pwc-archive/papers-with-abstracts huggingface dataset:
Toward learning better metrics for sequence generation training with policy gradient
Authors: Joji Toyama, Yusuke Iwasawa, Kotaro Nakayama, Yutaka Matsuo
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Reinforcement Learning, Text Generation

Designing a metric manually for unsupervised sequence generation tasks, such as text generation, is essentially difficult. In a such situation, learning a metric of a sequence from data is one possible solution. The previous study, SeqGAN, proposed the framework for unsupervised sequence generation, in which a metric is learned from data, and a generator is optimized with regard to the learned metric with policy gradient, inspired by generative adversarial nets (GANs) and reinforcement learning. In this paper, we make two proposals to learn better metric than SeqGAN's: partial reward function and expert-based reward function training. The partial reward function is a reward function for a partial sequence of a certain length. SeqGAN employs a reward function for completed sequence only. By combining long-scale and short-scale partial reward functions, we expect a learned metric to be able to evaluate a partial correctness as well as a coherence of a sequence, as a whole. In expert-based reward function training, a reward function is trained to discriminate between an expert (or true) sequence and a fake sequence that is produced by editing an expert sequence. Expert-based reward function training is not a kind of GAN frameworks. This makes the optimization of the generator easier. We examine the effect of the partial reward function and expert-based reward function training on synthetic data and real text data, and show improvements over SeqGAN and the model trained with MLE. Specifically, whereas SeqGAN gains 0.42 improvement of NLL over MLE on synthetic data, our best model gains 3.02 improvement, and whereas SeqGAN gains 0.029 improvement of BLEU over MLE, our best model gains 0.250 improvement.
Classifier-to-Generator Attack: Estimation of Training Data Distribution from Classifier
Authors: Kosuke Kusano, Jun Sakuma
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Classification, Face Recognition, General Classification, Object Recognition

Suppose a deep classification model is trained with samples that need to be kept private for privacy or confidentiality reasons. In this setting, can an adversary obtain the private samples if the classification model is given to the adversary? We call this reverse engineering against the classification model the Classifier-to-Generator (C2G) Attack. This situation arises when the classification model is embedded into mobile devices for offline prediction (e.g., object recognition for the automatic driving car and face recognition for mobile phone authentication).
For C2G attack, we introduce a novel GAN, PreImageGAN. In PreImageGAN, the generator is designed to estimate the the sample distribution conditioned by the preimage of classification model $f$, $P(X|f(X)=y)$, where $X$ is the random variable on the sample space and $y$ is the probability vector representing the target label arbitrary specified by the adversary. In experiments, we demonstrate PreImageGAN works successfully with hand-written character recognition and face recognition. In character recognition, we show that, given a recognition model of hand-written digits, PreImageGAN allows the adversary to extract alphabet letter images without knowing that the model is built for alphabet letter images. In face recognition, we show that, when an adversary obtains a face recognition model for a set of individuals, PreImageGAN allows the adversary to extract face images of specific individuals contained in the set, even when the adversary has no knowledge of the face of the individuals.
Better Generalization by Efficient Trust Region Method
Authors: Xuanqing Liu, Jason D. Lee, Cho-Jui Hsieh
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

In this paper, we develop a trust region method for training deep neural networks. At each iteration, trust region method computes the search direction by solving a non-convex subproblem. Solving this subproblem is non-trivial---existing methods have only sub-linear convergence rate. In the first part, we show that a simple modification of gradient descent algorithm can converge to a global minimizer of the subproblem with an asymptotic linear convergence rate. Moreover, our method only requires Hessian-vector products, which can be computed efficiently by back-propagation in neural networks. In the second part, we apply our algorithm to train large-scale convolutional neural networks, such as VGG and MobileNets. Although trust region method is about 3 times slower than SGD in terms of running time, we observe it finds a model that has lower generalization (test) error than SGD, and this difference is even more significant in large batch training. 
We conduct several interesting experiments to support our conjecture that the trust region method can avoid sharp local minimas.
Enhancing the Transferability of Adversarial Examples with Noise Reduced Gradient
Authors: Lei Wu, Zhanxing Zhu, Cheng Tai, Weinan E
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

Deep neural networks provide state-of-the-art performance for many applications of interest. Unfortunately they are known to be vulnerable to adversarial examples, formed by applying small but malicious perturbations to the original inputs. Moreover, the perturbations can transfer across models: adversarial examples generated for a specific model will often mislead other unseen models. Consequently  the adversary can leverage it to attack against the deployed black-box systems. 
In this work, we demonstrate that the adversarial perturbation can be decomposed into two components: model-specific and data-dependent one, and it is the latter that mainly contributes to the transferability. Motivated by this understanding, we propose to craft adversarial examples by utilizing the noise reduced gradient (NRG) which approximates the data-dependent component. Experiments on various classification models trained on ImageNet demonstrates that the new approach enhances the transferability dramatically. We also find that low-capacity models have more powerful attack capability than high-capacity counterparts, under the condition that they have comparable test performance.  These insights give rise to a principled manner to construct adversarial examples with high success rates and could potentially provide us guidance for designing effective defense approaches against black-box attacks.
Discovering Order in Unordered Datasets: Generative Markov Networks
Authors: Yao-Hung Hubert Tsai, Han Zhao, Nebojsa Jojic, Ruslan Salakhutdinov
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

The assumption that data samples are independently identically distributed is the backbone of many learning algorithms. Nevertheless, datasets often exhibit rich structures in practice, and we argue that there exist some unknown orders within the data instances. Aiming to find such orders, we introduce a novel Generative Markov Network (GMN) which we use to extract the order of data instances automatically. Specifically, we assume that the instances are sampled from a Markov chain. Our goal is to learn the transitional operator of the chain as well as the generation order by maximizing the generation probability under all possible data permutations. One of our key ideas is to use neural networks as a soft lookup table for approximating the possibly huge, but discrete transition matrix. This strategy allows us to amortize the space complexity with a single model and make the transitional operator generalizable to unseen instances. To ensure the learned Markov chain is ergodic, we propose a greedy batch-wise permutation scheme that allows fast training.  Empirically, we evaluate the learned Markov chain by showing that GMNs are able to discover orders among data instances and also perform comparably well to state-of-the-art methods on the one-shot recognition benchmark task.
Pointing Out SQL Queries From Text
Authors: Chenglong Wang, Marc Brockschmidt, Rishabh Singh
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Decoder, Reinforcement Learning

The digitization of data has resulted in making datasets available to millions of users in the form of relational databases and spreadsheet tables. However, a majority of these users come from diverse backgrounds and lack the programming expertise to query and analyze such tables. We present a system that allows for querying data tables using natural language questions, where the system translates the question into an executable SQL query. We use a deep sequence to sequence model in wich the decoder uses a simple type system of SQL expressions to structure the output prediction. Based on the type, the decoder either copies an output token from the input question using an attention-based copying mechanism or generates it from a fixed vocabulary. We also introduce a value-based loss function that transforms a distribution over locations to copy from into a distribution over the set of input tokens to improve training of our model. We evaluate our model on the recently released WikiSQL dataset and show that our model trained using only supervised learning significantly outperforms the current state-of-the-art Seq2SQL model that uses reinforcement learning.
Learning Graph Convolution Filters from Data Manifold
Authors: Guokun Lai, Hanxiao Liu, Yiming Yang
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

Convolution Neural Network (CNN) has gained tremendous success in computer vision tasks with its outstanding ability to capture the local latent features. Recently, there has been an increasing interest in extending CNNs to the general spatial domain. Although various types of graph convolution and geometric convolution methods have been proposed, their connections to traditional 2D-convolution are not well-understood. In this paper, we show that depthwise separable convolution is a path to unify the two kinds of convolution methods in one mathematical view, based on which we derive a novel Depthwise Separable Graph Convolution that subsumes existing graph convolution methods as special cases of our formulation. Experiments show that the proposed approach consistently outperforms other graph convolution and geometric convolution baselines on benchmark datasets in multiple domains.
Interpreting Deep Classification Models With Bayesian Inference
Authors: Hanshu Yan, Jiashi Feng
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Bayesian Inference, Classification, General Classification

In this paper, we propose a novel approach to interpret a well-trained classification model through systematically investigating effects of its hidden units on prediction making. We search for the core hidden units responsible for predicting inputs as the class of interest under the generative Bayesian inference framework. We model such a process of unit selection as an Indian Buffet Process, and derive a simplified objective function via the MAP asymptotic technique. The induced binary optimization problem is efficiently solved with a continuous relaxation method by attaching a Switch Gate layer to the hidden layers of interest. The resulted interpreter model is thus end-to-end optimized via standard gradient back-propagation. Experiments are conducted with two popular deep convolutional classifiers, respectively well-trained on the MNIST dataset and the CI- FAR10 dataset. The results demonstrate that the proposed interpreter successfully finds the core hidden units most responsible for prediction making. The modified model, only with the selected units activated, can hold correct predictions at a high rate. Besides, this interpreter model is also able to extract the most informative pixels in the images by connecting a Switch Gate layer to the input layer.
Parametric Manifold Learning Via Sparse Multidimensional Scaling
Authors: Gautam Pai, Ronen Talmon, Ron Kimmel
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Metric Learning

We propose a metric-learning framework for computing distance-preserving maps that generate low-dimensional embeddings for a certain class of manifolds. We employ Siamese networks to solve the problem of least squares multidimensional scaling for generating mappings that preserve geodesic distances on the manifold. In contrast to previous parametric manifold learning methods we show a substantial reduction in training effort enabled by the computation of geodesic distances in a farthest point sampling strategy. Additionally, the use of a network to model the distance-preserving map reduces the complexity of the multidimensional scaling problem and leads to an improved non-local generalization of the manifold compared to analogous non-parametric counterparts. We demonstrate our claims on point-cloud data and on image manifolds and show a numerical analysis of our technique to facilitate a greater understanding of the representational power of neural networks in modeling manifold data.
Fast and Accurate Inference with Adaptive Ensemble Prediction for Deep Networks
Authors: Hiroshi Inoue
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: image-classification, Image Classification

Ensembling multiple predictions is a widely-used technique to improve the accuracy of various machine learning tasks. In image classification tasks, for example, averaging the predictions for multiple patches extracted from the input image significantly improves accuracy. Using multiple networks trained independently to make predictions improves accuracy further. One obvious drawback of the ensembling technique is its higher execution cost during inference.% If we average 100 local predictions, the execution cost will be 100 times as high as the cost without the ensemble. This higher cost limits the real-world use of ensembling. In this paper, we first describe our insights on relationship between the probability of the prediction and the effect of ensembling with current deep neural networks; ensembling does not help mispredictions for inputs predicted with a high probability, i.e. the output from the softmax. This finding motivates us to develop a new technique called adaptive ensemble prediction, which achieves the benefits of ensembling with much smaller additional execution costs. Hence, we calculate the confidence level of the prediction for each input from the probabilities of the local predictions during the ensembling computation. If the prediction for an input reaches a high enough probability on the basis of the confidence level, we stop ensembling for this input to avoid wasting computation power. We evaluated the adaptive ensembling by using various datasets and showed that it reduces the computation cost significantly while achieving similar accuracy to the naive ensembling. We also showed that our statistically rigorous confidence-level-based termination condition reduces the burden of the task-dependent parameter tuning compared to the naive termination based on the pre-defined threshold in addition to yielding a better accuracy with the same cost.
Counterfactual Image Networks
Authors: Deniz Oktay, Carl Vondrick, Antonio Torralba
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: counterfactual, Object, Segmentation, Semantic Segmentation

We capitalize on the natural compositional structure of images in order to learn object segmentation with weakly labeled images. The intuition behind our approach is that removing objects from images will yield natural images, however removing random patches will yield unnatural images. We leverage this signal to develop a generative model that decomposes an image into layers, and when all layers are combined, it reconstructs the input image. However, when a layer is removed, the model learns to produce a different image that still looks natural to an adversary, which is possible by removing objects. Experiments and visualizations suggest that this model automatically learns object segmentation on images labeled only by scene better than baselines.
Massively Parallel Hyperparameter Tuning
Authors: Lisha Li, Kevin Jamieson, Afshin Rostamizadeh, Katya Gonina, Moritz Hardt, Benjamin Recht, Ameet Talwalkar
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

Modern machine learning models are characterized by large hyperparameter search spaces and prohibitively expensive training costs.  For such models, we cannot afford to train candidate models sequentially and wait months before finding a suitable hyperparameter configuration. Hence, we introduce the large-scale regime for parallel hyperparameter tuning, where we need to evaluate orders of magnitude more configurations than available parallel workers in a small multiple of the wall-clock time needed to train a single model.  We propose a novel hyperparameter tuning algorithm for this setting that exploits both parallelism and aggressive early-stopping techniques, building on the insights of the Hyperband algorithm.  Finally, we conduct a thorough empirical study of our algorithm on several benchmarks, including large-scale experiments with up to 500 workers.  Our results show that our proposed algorithm finds good hyperparameter settings nearly an order of magnitude faster than random search.
Hallucinating brains with artificial brains
Authors: Peiye Zhuang, Alexander G. Schwing, Oluwasanmi Koyejo
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Diversity

Human brain function as measured by functional magnetic resonance imaging
(fMRI), exhibits a rich diversity. In response, understanding the individual variability
of brain function and its association with behavior has become one of the
major concerns in modern cognitive neuroscience. Our work is motivated by the
view that generative models provide a useful tool for understanding this variability.
To this end, this manuscript presents two novel generative models trained
on real neuroimaging data which synthesize task-dependent functional brain images.
Brain images are high dimensional tensors which exhibit structured spatial
correlations. Thus, both models are 3D conditional Generative Adversarial networks
(GANs) which apply Convolutional Neural Networks (CNNs) to learn an
abstraction of brain image representations. Our results show that the generated
brain images are diverse, yet task dependent. In addition to qualitative evaluation,
we utilize the generated synthetic brain volumes as additional training data to improve
downstream fMRI classifiers (also known as decoding, or brain reading).
Our approach achieves significant improvements for a variety of datasets, classifi-
cation tasks and evaluation scores. Our classification results provide a quantitative
evaluation of the quality of the generated images, and also serve as an additional
contribution of this manuscript.
Still not systematic after all these years: On the compositional skills of sequence-to-sequence recurrent networks
Authors: Brenden Lake, Marco Baroni
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: All, Machine Translation, Translation, Zero-shot Generalization

Humans can understand and produce new utterances effortlessly, thanks to their systematic compositional skills. Once a person learns the meaning of a new verb "dax," he or she can immediately understand the meaning of "dax twice" or "sing and dax." In this paper, we introduce the SCAN domain, consisting of a set of simple compositional navigation commands paired with the corresponding action sequences. We then test the zero-shot generalization capabilities of a variety of recurrent neural networks (RNNs) trained on SCAN with sequence-to-sequence methods. We find that RNNs can generalize well when the differences between training and test commands are small, so that they can apply "mix-and-match" strategies to solve the task. However, when generalization requires systematic compositional skills (as in the "dax" example above), RNNs fail spectacularly. We conclude with a proof-of-concept experiment in neural machine translation, supporting the conjecture that lack of systematicity is an important factor explaining why neural networks need very large training sets.
Multi-task Learning on MNIST Image Datasets
Authors: Po-Chen Hsieh, Chia-Ping Chen
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Classification, General Classification, image-classification, Image Classification, Multi-Task Learning

We apply multi-task learning to image classification tasks on MNIST-like datasets. MNIST dataset has been referred to as the {\em drosophila} of machine learning and has been the testbed of many learning theories. The NotMNIST dataset and the FashionMNIST dataset have been created with the MNIST dataset as reference. In this work, we exploit these MNIST-like datasets for multi-task learning. The datasets are pooled together for learning the parameters of joint classification networks. Then the learned parameters are used as the initial parameters to retrain disjoint classification networks. The baseline recognition model are all-convolution neural networks. Without multi-task learning, the recognition accuracies for MNIST, NotMNIST and FashionMNIST are 99.56\%, 97.22\% and 94.32\% respectively. With multi-task learning to pre-train the networks, the recognition accuracies are respectively 99.70\%, 97.46\% and 95.25\%. The results re-affirm that multi-task learning framework, even with data with different genres, does lead to significant improvement.
A closer look at the word analogy problem
Authors: Siddharth Krishna Kumar
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

Although word analogy problems have become a standard tool for evaluating word vectors, little is known about why word vectors are so good at solving these problems. In this paper, I attempt to further our understanding of the subject, by developing a simple, but highly accurate generative approach to solve the word analogy problem for the case when all terms involved in the problem are nouns. My results demonstrate the ambiguities associated with learning the relationship between a word pair, and the role of the training dataset in determining the relationship which gets most highlighted. Furthermore, my results show that the ability of a model to accurately solve the word analogy problem may not be indicative of a model’s ability to learn the relationship between a word pair the way a human does.
Large Scale Multi-Domain Multi-Task Learning with MultiModel
Authors: Lukasz Kaiser, Aidan N. Gomez, Noam Shazeer, Ashish Vaswani, Niki Parmar, Llion Jones, Jakob Uszkoreit
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Image Captioning, image-classification, Image Classification, Multi-Task Learning, speech-recognition, Speech Recognition, Translation

Deep learning yields great results across many fields,
from speech recognition, image classification, to translation.
But for each problem, getting a deep model to work well involves
research into the architecture and a long period of tuning.

We present a single model that yields good results on a number
of problems spanning multiple domains. In particular, this single model
is trained concurrently on ImageNet, multiple translation tasks,
image captioning (COCO dataset), a speech recognition corpus,
and an English parsing task. 

Our model architecture incorporates building blocks from multiple
domains. It contains convolutional layers, an attention mechanism,
and sparsely-gated layers.

Each of these computational blocks is crucial for a subset of
the tasks we train on. Interestingly, even if a block is not
crucial for a task, we observe that adding it never hurts performance
and in most cases improves it on all tasks.

We also show that tasks with less data benefit largely from joint
training with other tasks, while performance on large tasks degrades
only slightly if at all.