have recently been successful in a range of computer vision tasks and provide a way to represent the features of images. In this paper, we introduce a method for estimating the structure of human categories that draws on ideas from both cognitive science and machine learning, blending human-based algorithms with state-of-the-art deep representation learners. We provide qualitative and quantitative results as a proof of concept for the feasibility of the method. Samples drawn from human distributions rival the quality of current state-of-the-art generative models and outperform alternative methods for estimating the structure of human categories.
Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks
Authors: Jinsung Yoon, William R. Zame, Mihaela van der Schaar
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

For every prediction we might wish to make, we must decide what to observe (what source of information) and when to observe it. Because making observations is costly, this decision must trade off the value of information against the cost of observation. Making observations (sensing) should be an active choice. To solve the problem of active sensing we develop a novel deep learning architecture: Deep Sensing. At training time, Deep Sensing learns how to issue predictions at various cost-performance points. To do this, it creates multiple representations at various performance levels associated with different measurement rates (costs). This requires learning how to estimate the value of real measurements vs. inferred measurements, which in turn requires learning how to infer missing (unobserved) measurements.  To infer missing measurements, we develop a Multi-directional Recurrent Neural Network (M-RNN).  An M-RNN differs from a bi-directional RNN in that it sequentially operates across streams in addition to within streams, and because the timing of inputs into the hidden layers is both lagged and advanced.  At runtime, the operator prescribes a performance level or a cost constraint, and Deep Sensing determines what measurements to take and what to infer from those measurements, and then issues predictions. To demonstrate the power of our method, we apply it to two real-world medical datasets with significantly improved performance.
Universality, Robustness, and Detectability of Adversarial Perturbations under Adversarial Training
Authors: Jan Hendrik Metzen
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

Classifiers such as deep neural networks have been shown to be vulnerable against adversarial perturbations on problems with high-dimensional input space. While adversarial training improves the robustness of classifiers against such adversarial perturbations, it leaves classifiers sensitive to them on a non-negligible fraction of the inputs. We argue that there are two different kinds of adversarial perturbations: shared perturbations which fool a classifier on many inputs and singular perturbations which only fool the classifier on a small fraction of the data. We find that adversarial training increases the robustness of classifiers against shared perturbations. Moreover, it is particularly effective in removing universal perturbations, which can be seen as an extreme form of shared perturbations. Unfortunately, adversarial training does not consistently increase the robustness against singular perturbations on unseen inputs. However, we find that adversarial training decreases robustness of the remaining perturbations against image transformations such as changes to contrast and brightness or  Gaussian blurring. It thus makes successful attacks on the classifier in the physical world less likely. Finally, we show that even singular perturbations can be easily detected and must thus exhibit generalizable patterns even though the perturbations are specific for certain inputs.
Autoregressive Generative Adversarial Networks
Authors: Yasin Yazici, Kim-Hui Yap, Stefan Winkler
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Binary Classification, General Classification, Generative Adversarial Network

Generative Adversarial Networks (GANs) learn a generative model by playing an adversarial game between a generator and an auxiliary discriminator, which classifies data samples vs. generated ones. However, it does not explicitly model feature co-occurrences in samples. In this paper, we propose a novel Autoregressive Generative Adversarial Network (ARGAN), that models the latent distribution of data using an autoregressive model, rather than relying on binary classification of samples into data/generated categories. In this way, feature co-occurrences in samples can be more efficiently captured. Our model was evaluated on two widely used datasets: CIFAR-10 and STL-10. Its performance is competitive with respect to other GAN models both quantitatively and qualitatively.
Learning to Select: Problem, Solution, and Applications
Authors: Heechang Ryu, Donghyun Kim, Hayong Shin
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Learning-To-Rank

We propose a "Learning to Select" problem that selects the best among the flexible size candidates. This makes decisions based not only on the properties of the candidate, but also on the environment in which they belong to. For example, job dispatching in the manufacturing factory is a typical "Learning to Select" problem. We propose Variable-Length CNN which combines the classification power using hidden features from CNN and the idea of flexible input from Learning to Rank algorithms. This not only can handles flexible candidates using Dynamic Computation Graph, but also is computationally efficient because it only builds a network with the necessary sizes to fit the situation. We applied the algorithm to the job dispatching problem which uses the dispatching log data obtained from the virtual fine-tuned factory. Our proposed algorithm shows considerably better performance than other comparable algorithms.
Policy Gradient For Multidimensional Action Spaces: Action Sampling and Entropy Bonus
Authors: Vuong Ho Quan, Yiming Zhang, Kenny Song, Xiao-Yue Gong, Keith W. Ross
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years deep reinforcement learning has been shown to be adept at solving sequential decision processes with high-dimensional state spaces such as in the Atari games. Many reinforcement learning problems, however, involve high-dimensional discrete action spaces as well as high-dimensional state spaces. In this paper, we develop a novel policy gradient methodology for the case of large multidimensional discrete action spaces. We propose two approaches for creating parameterized policies: LSTM parameterization and a Modified MDP (MMDP) giving rise to Feed-Forward Network (FFN) parameterization. Both of these approaches provide expressive models to which backpropagation can be applied for training. We then consider entropy bonus, which is typically added to the reward function to enhance exploration. In the case of high-dimensional action spaces, calculating the entropy and the gradient of the entropy requires enumerating all the actions in the action space and running forward and backpropagation for each action, which may be computationally infeasible. We develop several novel unbiased estimators for the entropy bonus and its gradient. Finally, we test our algorithms on two environments: a multi-hunter multi-rabbit grid game and a multi-agent multi-arm bandit problem.
Cheap DNN Pruning with Performance Guarantees
Authors: Konstantinos Pitas, Mike Davies, Pierre Vandergheynst
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Classification, General Classification

Recent DNN pruning algorithms have succeeded in reducing the number of parameters in fully connected layers often with little or no drop in classification accuracy. However most of the existing pruning schemes either have to be applied during training or require a costly retraining procedure after pruning to regain classification accuracy. In this paper we propose a cheap pruning algorithm based on difference of convex (DC) optimisation. We also provide theoretical analysis for the growth in the Generalisation Error (GE) of the new pruned network. Our method can be used with any convex regulariser and allows for a controlled degradation in classification accuracy while being orders of magnitude faster than competing approaches. Experiments on common feedforward neural networks show that for sparsity levels above 90% our method achieves 10% higher classification accuracy compared to Hard Thresholding.