pwc-archive/papers-with-abstracts huggingface dataset:
Neural Networks with Block Diagonal Inner Product Layers
Authors: Amy Nesky, Quentin Stout
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

Artificial neural networks have opened up a world of possibilities in data science and artificial intelligence, but neural networks are cumbersome tools that grow with the complexity of the learning problem. We make contributions to this issue by considering a modified version of the fully connected layer we call a block diagonal inner product layer. These modified layers have weight matrices that are block diagonal, turning a single fully connected layer into a set of densely connected neuron groups. This idea is a natural extension of group, or depthwise separable, convolutional layers applied to the fully connected layers. Block diagonal inner product layers can be achieved by either initializing a purely block diagonal weight matrix or by iteratively pruning off diagonal block entries. This method condenses network storage and speeds up the run time without significant adverse effect on the testing accuracy, thus offering a new approach to improve network computation efficiency.
End-to-End Abnormality Detection in Medical Imaging
Authors: Dufan Wu, Kyungsang Kim, Bin Dong, Quanzheng Li
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Anomaly Detection, Computed Tomography (CT), Lesion Detection, Lung Nodule Detection

Deep neural networks (DNN) have shown promising performance in computer vision. In medical imaging, encouraging results have been achieved with deep learning for applications such as segmentation, lesion detection and classification. Nearly all of the deep learning based image analysis methods work on reconstructed images, which are obtained from original acquisitions via solving inverse problems (reconstruction). The reconstruction algorithms are designed for human observers, but not necessarily optimized for DNNs which can often observe features that are incomprehensible for human eyes. Hence, it is desirable to train the DNNs directly from the original data which lie in a different domain with the images. In this paper, we proposed an end-to-end DNN for abnormality detection in medical imaging. To align the acquisition with the annotations made by radiologists in the image domain, a DNN was built as the unrolled version of iterative reconstruction algorithms to map the acquisitions to images, and followed by a 3D convolutional neural network (CNN) to detect the abnormality in the reconstructed images. The two networks were trained jointly in order to optimize the entire DNN for the detection task from the original acquisitions. The DNN was implemented for lung nodule detection in low-dose chest computed tomography (CT), where a numerical simulation was done to generate acquisitions from 1,018 chest CT images with radiologists' annotations. The proposed end-to-end DNN demonstrated better sensitivity and accuracy for the task compared to a two-step approach, in which the reconstruction and detection DNNs were trained separately. A significant reduction of false positive rate on suspicious lesions were observed, which is crucial for the known over-diagnosis in low-dose lung CT imaging. The images reconstructed by the proposed end-to-end network also presented enhanced details in the region of interest.
Compact Encoding of Words for Efficient Character-level Convolutional Neural Networks Text Classification
Authors: Wemerson Marinho, Luis Marti, Nayat Sanchez-pi
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: General Classification, text-classification, Text Classification

This paper puts forward a new text to tensor representation that relies on information compression techniques to assign shorter codes to the most frequently used characters. This representation is language-independent with no need of pretraining and produces an encoding with no information loss. It provides an adequate description of the morphology of text, as it is able to represent prefixes, declensions, and inflections with similar vectors and are able to represent even unseen words on the training dataset. Similarly, as it is compact yet sparse, is ideal for speed up training times using tensor processing libraries. As part of this paper, we show that this technique is especially effective when coupled with convolutional neural networks (CNNs) for text classification at character-level. We apply two variants of CNN coupled with it. Experimental results show that it drastically reduces the number of parameters to be optimized, resulting in competitive classification accuracy values in only a fraction of the time spent by one-hot encoding representations, thus enabling training in commodity hardware.
Anytime Neural Network: a Versatile Trade-off Between Computation and Accuracy
Authors: Hanzhang Hu, Debadeepta Dey, Martial Hebert, J. Andrew Bagnell
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

We present an approach for anytime predictions in deep neural networks (DNNs). For each test sample, an anytime predictor produces a coarse result quickly, and then continues to refine it until the test-time computational budget is depleted. Such predictors can address the growing computational problem of DNNs by automatically adjusting to varying test-time budgets. In this work, we study a \emph{general} augmentation to feed-forward networks to form anytime neural networks (ANNs) via auxiliary predictions and losses. Specifically, we point out a blind-spot in recent studies in such ANNs: the importance of high final accuracy. In fact, we show on multiple recognition data-sets and architectures that by having near-optimal final predictions in small anytime models, we can effectively double the speed of large ones to reach corresponding accuracy level. We achieve such speed-up with simple weighting of anytime losses that oscillate during training. We also assemble a sequence of exponentially deepening ANNs, to achieve both theoretically and practically near-optimal anytime results at any budget, at the cost of a constant fraction of additional consumed budget.
Adversarial Policy Gradient for Alternating Markov Games
Authors: Chao Gao, Martin Mueller, Ryan Hayward
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Policy Gradient Methods, Reinforcement Learning

Policy gradient reinforcement learning has been applied to two-player alternate-turn zero-sum games, e.g., in AlphaGo, self-play REINFORCE was used to improve the neural net model after supervised learning. In this paper, we emphasize that two-player zero-sum games with alternating turns, which have been previously formulated as Alternating Markov Games (AMGs), are different from standard MDP because of their two-agent nature. We exploit the difference in associated Bellman equations, which leads to different policy iteration algorithms. As policy gradient method is a kind of generalized policy iteration, we show how these differences in policy iteration are reflected in policy gradient for AMGs. We formulate an adversarial policy gradient and discuss potential possibilities for developing better policy gradient methods other than self-play REINFORCE. The core idea is to estimate the minimum rather than the mean for the “critic”. Experimental results on the game of Hex show the modified Monte Carlo policy gradient methods are able to learn better pure neural net policies than the REINFORCE variants. To apply learned neural weights to multiple board sizes Hex, we describe a board-size independent neural net architecture. We show that when combined with search, using a single neural net model, the resulting program consistently beats MoHex 2.0, the state-of-the-art computer Hex player, on board sizes from 9×9 to 13×13.
THE EFFECTIVENESS OF A TWO-LAYER NEURAL NETWORK FOR RECOMMENDATIONS
Authors: Oleg Rybakov, Vijai Mohan, Avishkar Misra, Scott LeGrand, Rejith Joseph, Kiuk Chung, Siddharth Singh, Qian You, Eric Nalisnick, Leo Dirac, Runfei Luo
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: GPU, Recommendation Systems, Vocal Bursts Valence Prediction

We present a personalized recommender system using neural network for recommending
products, such as eBooks, audio-books, Mobile Apps, Video and Music.
It produces recommendations based on customer’s implicit feedback history such
as purchases, listens or watches. Our key contribution is to formulate recommendation
problem as a model that encodes historical behavior to predict the future
behavior using soft data split, combining predictor and auto-encoder models. We
introduce convolutional layer for learning the importance (time decay) of the purchases
depending on their purchase date and demonstrate that the shape of the time
decay function can be well approximated by a parametrical function. We present
offline experimental results showing that neural networks with two hidden layers
can capture seasonality changes, and at the same time outperform other modeling
techniques, including our recommender in production. Most importantly, we
demonstrate that our model can be scaled to all digital categories, and we observe
significant improvements in an online A/B test. We also discuss key enhancements
to the neural network model and describe our production pipeline. Finally
we open-sourced our deep learning library which supports multi-gpu model parallel
training. This is an important feature in building neural network based recommenders
with large dimensionality of input and output data.
Iterative Deep Compression : Compressing Deep Networks for Classification and Semantic Segmentation
Authors: Sugandha Doda, Vitor Fortes Rey, Dr. Nadereh Hatami, Prof. Dr. Paul Lukowicz
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Autonomous Driving, General Classification, Quantization, Semantic Segmentation, speech-recognition, Speech Recognition

Machine learning and in particular deep learning approaches have outperformed many traditional techniques in accomplishing complex tasks such as
image classfication, natural language processing or speech recognition. Most of the state-of-the art deep networks have complex architecture and use a vast number of parameters to reach this superior performance. Though these networks use a large number of learnable parameters, those parameters present significant redundancy. Therefore, it is possible to compress the network without much affecting its accuracy by eliminating those redundant and unimportant parameters.
In this work, we propose a three stage compression pipeline, which consists of pruning, weight sharing and quantization to compress deep neural networks.
Our novel pruning technique combines magnitude based ones with dense sparse dense ideas and iteratively finds for each layer its achievable sparsity instead of selecting a single threshold for the whole network.
Unlike previous works, where compression is only applied on networks performing classification, we evaluate and perform compression on networks for classification as well as semantic segmentation, which is greatly useful for understanding scenes in autonomous driving.
We tested our method on LeNet-5 and FCNs, performing classification and semantic segmentation, respectively. With LeNet-5 on MNIST, pruning reduces the number of parameters by 15.3 times and storage requirement from 1.7 MB to 0.006 MB with accuracy loss of 0.03%. With FCN8 on Cityscapes, we decrease the number of parameters by 8 times and reduce the storage requirement from 537.47 MB to 18.23 MB with class-wise intersection-over-union (IoU) loss of 4.93% on the validation data.
Do Deep Reinforcement Learning Algorithms really Learn to Navigate?
Authors: Shurjo Banerjee, Vikas Dhiman, Brent Griffin, Jason J. Corso
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Reinforcement Learning, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning (DRL) algorithms have demonstrated progress in learning to find a goal in challenging environments. As the title of the paper by Mirowski et al. (2016) suggests, one might assume that DRL-based algorithms are able to “learn to navigate” and are thus ready to replace classical mapping and path-planning algorithms, at least in simulated environments. Yet, from experiments and analysis in this earlier work, it is not clear what strategies are used by these algorithms in navigating the mazes and finding the goal. In this paper, we pose and study this underlying question: are DRL algorithms doing some form of mapping and/or path-planning? Our experiments show that the algorithms are not memorizing the maps of mazes at the testing stage but, rather, at the training stage. Hence, the DRL algorithms fall short of qualifying as mapping or path-planning algorithms with any reasonable definition of mapping. We extend the experiments in Mirowski et al. (2016) by separating the set of training and testing maps and by a more ablative coverage of the space of experiments. Our systematic experiments show that the NavA3C-D1-D2-L algorithm, when trained and tested on the same maps, is able to choose the shorter paths to the goal. However, when tested on unseen maps the algorithm utilizes a wall-following strategy to find the goal without doing any mapping or path planning.
Towards Interpretable Chit-chat: Open Domain Dialogue Generation with Dialogue Acts
Authors: Wei Wu, Can Xu, Yu Wu, Zhoujun Li
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Dialogue Generation, Response Generation

Conventional methods model open domain dialogue generation as a black box through end-to-end learning from large scale conversation data. In this work, we make the first step to open the black box by introducing dialogue acts into open domain dialogue generation. The dialogue acts are generally designed and reveal how people engage in social chat. Inspired by analysis on real data, we propose jointly modeling dialogue act selection and response generation, and perform learning with human-human conversations tagged with a dialogue act classifier and a reinforcement approach to further optimizing the model for long-term conversation. With the dialogue acts, we not only achieve significant improvement over state-of-the-art methods on response quality for given contexts and long-term conversation in both machine-machine simulation and human-machine conversation, but also are capable of explaining why such achievements can be made.
Unleashing the Potential of CNNs for Interpretable Few-Shot Learning
Authors: Boyang Deng, Qing Liu, Siyuan Qiao, Alan Yuille
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Few-Shot Learning

Convolutional neural networks (CNNs) have been generally acknowledged as one of the driving forces for the advancement of computer vision. Despite their promising performances on many tasks, CNNs still face major obstacles on the road to achieving ideal machine intelligence. One is that CNNs are complex and hard to interpret. Another is that standard CNNs require large amounts of annotated data, which is sometimes very hard to obtain, and it is desirable to be able to learn them from few examples. In this work, we address these limitations of CNNs by developing novel, simple, and interpretable models for few-shot learn- ing. Our models are based on the idea of encoding objects in terms of visual concepts, which are interpretable visual cues represented by the feature vectors within CNNs. We first adapt the learning of visual concepts to the few-shot setting, and then uncover two key properties of feature encoding using visual concepts, which we call category sensitivity and spatial pattern. Motivated by these properties, we present two intuitive models for the problem of few-shot learning. Experiments show that our models achieve competitive performances, while being much more flexible and interpretable than alternative state-of-the-art few-shot learning methods. We conclude that using visual concepts helps expose the natural capability of CNNs for few-shot learning.
Clipping Free Attacks Against Neural Networks
Authors: Boussad ADDAD
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Malware Detection, speech-recognition, Speech Recognition

During the last years, a remarkable breakthrough has been made in AI domain
thanks to artificial deep neural networks that achieved a great success in many
machine learning tasks in computer vision, natural language processing, speech
recognition, malware detection and so on. However, they are highly vulnerable
to easily crafted adversarial examples. Many investigations have pointed out this
fact and different approaches have been proposed to generate attacks while adding
a limited perturbation to the original data. The most robust known method so far
is the so called C&W attack [1]. Nonetheless, a countermeasure known as fea-
ture squeezing coupled with ensemble defense showed that most of these attacks
can be destroyed [6]. In this paper, we present a new method we call Centered
Initial Attack (CIA) whose advantage is twofold : first, it insures by construc-
tion the maximum perturbation to be smaller than a threshold fixed beforehand,
without the clipping process that degrades the quality of attacks. Second, it is
robust against recently introduced defenses such as feature squeezing, JPEG en-
coding and even against a voting ensemble of defenses. While its application is
not limited to images, we illustrate this using five of the current best classifiers
on ImageNet dataset among which two are adversarialy retrained on purpose to
be robust against attacks. With a fixed maximum perturbation of only 1.5% on
any pixel, around 80% of attacks (targeted) fool the voting ensemble defense and
nearly 100% when the perturbation is only 6%. While this shows how it is difficult
to defend against CIA attacks, the last section of the paper gives some guidelines
to limit their impact.
The Set Autoencoder: Unsupervised Representation Learning for Sets
Authors: Malte Probst
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Machine Translation, Representation Learning

We propose the set autoencoder, a model for unsupervised representation learning for sets of elements. It is closely related to sequence-to-sequence models, which learn fixed-sized latent representations for sequences, and have been applied to a number of challenging supervised sequence tasks such as machine translation, as well as unsupervised representation learning for sequences.
In contrast to sequences, sets are permutation invariant. The proposed set autoencoder considers this fact, both with respect to the input as well as the output of the model. On the input side, we adapt a recently-introduced recurrent neural architecture using a content-based attention mechanism. On the output side, we use a stable marriage algorithm to align predictions to labels in the learning phase.
We train the model on synthetic data sets of point clouds and show that the learned representations change smoothly with translations in the inputs, preserve distances in the inputs, and that the set size is represented directly. We apply the model to supervised tasks on the point clouds using the fixed-size latent representation. For a number of difficult classification problems, the results are better than those of a model that does not consider the permutation invariance. Especially for small training sets, the set-aware model benefits from unsupervised pretraining.
CAYLEYNETS: SPECTRAL GRAPH CNNS WITH COMPLEX RATIONAL FILTERS
Authors: Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Community Detection, Deep Learning, General Classification, image-classification, Image Classification, Matrix Completion

The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains. 
In this paper, we introduce a new spectral domain convolutional architecture for deep learning on graphs. The core ingredient of our model is a new class of parametric rational complex functions (Cayley polynomials) allowing to efficiently compute spectral filters on graphs that specialize on frequency bands of interest. Our model generates rich spectral filters that are localized in space, scales linearly with the size of the input data for sparsely-connected graphs, and can handle different constructions of Laplacian operators. Extensive experimental results show the superior performance of our approach on spectral image classification, community detection, vertex classification and matrix completion tasks.
Baseline-corrected space-by-time non-negative matrix factorization for decoding single trial population spike trains
Authors: Arezoo Alizadeh, Marion Mutter, Thomas Münch, Arno Onken, Stefano Panzeri
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Tensor Decomposition

Activity of populations of sensory neurons carries stimulus information in both the temporal and the spatial dimensions. This poses the question of how to compactly represent all the information that the population codes carry across all these dimensions. Here, we developed an analytical method to factorize a large number of retinal ganglion cells' spike trains into a robust low-dimensional representation that captures efficiently both their spatial and temporal information. In particular, we extended previously used single-trial space-by-time tensor decomposition based on non-negative matrix factorization to efficiently discount pre-stimulus baseline activity. On data recorded from retinal ganglion cells with strong pre-stimulus baseline, we showed that in situations were the stimulus elicits a strong change in firing rate, our extensions yield a boost in stimulus decoding performance. Our results thus suggest that taking into account the baseline can be important for finding a compact information-rich representation of neural activity.
Avoiding Catastrophic States with Intrinsic Fear
Authors: Zachary C. Lipton, Kamyar Azizzadenesheli, Abhishek Kumar, Lihong Li, Jianfeng Gao, Li Deng
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Atari Games, Deep Reinforcement Learning, General Classification, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Many practical reinforcement learning problems contain catastrophic states that the optimal policy visits infrequently or never. Even on toy problems, deep reinforcement learners periodically revisit these states, once they are forgotten under a new policy. In this paper, we introduce intrinsic fear, a learned reward shaping that accelerates deep reinforcement learning and guards oscillating policies against periodic catastrophes. Our approach incorporates a second model trained via supervised learning to predict the probability of imminent catastrophe. This score acts as a penalty on the Q-learning objective. Our theoretical analysis demonstrates that the perturbed objective yields the same average return under strong assumptions and an $\epsilon$-close average return under weaker assumptions. Our analysis also shows robustness to classification errors. Equipped with intrinsic fear, our DQNs solve the toy environments and improve on the Atari games Seaquest, Asteroids, and Freeway.