pwc-archive/papers-with-abstracts huggingface dataset:
3C-GAN: AN CONDITION-CONTEXT-COMPOSITE GENERATIVE ADVERSARIAL NETWORKS FOR GENERATING IMAGES SEPARATELY
Authors: Yeu-Chern Harn, Vladimir Jojic
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

We present 3C-GAN: a novel multiple generators structures, that contains one conditional generator that generates a semantic part of an image conditional on its input label, and one context generator generates the rest of an image. Compared to original GAN model, this model has multiple generators and gives control over what its generators should generate. Unlike previous multi-generator models use a subsequent generation process, that one layer is generated given the previous layer, our model uses a process of generating different part of the images together. This way the model contains fewer parameters and the generation speed is faster. Speciﬁcally, the model leverages the label information to separate the object from the image correctly. Since the model conditional on the label information does not restrict to generate other parts of an image, we proposed a cost function that encourages the model to generate only the succinct part of an image in terms of label discrimination. We also found an exclusive prior on the mask of the model help separate the object. The experiments on MNIST, SVHN, and CelebA datasets show 3C-GAN can generate different objects with different generators simultaneously, according to the labels given to each generator.
Alpha-divergence bridges maximum likelihood and reinforcement learning in neural sequence generation
Authors: Sotetsu Koyamada, Yuta Kikuchi, Atsunori Kanemura, Shin-ichi Maeda, Shin Ishii
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Machine Translation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Translation

Neural sequence generation is commonly approached by using maximum- likelihood (ML) estimation or reinforcement learning (RL). However, it is known that they have their own shortcomings; ML presents training/testing discrepancy, whereas RL suffers from sample inefficiency. We point out that it is difficult to resolve all of the shortcomings simultaneously because of a tradeoff between ML and RL. In order to counteract these problems, we propose an objective function for sequence generation using α-divergence, which leads to an ML-RL integrated method that exploits better parts of ML and RL. We demonstrate that the proposed objective function generalizes ML and RL objective functions because it includes both as its special cases (ML corresponds to α → 0 and RL to α → 1). We provide a proposition stating that the difference between the RL objective function and the proposed one monotonically decreases with increasing α. Experimental results on machine translation tasks show that minimizing the proposed objective function achieves better sequence generation performance than ML-based methods.
Don't encrypt the data; just approximate the model \ Towards Secure Transaction and Fair Pricing of Training Data
Authors: Xinlei Xu
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Federated Learning, Model Compression, Model extraction

As machine learning becomes ubiquitous, deployed systems need to be as accu- rate as they can. As a result, machine learning service providers have a surging need for useful, additional training data that benefits training, without giving up all the details about the trained program. At the same time, data owners would like to trade their data for its value, without having to first give away the data itself be- fore receiving compensation. It is difficult for data providers and model providers to agree on a fair price without first revealing the data or the trained model to the other side. Escrow systems only complicate this further, adding an additional layer of trust required of both parties. Currently, data owners and model owners don’t have a fair pricing system that eliminates the need to trust a third party and training the model on the data, which 1) takes a long time to complete, 2) does not guarantee that useful data is paid valuably and that useless data isn’t, without trusting in the third party with both the model and the data. Existing improve- ments to secure the transaction focus heavily on encrypting or approximating the data, such as training on encrypted data, and variants of federated learning. As powerful as the methods appear to be, we show them to be impractical in our use case with real world assumptions for preserving privacy for the data owners when facing black-box models. Thus, a fair pricing scheme that does not rely on secure data encryption and obfuscation is needed before the exchange of data. This pa- per proposes a novel method for fair pricing using data-model efficacy techniques such as influence functions, model extraction, and model compression methods, thus enabling secure data transactions. We successfully show that without running the data through the model, one can approximate the value of the data; that is, if the data turns out redundant, the pricing is minimal, and if the data leads to proper improvement, its value is properly assessed, without placing strong assumptions on the nature of the model. Future work will be focused on establishing a system with stronger transactional security against adversarial attacks that will reveal details about the model or the data to the other party.
An inference-based policy gradient method for learning options
Authors: Matthew J. A. Smith, Herke van Hoof, Joelle Pineau
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

In the pursuit of increasingly intelligent learning systems, abstraction plays a vital role in enabling sophisticated decisions to be made in complex environments. The options framework provides formalism for such abstraction over sequences of decisions.  However most models require that options be given a priori, presumably specified by hand, which is neither efficient, nor scalable. Indeed, it is preferable to learn options directly from interaction with the environment. Despite several efforts, this remains a difficult problem: many approaches require access to a model of the environmental dynamics, and inferred options are often not interpretable, which limits our ability to explain the system behavior for verification or debugging purposes.  In this work we develop a novel policy gradient method for the automatic learning of policies with options.  This algorithm uses inference methods to simultaneously improve all of the options available to an agent, and thus can be employed in an off-policy manner, without observing option labels. Experimental results show that the options learned can be interpreted. Further, we find that the method presented here is more sample efficient than existing methods, leading to faster and more stable learning of policies with options.
What is image captioning made of?
Authors: Pranava Madhyastha, Josiah Wang, Lucia Specia
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Image Captioning, Text Generation

We hypothesize that end-to-end neural image captioning systems work seemingly well because they exploit and learn ‘distributional similarity’ in a multimodal feature space, by mapping a test image to similar training images in this space and generating a caption from the same space. To validate our hypothesis, we focus on the ‘image’ side of image captioning, and vary the input image representation but keep the RNN text generation model of a CNN-RNN constant. We propose a sparse bag-of-objects vector as an interpretable representation to investigate our distributional similarity hypothesis. We found that image captioning models (i) are capable of separating structure from noisy input representations; (ii) experience virtually no significant performance loss when a high dimensional representation is compressed to a lower dimensional space; (iii) cluster images with similar visual and linguistic information together; (iv) are heavily reliant on test sets with a similar distribution as the training set; (v) repeatedly generate the same captions by matching images and ‘retrieving’ a caption in the joint visual-textual space. Our experiments all point to one fact: that our distributional similarity hypothesis holds. We conclude that, regardless of the image representation, image captioning systems seem to match images and generate captions in a learned joint image-text semantic subspace.
TD Learning with Constrained Gradients
Authors: Ishan Durugkar, Peter Stone
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Q-Learning

Temporal Difference Learning with function approximation is known to be unstable. Previous work like \citet{sutton2009fast} and \citet{sutton2009convergent} has presented alternative objectives that are stable to minimize. However, in practice, TD-learning with neural networks requires various tricks like using a target network that updates slowly \citep{mnih2015human}. In this work we propose a constraint on the TD update that minimizes change to the target values. This constraint can be applied to the gradients of any TD objective, and can be easily applied to nonlinear function approximation. We validate this update by applying our technique to deep Q-learning, and training without a target network. We also show that adding this constraint on Baird's counterexample keeps Q-learning from diverging.
WSNet: Learning Compact and Efficient Networks with Weight Sampling
Authors: Xiaojie Jin, Yingzhen Yang, Ning Xu, Jianchao Yang, Jiashi Feng, Shuicheng Yan
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Audio Classification, General Classification, Paper generation, Quantization

	We present a new approach and a novel architecture, termed WSNet, for learning compact and efficient deep neural networks. Existing approaches conventionally learn full model parameters independently and then compress them via \emph{ad hoc} processing such as model pruning or filter factorization. Alternatively, WSNet proposes learning model parameters by sampling from a compact set of learnable parameters, which naturally enforces {parameter sharing} throughout the learning process. We demonstrate that such a novel weight sampling approach (and induced WSNet) promotes both weights and computation sharing favorably. By employing this method, we can more efficiently learn much smaller networks with competitive performance compared to baseline networks with equal numbers of convolution filters. Specifically, we consider learning compact and efficient 1D convolutional neural networks for audio classification. Extensive experiments on multiple audio classification datasets verify the effectiveness of WSNet. Combined with weight quantization, the resulted models are up to \textbf{180$\times$} smaller and theoretically up to \textbf{16$\times$} faster than the well-established baselines, without noticeable performance drop.
Generative Entity Networks: Disentangling Entitites and Attributes in Visual Scenes using Partial Natural Language Descriptions
Authors: Charlie Nash, Sebastian Nowozin, Nate Kushman
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Question Answering

Generative image models have made significant progress in the last few years, and are now able to generate low-resolution images which sometimes look realistic. However the state-of-the-art models utilize fully entangled latent representations where small changes to a single neuron can effect every output pixel in relatively arbitrary ways, and different neurons have possibly arbitrary relationships with each other. This limits the ability of such models to generalize to new combinations or orientations of objects as well as their ability to connect with more structured representations such as natural language, without explicit strong supervision. In this work explore the synergistic effect of using partial natural language scene descriptions to help disentangle the latent entities visible an image. We present a novel neural network architecture called Generative Entity Networks, which jointly generates both the natural language descriptions and the images from a set of latent entities. Our model is based on the variational autoencoder framework and makes use of visual attention to identify and characterise the visual attributes of each entity. Using the Shapeworld dataset, we show that our representation both enables a better generative model of images, leading to higher quality image samples, as well as creating more semantically useful representations that improve performance over purely dicriminative models on a simple natural language yes/no question answering task.
Prediction Under Uncertainty with Error Encoding Networks
Authors: Mikael Henaff, Junbo Zhao, Yann Lecun
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Prediction, Video Prediction

In this work we introduce a new framework for performing temporal predictions
in the presence of uncertainty. It is based on a simple idea of disentangling com-
ponents of the future state which are predictable from those which are inherently
unpredictable, and encoding the unpredictable components into a low-dimensional
latent variable which is fed into the forward model. Our method uses a simple su-
pervised training objective which is fast and easy to train. We evaluate it in the
context of video prediction on multiple datasets and show that it is able to consi-
tently generate diverse predictions without the need for alternating minimization
over a latent space or adversarial training.
Noise-Based Regularizers for Recurrent Neural Networks
Authors: Adji B. Dieng, Jaan Altosaar, Rajesh Ranganath, David M. Blei
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Language Modeling, Language Modelling

Recurrent neural networks (RNNs) are powerful models for sequential data. They can approximate arbitrary computations, and have been used successfully in domains such as text and speech. However, the flexibility of RNNs makes them susceptible to overfitting and regularization is important. We develop a noise-based regularization method for RNNs. The idea is simple and easy to implement: we inject noise in the hidden units of the RNN and then maximize the original RNN's likelihood averaged over the injected noise. On a language modeling benchmark, our method achieves better performance than the deterministic RNN and the variational dropout.
Deep learning mutation prediction enables early stage lung cancer detection in liquid biopsy
Authors: Steven T. Kothen-Hill, Asaf Zviran, Rafael C. Schulman, Sunil Deochand, Federico Gaiti, Dillon Maloney, Kevin Y. Huang, Will Liao, Nicolas Robine, Nathaniel D. Omans, Dan A. Landau
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

Somatic cancer mutation detection at ultra-low variant allele frequencies (VAFs) is an unmet challenge that is intractable with current state-of-the-art mutation calling methods. Specifically, the limit of VAF detection is closely related to the depth of coverage, due to the requirement of multiple supporting reads in extant methods, precluding the detection of mutations at VAFs that are orders of magnitude lower than the depth of coverage. Nevertheless, the ability to detect cancer-associated mutations in ultra low VAFs is a fundamental requirement for low-tumor burden cancer diagnostics applications such as early detection, monitoring, and therapy nomination using liquid biopsy methods (cell-free DNA). Here we defined a spatial representation of sequencing information adapted for convolutional architecture that enables variant detection at VAFs, in a manner independent of the depth of sequencing. This method enables the detection of cancer mutations even in VAFs as low as 10x-4^, >2 orders of magnitude below the current state-of-the-art. We validated our method on both simulated plasma and on clinical cfDNA plasma samples from cancer patients and non-cancer controls. This method introduces a new domain within bioinformatics and personalized medicine – somatic whole genome mutation calling for liquid biopsy.
A novel method to determine the number of latent dimensions with SVD
Authors: Asana Neishabouri, Michel Desmarais
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Clustering

Determining the number of latent dimensions is a ubiquitous problem in machine
learning. In this study, we introduce a novel method that relies on SVD to discover
the number of latent dimensions. The general principle behind the method is to
compare the curve of singular values of the SVD decomposition of a data set with
the randomized data set curve. The inferred number of latent dimensions corresponds
to the crossing point of the two curves. To evaluate our methodology, we
compare it with competing methods such as Kaisers eigenvalue-greater-than-one
rule (K1), Parallel Analysis (PA), Velicers MAP test (Minimum Average Partial).
We also compare our method with the Silhouette Width (SW) technique which is
used in different clustering methods to determine the optimal number of clusters.
The result on synthetic data shows that the Parallel Analysis and our method have
similar results and more accurate than the other methods, and that our methods is
slightly better result than the Parallel Analysis method for the sparse data sets.
A Goal-oriented Neural Conversation Model by Self-Play
Authors: Wei Wei, Quoc V. Le, Andrew M. Dai, Li-Jia Li
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Language Modeling, Language Modelling, Natural Language Understanding

Building chatbots that can accomplish goals such as booking a flight ticket is an unsolved problem in natural language understanding. Much progress has been made to build conversation models using techniques such as sequence2sequence modeling. One challenge in applying such techniques to building goal-oriented conversation models is that maximum likelihood-based models are not optimized toward accomplishing goals. Recently, many methods have been proposed to address this issue by optimizing a reward that contains task status or outcome. However, adding the reward optimization on the fly usually provides little guidance for language construction and the conversation model soon becomes decoupled from the language model. In this paper, we propose a new setting in goal-oriented dialogue system to tighten the gap between these two aspects by enforcing model level information isolation on individual models between two agents. Language construction now becomes an important part in reward optimization since it is the only way information can be exchanged. We experimented our models using self-play and results showed that our method not only beat the baseline sequence2sequence model in rewards but can also generate human-readable meaningful conversations of comparable quality.
Video Action Segmentation with Hybrid Temporal Networks
Authors: Li Ding, Chenliang Xu
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Action Segmentation, Decoder, Segmentation

Action segmentation as a milestone towards building automatic systems to understand untrimmed videos has received considerable attention in the recent years. It is typically being modeled as a sequence labeling problem but contains intrinsic and sufficient differences than text parsing or speech processing. In this paper, we introduce a novel hybrid temporal convolutional and recurrent network (TricorNet), which has an encoder-decoder architecture: the encoder consists of a hierarchy of temporal convolutional kernels that capture the local motion changes of different actions; the decoder is a hierarchy of recurrent neural networks that are able to learn and memorize long-term action dependencies after the encoding stage. Our model is simple but extremely effective in terms of video sequence labeling. The experimental results on three public action segmentation datasets have shown that the proposed model achieves superior performance over the state of the art.
The Variational Homoencoder: Learning to Infer High-Capacity Generative Models from Few Examples
Authors: Luke Hewitt, Andrea Gane, Tommi Jaakkola, Joshua B. Tenenbaum
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: General Classification

Hierarchical Bayesian methods have the potential to unify many related tasks (e.g. k-shot classification, conditional, and unconditional generation) by framing each as inference within a single generative model. We show that existing approaches for learning such models can fail on expressive generative networks such as PixelCNNs, by describing the global distribution with little reliance on latent variables. To address this, we develop a modification of the Variational Autoencoder in which encoded observations are decoded to new elements from the same class; the result, which we call a Variational Homoencoder (VHE), may be understood as training a hierarchical latent variable model which better utilises latent variables in these cases. Using this framework enables us to train a hierarchical PixelCNN for the Omniglot dataset, outperforming all existing models on test set likelihood. With a single model we achieve both strong one-shot generation and near human-level classification, competitive with state-of-the-art discriminative classifiers. The VHE objective extends naturally to richer dataset structures such as factorial or hierarchical categories, as we illustrate by training models to separate character content from simple variations in drawing style, and to generalise the style of an alphabet to new characters.
Hyperedge2vec: Distributed Representations for Hyperedges
Authors: Ankit Sharma, Shafiq Joty, Himanshu Kharkwal, Jaideep Srivastava
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Probabilistic Deep Learning, Sentence

Data structured in form of overlapping or non-overlapping sets is found in a variety of domains, sometimes explicitly but often subtly. For example, teams, which are of prime importance in social science studies are \enquote{sets of individuals}; \enquote{item sets} in pattern mining are sets; and for various types of analysis in language studies a sentence can be considered as a \enquote{set or bag of words}. Although building models and inference algorithms for structured data has been an important task in the fields of machine learning and statistics, research on \enquote{set-like} data still remains less explored. Relationships between pairs of elements can be modeled as edges in a graph. However, modeling relationships that involve all members of a set, a hyperedge is a more natural representation for the set. In this work, we focus on the problem of embedding hyperedges in a hypergraph (a network of overlapping sets) to a low dimensional vector space. We propose a probabilistic deep-learning based method as well as a tensor-based algebraic model, both of which capture the hypergraph structure in a principled manner without loosing set-level information. Our central focus is to highlight the connection between hypergraphs (topology), tensors (algebra) and probabilistic models. We present a number of interesting baselines, some of which adapt existing node-level embedding models to the hyperedge-level, as well as sequence based language techniques which are adapted for set structured hypergraph topology. The performance is evaluated with a network of social groups and a network of word phrases. Our experiments show that accuracy wise our methods perform similar to those of baselines which are not designed for hypergraphs. Moreover, our tensor based method is quiet efficient as compared to deep-learning based auto-encoder method. We therefore, argue that we have proposed more general methods which are suited for hypergraphs (and therefore also for graphs) while maintaining accuracy and efficiency.