pwc-archive/papers-with-abstracts huggingface dataset:
Question Generation using a Scratchpad Encoder
Authors: Ryan Y. Benmalek, Madian Khabsa, Suma Desu, Claire Cardie, Michele Banko
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Decoder, Question Generation, Question-Generation

In this paper we introduce the Scratchpad Encoder, a novel addition to the sequence to sequence (seq2seq) framework and explore its effectiveness in generating natural language questions from a given logical form. The Scratchpad encoder enables the decoder at each time step to modify all the encoder outputs, thus using the encoder as a "scratchpad" memory to keep track of what has been generated so far and to guide future generation. Experiments on a knowledge based question generation dataset show that our approach generates more fluent and expressive questions according to quantitative metrics and human judgments.
KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks
Authors: James Jordon, Jinsung Yoon, Mihaela van der Schaar
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: feature selection

Feature selection is a pervasive problem. The discovery of relevant features can be as important for performing a particular task (such as to avoid overfitting in prediction) as it can be for understanding the underlying processes governing the true label (such as discovering relevant genetic factors for a disease). Machine learning driven feature selection can enable discovery from large, high-dimensional, non-linear observational datasets by creating a subset of features for experts to focus on. In order to use expert time most efficiently, we need a principled methodology capable of controlling the False Discovery Rate. In this work, we build on the promising Knockoff framework by developing a flexible knockoff generation model. We adapt the Generative Adversarial Networks framework to allow us to generate knockoffs with no assumptions on the feature distribution. Our model consists of 4 networks, a generator, a discriminator, a stability network and a power network. We demonstrate the capability of our model to perform feature selection, showing that it performs as well as the originally proposed knockoff generation model in the Gaussian setting and that it outperforms the original model in non-Gaussian settings, including on a real-world dataset.
Representation-Constrained Autoencoders and an Application to Wireless Positioning
Authors: Pengzhi Huang, Emre Gonultas, Said Medjkouh, Oscar Castaneda, Olav Tirkkonen, Tom Goldstein, Christoph Studer
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Dimensionality Reduction

In a number of practical applications that rely on dimensionality reduction, the dataset or measurement process provides valuable side information that can be incorporated when learning low-dimensional embeddings. We propose the inclusion of pairwise representation constraints into autoencoders (AEs) with the goal of promoting application-specific structure. We use synthetic results to show that only a small amount of AE representation constraints are required to substantially improve the local and global neighborhood preserving properties of the learned embeddings. To demonstrate the efficacy of our approach and to illustrate a practical application that naturally provides such representation constraints, we focus on wireless positioning using a recently proposed channel charting framework. We show that representation-constrained AEs recover the global geometry of the learned low-dimensional representations, which enables channel charting to perform approximate positioning without access to global navigation satellite systems or supervised learning methods that rely on extensive measurement campaigns.
Dimensionality Reduction for Representing the Knowledge of Probabilistic Models
Authors: Marc T. Law, Jake Snell, Amir-Massoud Farahmand, Raquel Urtasun, Richard S. Zemel
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Classification, Dimensionality Reduction, General Classification, Zero-Shot Learning

Most deep learning models rely on expressive high-dimensional representations to achieve good performance on tasks such as classification. However, the high dimensionality of these representations makes them difficult to interpret and prone to over-fitting. We propose a simple, intuitive and scalable dimension reduction framework that takes into account the soft probabilistic interpretation of standard deep models for classification. When applying our framework to visualization, our representations more accurately reflect inter-class distances than standard visualization techniques such as t-SNE. We show experimentally that our framework improves generalization performance to unseen categories in zero-shot learning. We also provide a finite sample error upper bound guarantee for the method.
Learn From Neighbour: A Curriculum That Train Low Weighted Samples By Imitating
Authors: Benyuan Sun, Yizhou Wang
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

Deep neural networks, which gain great success in a wide spectrum of applications, are often time, compute and storage hungry. Curriculum learning proposed to boost training of network by a syllabus from easy to hard. However, the relationship between data complexity and network training is unclear: why hard example harm the performance at beginning but helps at end. In this paper, we aim to investigate on this problem. Similar to internal covariate shift in network forward pass, the distribution changes in weight of top layers also affects training of preceding layers during the backward pass. We call this phenomenon inverse "internal covariate shift". Training hard examples aggravates the distribution shifting and damages the training. To address this problem, we introduce a curriculum loss that consists of two parts: a) an adaptive weight that mitigates large early punishment; b) an additional representation loss for low weighted samples. The intuition of the loss is very simple. We train top layers on "good" samples to reduce large shifting, and encourage "bad" samples to learn from "good" sample. In detail, the adaptive weight assigns small values to hard examples, reducing the influence of noisy gradients. On the other hand, the less-weighted hard sample receives the proposed representation loss. Low-weighted data gets nearly no training signal and can stuck in embedding space for a long time. The proposed representation loss aims to encourage their training. This is done by letting them learn a better representation from its superior neighbours but not participate in learning of top layers. In this way, the fluctuation of top layers is reduced and hard samples also received signals for training. We found in this paper that curriculum learning needs random sampling between tasks for better training. Our curriculum loss is easy to combine with existing stochastic algorithms like SGD. Experimental result shows an consistent improvement over several benchmark datasets.
Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach
Authors: Saeed Amizadeh, Sergiy Matusevych, Markus Weimer
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Combinatorial Optimization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

Recent efforts to combine Representation Learning with Formal Methods, commonly known as the Neuro-Symbolic Methods, have given rise to a new trend of applying rich neural architectures to solve classical combinatorial optimization problems. In this paper, we propose a neural framework that can learn to solve the Circuit Satisfiability problem. Our framework is built upon two fundamental contributions: a rich embedding architecture that encodes the problem structure and an end-to-end differentiable training procedure that mimics Reinforcement Learning and trains the model directly toward solving the SAT problem. The experimental results show the superior out-of-sample generalization performance of our framework compared to the recently developed NeuroSAT method.
On the Statistical and Information Theoretical Characteristics of DNN Representations
Authors: Daeyoung Choi, Wonjong Rhee, Kyungeun Lee, Changho Shin
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

It has been common to argue or imply that a regularizer can be used to alter a statistical property of a hidden layer's representation and thus improve generalization or performance of deep networks. For instance, dropout has been known to improve performance by reducing co-adaptation, and representational sparsity has been argued as a good characteristic because many data-generation processes have only a small number of factors that are independent. In this work, we analytically and empirically investigate the popular characteristics of learned representations, including correlation, sparsity, dead unit, rank, and mutual information, and disprove many of the \textit{conventional wisdom}. We first show that infinitely many Identical Output Networks (IONs) can be constructed for any deep network with a linear layer, where any invertible affine transformation can be applied to alter the layer's representation characteristics. The existence of ION proves that the correlation characteristics of representation can be either low or high for a well-performing network. Extensions to ReLU layers are provided, too. Then, we consider sparsity, dead unit, and rank to show that only loose relationships exist among the three characteristics. It is shown that a higher sparsity or additional dead units do not imply a better or worse performance when the rank of representation is fixed. We also develop a rank regularizer and show that neither representation sparsity nor lower rank is helpful for improving performance even when the data-generation process has only a small number of independent factors. Mutual information $I(\z_l;\x)$ and $I(\z_l;\y)$ are investigated as well, and we show that regularizers can affect $I(\z_l;\x)$ and thus indirectly influence the performance. Finally, we explain how a rich set of regularizers can be used as a powerful tool for performance tuning.
Integer Networks for Data Compression with Latent-Variable Models
Authors: Johannes Ballé, Nick Johnston, David Minnen
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Data Compression, Math

We consider the problem of using variational latent-variable models for data compression. For such models to produce a compressed binary sequence, which is the universal data representation in a digital world, the latent representation needs to be subjected to entropy coding. Range coding as an entropy coding technique is optimal, but it can fail catastrophically if the computation of the prior differs even slightly between the sending and the receiving side. Unfortunately, this is a common scenario when floating point math is used and the sender and receiver operate on different hardware or software platforms, as numerical round-off is often platform dependent. We propose using integer networks as a universal solution to this problem, and demonstrate that they enable reliable cross-platform encoding and decoding of images using variational models.
Trajectory VAE for multi-modal imitation
Authors: Xiaoyu Lu, Jan Stuehmer, Katja Hofmann
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: continuous-control, Continuous Control, Decision Making, Imitation Learning, Minecraft, Sequential Decision Making

We address the problem of imitating multi-modal expert demonstrations in sequential decision making problems. In many practical applications, for example video games, behavioural demonstrations are readily available that contain multi-modal structure not captured by typical existing imitation learning approaches. For example, differences in the observed players' behaviours may be representative of different underlying playstyles.

 In this paper, we use a generative model to capture different emergent playstyles in an unsupervised manner, enabling the imitation of a diverse range of distinct behaviours. We utilise a variational autoencoder to learn an embedding of the different types of expert demonstrations on the trajectory level, and jointly learn a latent representation with a policy. In experiments on a range of 2D continuous control problems representative of Minecraft environments, we empirically demonstrate that our model can capture a multi-modal structured latent space from the demonstrated behavioural trajectories.
SIMILE: Introducing Sequential Information towards More Effective Imitation Learning
Authors: Yutong Bai, Lingxi Xie
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Imitation Learning, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Weakly-supervised Learning

Reinforcement learning (RL) is a metaheuristic aiming at teaching an agent to interact with an environment and maximizing the reward in a complex task. RL algorithms often encounter the difficulty in defining a reward function in a sparse solution space. Imitation learning (IL) deals with this issue by providing a few expert demonstrations, and then either mimicking the expert's behavior (behavioral cloning, BC) or recovering the reward function by assuming the optimality of the expert (inverse reinforcement learning, IRL). Conventional IL approaches formulate the agent policy by mapping one single state to a distribution over actions, which did not consider sequential information. This strategy can be less accurate especially in IL, a weakly supervised learning environment, especially when the number of expert demonstrations is limited.

This paper presents an effective approach named Sequential IMItation LEarning (SIMILE). The core idea is to introduce sequential information, so that an agent can refer to both the current state and past state-action pairs to make a decision. We formulate our approach into a recurrent model, and instantiate it using LSTM so as to fuse both long-term and short-term information. SIMILE is a generalized IL framework which is easily applied to BL and IRL, two major types of IL algorithms. Experiments are performed on several robot controlling tasks in OpenAI Gym. SIMILE not only achieves performance gain over the baseline approaches, but also enjoys the benefit of faster convergence and better stability of testing performance. These advantages verify a higher learning efficiency of SIMILE, and implies its potential applications in real-world scenarios, i.e., when the agent-environment interaction is more difficult and/or expensive.
A RECURRENT NEURAL CASCADE-BASED MODEL FOR CONTINUOUS-TIME DIFFUSION PROCESS
Authors: Sylvain Lamprier
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

Many works have been proposed in the literature to capture the dynamics of diffusion in networks. While some of them define graphical markovian models to extract temporal relationships between node infections in networks, others consider diffusion episodes as sequences of infections via recurrent neural models. In this paper we propose a model at the crossroads of these two extremes, which embeds the history of diffusion in infected nodes as hidden continuous states. Depending on the trajectory followed by the content before reaching a given node, the distribution of influence probabilities may vary. However, content trajectories  are usually hidden in the data, which induces challenging learning problems. We propose a topological recurrent neural model which exhibits good experimental performances for diffusion modelling and prediction.
Multi-task Learning with Gradient Communication
Authors: Pengfei Liu, Xuanjing Huang
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Inductive Bias, Multi-Task Learning

  In this paper, we describe a general framework to systematically analyze current neural models for multi-task learning, in which we find that existing models expect to disentangle features into different spaces while features learned in practice are still entangled in shared space,  leaving potential hazards for other training or unseen tasks. We propose to alleviate this problem by incorporating a new inductive bias into the process of multi-task learning, that different tasks can communicate with each other not only by passing hidden variables but gradients explicitly. Experimentally, we evaluate proposed methods on three groups of tasks and two types of settings (\textsc{in-task} and \textsc{out-of-task}). Quantitative and qualitative results show their effectiveness.
Improving Composition of Sentence Embeddings through the Lens of Statistical Relational Learning
Authors: Damien Sileo, Tim Van De Cruys, Camille Pradel, Philippe Muller
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Natural Language Inference, Prediction, Relational Reasoning, Relation Prediction, Representation Learning, Sentence, Sentence Embeddings, Sentence Similarity

Various NLP problems -- such as the prediction of sentence similarity, entailment, and discourse relations -- are all instances of the same general task: the modeling of semantic relations between a pair of textual elements. We call them textual relational problems. A popular model for textual relational problems is to embed sentences into fixed size vectors and use composition functions (e.g. difference or concatenation) of those vectors as features for the prediction. Meanwhile, composition of embeddings has been a main focus within the field of Statistical Relational Learning (SRL) whose goal is to predict relations between entities (typically from knowledge base triples). In this work, we show that textual relational models implicitly use compositions from baseline SRL models. We show that such compositions are not expressive enough for several tasks (e.g. natural language inference).  We build on recent SRL models to address textual relational problems, showing that they are more expressive, and can alleviate issues from simpler compositions. The resulting models significantly improve the state of the art in both transferable sentence representation learning and relation prediction.
Inferring Reward Functions from Demonstrators with Unknown Biases
Authors: Rohin Shah, Noah Gundotra, Pieter Abbeel, Anca Dragan
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Reinforcement Learning

Our goal is to infer reward functions from demonstrations. In order to infer the correct reward function, we must account for the systematic ways in which the demonstrator is suboptimal. Prior work in inverse reinforcement learning can account for specific, known biases, but cannot handle demonstrators with unknown biases. In this work, we explore the idea of learning the demonstrator's planning algorithm (including their unknown biases), along with their reward function. What makes this challenging is that any demonstration could be explained either by positing a term in the reward function, or by positing a particular systematic bias. We explore what assumptions are sufficient for avoiding this impossibility result: either access to tasks with known rewards which enable estimating the planner separately, or that the demonstrator is sufficiently close to optimal that this can serve as a regularizer. In our exploration with synthetic models of human biases, we find that it is possible to adapt to different biases and perform better than assuming a fixed model of the demonstrator, such as Boltzmann rationality.
Likelihood-based Permutation Invariant Loss Function for Probability Distributions
Authors: Masataro Asai
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Object Reconstruction

We propose a permutation-invariant loss function designed for the neural networks reconstructing a set of elements without considering the order within its vector representation. Unlike popular approaches for encoding and decoding a set, our work does not rely on a carefully engineered network topology nor by any additional sequential algorithm. The proposed method, Set Cross Entropy, has a natural information-theoretic interpretation and is related to the metrics defined for sets. We evaluate the proposed approach in two object reconstruction tasks and a rule learning task.
Learning to Progressively Plan
Authors: Xinyun Chen, Yuandong Tian
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

For problem solving, making reactive decisions based on problem description is fast but inaccurate, while search-based planning using heuristics gives better solutions but could be exponentially slow. In this paper, we propose a new approach that improves an existing solution by iteratively picking and rewriting its local components until convergence. The rewriting policy employs a neural network trained with reinforcement learning. We evaluate our approach in two domains: job scheduling and expression simplification. Compared to common effective heuristics, baseline deep models and search algorithms, our approach efficiently gives solutions with higher quality.
CNNSAT: Fast, Accurate Boolean Satisfiability using Convolutional Neural Networks
Authors: Yu Wang, Fengjuan Gao, Amin Alipour, Linzhang Wang, Xuandong Li, Zhendong Su
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

Boolean satisfiability (SAT) is one of the most well-known NP-complete
problems and has been extensively studied.  State-of-the-art solvers
exist and have found a wide range of applications. However, they still
do not scale well to formulas with hundreds of variables. To tackle
this fundamental scalability challenge, we introduce CNNSAT, a fast
and accurate statistical decision procedure for SAT based on
convolutional neural networks. CNNSAT's effectiveness is due to a
precise and compact representation of Boolean
formulas. On both real and synthetic formulas, CNNSAT is highly
  accurate and orders of magnitude faster than the
state-of-the-art solver Z3.  We also describe how to extend CNNSAT to
predict satisfying assignments when it predicts a formula to be
satisfiable.
Transferring SLU Models in Novel Domains
Authors: Yaohua Tang, Kaixiang Mo, Qian Xu, Chao Zhang, Qiang Yang
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Intent Recognition, Meta-Learning, slot-filling, Slot Filling, Spoken Language Understanding, Transfer Learning

Spoken language understanding (SLU) is a critical component in building dialogue systems. When building models for novel natural language domains, a major challenge is the lack of data in the new domains, no matter whether the data is annotated or not. Recognizing and annotating ``intent'' and ``slot'' of natural languages is a time-consuming process. Therefore, spoken language understanding in low resource domains remains a crucial problem to address. In this paper, we address this problem by proposing a transfer-learning method, whereby a SLU model is transferred to a novel but data-poor domain via a deep neural network framework. We also introduce meta-learning in our work to bridge the semantic relations between seen and unseen data, allowing new intents to be recognized and new slots to be filled with much lower new training effort. We show the performance improvement with extensive experimental results for spoken language understanding in low resource domains. We show that our method can also handle novel intent recognition and slot-filling tasks. Our methodology provides a feasible solution for alleviating data shortages in spoken language understanding.