pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Write by Learning the Objective
Authors: Ari Holtzman, Jan Buys, Maxwell Forbes, Antoine Bosselut, Yejin Choi
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Language Modeling, Language Modelling

Recurrent Neural Networks (RNNs) are powerful autoregressive sequence models for learning prevalent patterns in natural language.   Yet language generated by RNNs often shows several degenerate characteristics that are uncommon in human language; while fluent, RNN language production can be overly generic, repetitive, and even self-contradictory.  We postulate that the objective function optimized by RNN language models, which amounts to the overall perplexity of a text, is not expressive enough to capture the abstract qualities of good generation such as Grice’s Maxims. In this paper, we introduce a general learning framework that can construct a decoding objective better suited for generation. Starting with a generatively trained RNN language model, our framework learns to construct a substantially stronger generator by combining several discriminatively trained models that can collectively address the limitations of RNN generation.  Human evaluation demonstrates that text generated by the resulting generator is preferred over  that  of  baselines  by  a  large  margin  and  significantly  enhances  the  overall coherence, style, and information content of the generated text.
Key Protected Classification for GAN Attack Resilient Collaborative Learning
Authors: Mert Bülent Sarıyıldız, Ramazan Gökberk Cinbiş, Erman Ayday
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Classification, General Classification, Privacy Preserving

Large-scale publicly available datasets play a fundamental role in training deep learning models. However, large-scale
datasets are difficult to collect in problems that involve processing of sensitive information.
Collaborative learning techniques provide a privacy-preserving solution in such cases, by enabling
training over a number of private datasets that are not shared by their owners.
Existing collaborative learning
techniques, combined with differential privacy, are shown to be resilient against a passive
adversary which tries to infer the training data only from the model parameters. However, recently, it has
been shown that the existing collaborative learning techniques are vulnerable to an active adversary that runs a GAN
attack during the learning phase. In this work, we propose a novel key-based collaborative learning technique that is
resilient against such GAN attacks. For this purpose, we present a collaborative learning formulation in which class scores 
are protected by class-specific keys, and therefore, prevents a GAN attack. We also show that
very high dimensional class-specific keys can be utilized to improve robustness against attacks, without increasing the model complexity. 
Our experimental results on two popular datasets, MNIST and AT&T Olivetti Faces, demonstrate the effectiveness of the proposed technique
against the GAN attack. To the best of our knowledge, the proposed approach is the first collaborative learning
formulation that effectively tackles an active adversary, and, unlike model corruption or differential privacy formulations,
our approach does not inherently feature a trade-off between model accuracy and data privacy.
Bit-Regularized Optimization of Neural Nets
Authors: Mohamed Amer, Aswin Raghavan, Graham W. Taylor, Sek Chai
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Translation

We present a novel regularization strategy for training neural networks which we call ``BitNet''. The parameters of neural networks are usually unconstrained and have a dynamic range dispersed over a real valued range. Our key idea is to control the expressive power of the network by dynamically quantizing the range and set of values that the parameters can take. We formulate this idea using a novel end-to-end approach  that regularizes a typical classification loss function. Our regularizer is inspired by the Minimum Description Length (MDL) principle. For each layer of the network, our approach optimizes a translation and scaling factor along with integer-valued parameters. We empirically compare BitNet to an equivalent unregularized model on the MNIST and CIFAR-10 datasets. We show that BitNet converges faster to a superior quality solution. Additionally, the resulting model is significantly smaller in size due to the use of integer instead of floating-point parameters.
ShakeDrop regularization
Authors: Yoshihiro Yamada, Masakazu Iwamura, Koichi Kise
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

This paper proposes a powerful regularization method named \textit{ShakeDrop regularization}.
ShakeDrop is inspired by Shake-Shake regularization that decreases error rates by disturbing learning.
While Shake-Shake can be applied to only ResNeXt which has multiple branches, ShakeDrop can be applied to not only ResNeXt but also ResNet, Wide ResNet and PyramidNet in a memory efficient way.
Important and interesting feature of ShakeDrop is that it strongly disturbs learning by multiplying even a negative factor to the output of a convolutional layer in the forward training pass.
The effectiveness of ShakeDrop is confirmed by experiments on CIFAR-10/100 and Tiny ImageNet datasets.
AUTOMATA GUIDED HIERARCHICAL REINFORCEMENT LEARNING FOR ZERO-SHOT SKILL COMPOSITION
Authors: Xiao Li, Yao Ma, Calin Belta
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Reinforcement Learning, Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

An obstacle that prevents the wide adoption of (deep) reinforcement learning (RL) in control systems is its need for a large number of interactions with the environment in order to master a skill. The learned skill usually generalizes poorly across domains and re-training is often necessary when presented with a new task. We present a framework that combines techniques in \textit{formal methods} with \textit{hierarchical reinforcement learning} (HRL). The set of techniques we provide allows for the convenient specification of tasks with logical expressions, learns hierarchical policies (meta-controller and low-level controllers) with well-defined intrinsic rewards using any RL methods and is able to construct new skills from existing ones without additional learning. We evaluate the proposed methods in a simple grid world simulation as well as simulation on a Baxter robot.
Jiffy: A Convolutional Approach to Learning Time Series Similarity
Authors: Divya Shanmugam, Davis Blalock, John Guttag
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Representation Learning, Time Series, Time Series Analysis

Computing distances between examples is at the core of many learning algorithms for time series. Consequently, a great deal of work has gone into designing effective time series distance measures. We present Jiffy, a simple and scalable distance metric for multivariate time series. Our approach is to reframe the task as a representation learning problem---rather than design an elaborate distance function, we use a CNN to learn an embedding such that the Euclidean distance is effective. By aggressively max-pooling and downsampling, we are able to construct this embedding using a highly compact neural network. Experiments on a diverse set of multivariate time series datasets show that our approach consistently outperforms existing methods.
Transfer Learning on Manifolds via Learned Transport Operators
Authors: Marissa Connor, Christopher Rozell
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Data Augmentation, Few-Shot Image Classification, General Classification, Image Animation, image-classification, Image Classification, Transfer Learning

Within-class variation in a high-dimensional dataset can be modeled as being on a low-dimensional manifold due to the constraints of the physical processes producing that variation (e.g., translation, illumination, etc.). We desire a method for learning a representation of the manifolds induced by identity-preserving transformations that can be used to increase robustness, reduce the training burden, and encourage interpretability in machine learning tasks. In particular, what is needed is a representation of the transformation manifold that can robustly capture the shape of the manifold from the input data, generate new points on the manifold, and extend transformations outside of the training domain without significantly increasing the error. Previous work has proposed algorithms to efficiently learn analytic operators (called transport operators) that define the process of transporting one data point on a manifold to another.  The main contribution of this paper is to define two transfer learning methods that use this generative manifold representation to learn natural transformations and incorporate them into new data. The first method uses this representation in a novel randomized approach to transfer learning that employs the learned generative model to map out unseen regions of the data space. These results are shown through demonstrations of transfer learning in a data augmentation task for few-shot image classification. The second method use of transport operators for injecting specific transformations into new data examples which allows for realistic image animation and informed data augmentation.  These results are shown on stylized constructions using the classic swiss roll data structure and in demonstrations of transfer learning in a data augmentation task for few-shot image classification. We also propose the use of transport operators for injecting transformations into new data examples which allows for realistic image animation.
Demystifying overcomplete nonlinear auto-encoders: fast SGD convergence towards sparse representation from random initialization
Authors: Cheng Tang, Claire Monteleoni
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Dictionary Learning, Representation Learning

Auto-encoders are commonly used for unsupervised representation learning and for pre-training deeper neural networks.
When its activation function is linear and the encoding dimension (width of hidden layer) is smaller than the input dimension, it is well known that auto-encoder is optimized to learn the principal components of the data distribution (Oja1982).
However, when the activation is nonlinear and when the width is larger than the input dimension (overcomplete), auto-encoder behaves differently from PCA, and in fact is known to perform well empirically for sparse coding problems. 

We provide a theoretical explanation for this empirically observed phenomenon, when rectified-linear unit (ReLu) is adopted as the activation function and the hidden-layer width is set to be large.
In this case, we show that, with significant probability, initializing the weight matrix of an auto-encoder by sampling from a spherical Gaussian distribution followed by stochastic gradient descent (SGD) training converges towards the ground-truth representation for a class of sparse dictionary learning models.
In addition, we can show that, conditioning on convergence, the expected convergence rate is O(1/t), where t is the number of updates.
Our analysis quantifies how increasing hidden layer width helps the training performance when random initialization is used, and how the norm of network weights influence the speed of SGD convergence.
Gated ConvNets for Letter-Based ASR
Authors: Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Decoder, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

In this paper we introduce a new speech recognition system, leveraging a simple letter-based ConvNet acoustic model. The acoustic model requires only audio transcription for training -- no alignment annotations, nor any forced alignment step is needed. At inference, our decoder takes only a word list and a language model, and is fed with letter scores from the acoustic model -- no phonetic word lexicon is needed. Key ingredients for the acoustic model are Gated Linear Units and high dropout. We show near state-of-the-art results in word error rate on the LibriSpeech corpus with MFSC features, both on the clean and other configurations.
The Information-Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Modeling
Authors: Shengjia Zhao, Jiaming Song, Stefano Ermon
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

A variety of learning objectives have been recently proposed for training generative models. We show that many of them, including InfoGAN, ALI/BiGAN, ALICE, CycleGAN, VAE, $\beta$-VAE, adversarial autoencoders, AVB, and InfoVAE, are Lagrangian duals of the same primal optimization problem. This generalization reveals the implicit modeling trade-offs  between flexibility and  computational requirements being made by these models. Furthermore, we characterize the class of all objectives that can be optimized under certain computational constraints.
Finally, we show how this new Lagrangian perspective can explain undesirable behavior of existing methods and provide new principled solutions.
Faster Reinforcement Learning with Expert State Sequences
Authors: Xiaoxiao Guo, Shiyu Chang, Mo Yu, Miao Liu, Gerald Tesauro
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Imitation learning relies on expert demonstrations. Existing approaches often re- quire that the complete demonstration data, including sequences of actions and states are available. In this paper, we consider a realistic and more difficult sce- nario where a reinforcement learning agent only has access to the state sequences of an expert, while the expert actions are not available. Inferring the unseen ex- pert actions in a stochastic environment is challenging and usually infeasible when combined with a large state space. We propose a novel policy learning method which only utilizes the expert state sequences without inferring the unseen ac- tions. Specifically, our agent first learns to extract useful sub-goal information from the state sequences of the expert and then utilizes the extracted sub-goal information to factorize the action value estimate over state-action pairs and sub- goals. The extracted sub-goals are also used to synthesize guidance rewards in the policy learning. We evaluate our agent on five Doom tasks. Our empirical results show that the proposed method significantly outperforms the conventional DQN method.
Training Neural Machines with Partial Traces
Authors: Matthew Mirman, Dimitar Dimitrov, Pavle Djordjevich, Timon Gehr, Martin Vechev
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

We present a novel approach for training neural abstract architectures which in- corporates (partial) supervision over the machine’s interpretable components. To cleanly capture the set of neural architectures to which our method applies, we introduce the concept of a differential neural computational machine (∂NCM) and show that several existing architectures (e.g., NTMs, NRAMs) can be instantiated as a ∂NCM and can thus benefit from any amount of additional supervision over their interpretable components. Based on our method, we performed a detailed experimental evaluation with both, the NTM and NRAM architectures, and showed that the approach leads to significantly better convergence and generalization capabilities of the learning phase than when training using only input-output examples.
STRUCTURED ALIGNMENT NETWORKS
Authors: Yang Liu, Matt Gardner
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Natural Language Inference, Sentence

 Many tasks in natural language processing involve comparing two sentences to compute some notion of relevance, entailment, or similarity. Typically this comparison is done either at the word level or at the sentence level, with no attempt to leverage the inherent structure of the sentence. When sentence structure is used for comparison, it is obtained during a non-differentiable pre-processing step, leading to propagation of errors. We introduce a model of structured alignments between sentences, showing how to compare two sentences by matching their latent structures. Using a structured attention mechanism, our model matches possible spans in the first sentence to possible spans in the second sentence, simultaneously discovering the tree structure of each sentence and performing a comparison, in a model that is fully differentiable and is trained only on the comparison objective. We evaluate this model on two sentence comparison tasks: the Stanford natural language inference dataset and the TREC-QA dataset. We find that comparing spans results in superior performance to comparing words individually, and that the learned trees are consistent with actual linguistic structures.
GATED FAST WEIGHTS FOR ASSOCIATIVE RETRIEVAL
Authors: Imanol Schlag, Jürgen Schmidhuber
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Retrieval

We improve previous end-to-end differentiable neural networks (NNs) with fast
weight memories. A gate mechanism updates fast weights at every time step of
a sequence through two separate outer-product-based matrices generated by slow
parts of the net. The system is trained on a complex sequence to sequence variation
of the Associative Retrieval Problem with roughly 70 times more temporal
memory (i.e. time-varying variables) than similar-sized standard recurrent NNs
(RNNs). In terms of accuracy and number of parameters, our architecture outperforms
a variety of RNNs, including Long Short-Term Memory, Hypernetworks,
and related fast weight architectures.
POLICY DRIVEN GENERATIVE ADVERSARIAL NETWORKS FOR ACCENTED SPEECH GENERATION
Authors: Prannay Khosla, Preethi Jyothi, Vinay P. Namboodiri, Mukundhan Srinivasan
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Speech Synthesis

In this paper, we propose the generation of accented speech using generative adversarial
networks. Through this work we make two main contributions a) The
ability to condition latent representations while generating realistic speech samples
b) The ability to efficiently generate long speech samples by using a novel
latent variable transformation module that is trained using policy gradients. Previous
methods are limited in being able to generate only relatively short samples
or are not very efficient at generating long samples. The generated speech samples
are validated through a number of various evaluation measures viz, a WGAN
critic loss and through subjective scores on user evaluations against competitive
speech synthesis baselines and detailed ablation analysis of the proposed model.
The evaluations demonstrate that the model generates realistic long speech samples
conditioned on accent efficiently.
Autonomous Vehicle Fleet Coordination With Deep Reinforcement Learning
Authors: Cane Punma
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Autonomous Vehicles, Decision Making, Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Autonomous vehicles are becoming more common in city transportation.  Companies will begin to find a need to teach these vehicles smart city fleet coordination.  Currently, simulation based modeling along with hand coded rules dictate the decision making of these autonomous vehicles. We believe that complex intelligent behavior can be learned by these agents through Reinforcement Learning.In this paper, we discuss our work for solving this system by adapting the Deep Q-Learning (DQN) model to the multi-agent setting.  Our approach applies deep reinforcement learning by combining convolutional neural networks with DQN to teach agents to fulfill customer demand in an environment that is partially observ-able to them. We also demonstrate how to utilize transfer learning to teach agents to balance multiple objectives such as navigating to a charging station when its en-ergy level is low. The two evaluations presented show that our solution has shown  hat we are successfully able to teach agents cooperation policies while balancing multiple objectives.
Learning Representations for Faster Similarity Search
Authors: Ludwig Schmidt, Kunal Talwar
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: General Classification

In high dimensions, the performance of nearest neighbor algorithms depends crucially on structure in the data.
While traditional nearest neighbor datasets consisted mostly of hand-crafted feature vectors, an increasing number of datasets comes from representations learned with neural networks.
We study the interaction between nearest neighbor algorithms and neural networks in more detail.
We find that the network architecture can significantly influence the efficacy of nearest neighbor algorithms even when the classification accuracy is unchanged.
Based on our experiments, we propose a number of training modifications that lead to significantly better datasets for nearest neighbor algorithms.
Our modifications lead to learned representations that can accelerate nearest neighbor queries by 5x.
TOWARDS ROBOT VISION MODULE DEVELOPMENT WITH EXPERIENTIAL ROBOT LEARNING
Authors: Ahmed A Aly, Joanne Bechta Dugan
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: object-detection, Object Detection, Semi-Supervised Object Detection

n this paper we present a thrust in three directions of visual development us- ing supervised and semi-supervised techniques. The first is an implementation of semi-supervised object detection and recognition using the principles of Soft At- tention and Generative Adversarial Networks (GANs). The second and the third are supervised networks that learn basic concepts of spatial locality and quantity respectively using Convolutional Neural Networks (CNNs). The three thrusts to- gether are based on the approach of Experiential Robot Learning, introduced in previous publication. While the results are unripe for implementation, we believe they constitute a stepping stone towards autonomous development of robotic vi- sual modules.
Towards a Testable Notion of Generalization for Generative Adversarial Networks
Authors: Robert Cornish, Hongseok Yang, Frank Wood
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Generative Adversarial Network

We consider the question of how to assess generative adversarial networks, in particular with respect to whether or not they generalise beyond memorising the training data. We propose a simple procedure for assessing generative adversarial network performance based on a principled consideration of what the actual goal of generalisation is. Our approach involves using a test set to estimate the Wasserstein distance between the generative distribution produced by our procedure, and the underlying data distribution. We use this procedure to assess the performance of several modern generative adversarial network architectures. We find that this procedure is sensitive to the choice of ground metric on the underlying data space, and suggest a choice of ground metric that substantially improves performance.  We finally suggest that attending to the ground metric used in Wasserstein generative adversarial network training may be fruitful, and outline a concrete pathway towards doing so.