pwc-archive/papers-with-abstracts huggingface dataset:
Learning Procedural Abstractions and Evaluating Discrete Latent Temporal Structure
Authors: Karan Goel, Emma Brunskill
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Clustering, Time Series, Time Series Analysis

Clustering methods and latent variable models are often used as tools for pattern mining and discovery of latent structure in time-series data. In this work, we consider the problem of learning procedural abstractions from possibly high-dimensional observational sequences, such as video demonstrations. Given a dataset of time-series, the goal is to identify the latent sequence of steps common to them and label each time-series with the temporal extent of these procedural steps. We introduce a hierarchical Bayesian model called Prism that models the realization of a common procedure across multiple time-series, and can recover procedural abstractions with supervision. We also bring to light two characteristics ignored by traditional evaluation criteria when evaluating latent temporal labelings (temporal clusterings) -- segment structure, and repeated structure -- and develop new metrics tailored to their evaluation. We demonstrate that our metrics improve interpretability and ease of analysis for evaluation on benchmark time-series datasets. Results on benchmark and video datasets indicate that Prism outperforms standard sequence models as well as state-of-the-art techniques in identifying procedural abstractions.
Critical Learning Periods in Deep Networks
Authors: Alessandro Achille, Matteo Rovere, Stefano Soatto
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Disentanglement, Representation Learning

Similar to humans and animals, deep artificial neural networks exhibit critical periods during which a temporary stimulus deficit can impair the development of a skill. The extent of the impairment depends on the onset and length of the deficit window, as in animal models, and on the size of the neural network. Deficits that do not affect low-level statistics, such as vertical flipping of the images, have no lasting effect on performance and can be overcome with further training.  To better understand this phenomenon, we use the Fisher Information of the weights to measure the effective connectivity between layers of a network during training.  Counterintuitively, information rises rapidly in the early phases of training, and then decreases, preventing redistribution of information resources in a phenomenon we refer to as a loss of "Information Plasticity".  Our analysis suggests that the first few epochs are critical for the creation of strong connections that are optimal relative to the input data distribution. Once such strong connections are created, they do not appear to change during additional training. These findings suggest that the initial learning transient, under-scrutinized compared to asymptotic behavior, plays a key role in determining the outcome of the training process. Our findings, combined with recent theoretical results in the literature, also suggest that forgetting (decrease of information in the weights) is critical to achieving invariance and disentanglement in representation learning. Finally, critical periods are not restricted to biological systems, but can emerge naturally in learning systems, whether biological or artificial, due to fundamental constrains arising from learning dynamics and information processing.
Kernel RNN Learning (KeRNL)
Authors: Christopher Roth, Ingmar Kanitscheider, Ila Fiete
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Sensitivity

We describe Kernel RNN Learning (KeRNL), a reduced-rank, temporal eligibility trace-based approximation to backpropagation through time (BPTT) for training recurrent neural networks (RNNs) that gives competitive performance to BPTT on long time-dependence tasks. The approximation replaces a rank-4 gradient learning tensor, which describes how past hidden unit activations affect the current state, by a simple reduced-rank product of a sensitivity weight and a temporal eligibility trace. In this structured approximation motivated by node perturbation, the sensitivity weights and eligibility kernel time scales are themselves learned by applying perturbations. The rule represents another step toward biologically plausible or neurally inspired ML, with lower complexity in terms of relaxed architectural requirements (no symmetric return weights), a smaller memory demand (no unfolding and storage of states over time), and a shorter feedback time.
Modeling the Long Term Future in Model-Based Reinforcement Learning
Authors: Nan Rosemary Ke, Amanpreet Singh, Ahmed Touati, Anirudh Goyal, Yoshua Bengio, Devi Parikh, Dhruv Batra
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Imitation Learning, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), valid, Variational Inference

In model-based reinforcement learning, the agent interleaves between model learning and planning.  These two components are  inextricably intertwined. If the model is not able to provide sensible long-term prediction, the executed planer would exploit model flaws, which can yield catastrophic failures. This paper focuses on building a model that reasons about the long-term future and demonstrates how to use this for efficient planning and exploration. To this end, we build a latent-variable autoregressive model by leveraging recent ideas in variational inference. We argue that forcing latent variables to carry future information through an auxiliary task substantially improves long-term predictions. Moreover, by planning in the latent space, the planner's solution is ensured to be within regions where the model is valid. An exploration strategy can be devised by searching for unlikely trajectories under the model. Our methods achieves higher reward faster compared to baselines on a variety of tasks and environments in both the imitation learning and model-based reinforcement learning settings.
ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees
Authors: Hao He, Hao Wang, Guang-He Lee, Yonglong Tian
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Image Generation

Probabilistic modelling is a principled framework to perform model aggregation, which has been a primary mechanism to combat mode collapse in the context of Generative Adversarial Networks (GAN). In this paper, we propose a novel probabilistic framework for GANs, ProbGAN, which iteratively learns a distribution over generators with a carefully crafted prior. Learning is efficiently triggered by a tailored stochastic gradient Hamiltonian Monte Carlo with a novel gradient approximation to perform Bayesian inference. Our theoretical analysis further reveals that our treatment is the first probabilistic framework that yields an equilibrium where generator distributions are faithful to the data distribution. Empirical evidence on synthetic high-dimensional multi-modal data and image databases (CIFAR-10, STL-10, and ImageNet) demonstrates the superiority of our method over both start-of-the-art multi-generator GANs and other probabilistic treatment for GANs.
Don't let your Discriminator be fooled
Authors: Brady Zhou, Philipp Krähenbühl
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Adversarial Robustness

Generative Adversarial Networks are one of the leading tools in generative modeling, image editing and content creation. 
However, they are hard to train as they require a delicate balancing act between two deep networks fighting a never ending duel. Some of the most promising adversarial models today minimize a Wasserstein objective. It is smoother and more stable to optimize. In this paper, we show that the Wasserstein distance is just one out of a large family of objective functions that yield these properties. By making the discriminator of a GAN robust to adversarial attacks we can turn any GAN objective into a smooth and stable loss. We experimentally show that any GAN objective, including Wasserstein GANs, benefit from adversarial robustness both quantitatively and qualitatively. The training additionally becomes more robust to suboptimal choices of hyperparameters, model architectures, or objective functions.
Application of simplest random walk algorithms for pricing barrier options
Authors: M. Krivko, M. V. Tretyakov
Date: 2012-11-25 00:00:00

We demonstrate effectiveness of the first-order algorithm from [Milstein,
Tretyakov. Theory Prob. Appl. 47 (2002), 53-68] in application to barrier
option pricing. The algorithm uses the weak Euler approximation far from
barriers and a special construction motivated by linear interpolation of the
price near barriers. It is easy to implement and is universal: it can be
applied to various structures of the contracts including derivatives on
multi-asset correlated underlyings and can deal with various type of barriers.
In contrast to the Brownian bridge techniques currently commonly used for
pricing barrier options, the algorithm tested here does not require knowledge
of trigger probabilities nor their estimates. We illustrate this algorithm via
pricing a barrier caplet, barrier trigger swap and barrier swaption.
Event Detection via Gated Multilingual Attention Mechanism
Authors: Jian Liu, Yubo Chen, Kang Liu, Jun Zhao
Date: 2018-09-01 00:00:00
Proceeding: AAAI-18 2018 9
Tasks: Event Detection

Identifying event instance in text plays a critical role in building NLP applications such as Information Extraction (IE) system. However, most existing methods for this task focus only on monolingual clues of a specific language and ignore the massive information provided by other languages. Data scarcity and monolingual ambiguity hinder the performance of these monolingual approaches. In this paper, we propose a novel multilingual approach — dubbed as Gated MultiLingual Attention (GMLATT) framework — to address the two issues simultaneously. In specific, to alleviate data scarcity problem, we exploit the consistent information in multilingual data via context attention mechanism. Which takes advantage of the consistent evidence in multilingual data other than learning only from monolingual data. To deal with monolingual ambiguity problem, we propose gated cross-lingual attention to exploit the complement information conveyed by multilingual data, which is helpful for the disambiguation. The cross-lingual attention gate serves as a sentinel modelling the confidence of the clues provided by other languages and controls the information integration of various languages. We have conducted extensive experiments on
the ACE 2005 benchmark. Experimental results show that our
approach significantly outperforms state-of-the-art methods.
Information Theoretic lower bounds on negative log likelihood
Authors: Luis A. Lastras-Montaño
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

In this article we use rate-distortion theory, a branch of information theory devoted to the problem of lossy compression, to shed light on an important problem in latent variable modeling of data: is there room to improve the model? One way to address this question is to find an upper bound on the probability (equivalently a lower bound on the negative log likelihood) that the model can assign to some data as one varies the prior and/or the likelihood function in a latent variable model. The core of our contribution is to formally show that the problem of optimizing priors in latent variable models is exactly an instance of the variational optimization problem that information theorists solve when computing rate-distortion functions, and then to use this to derive a lower bound on negative log likelihood. Moreover, we will show that if changing the prior can improve the log likelihood, then there is a way to change the likelihood function instead and attain the same log likelihood, and thus rate-distortion theory is of relevance to both optimizing priors as well as optimizing likelihood functions. We will experimentally argue for the usefulness of quantities derived from rate-distortion theory in latent variable modeling by applying them to a problem in image modeling.
A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery
Authors: Ali Mousavi, Gautam Dasarathy, Richard G. Baraniuk
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Dimensionality Reduction

In this paper, we focus on two challenges which offset the promise of sparse signal representation, sensing, and recovery. First, real-world signals can seldom be described as perfectly sparse vectors in a known basis, and traditionally used random measurement schemes are seldom optimal for sensing them. Second, existing signal recovery algorithms are usually not fast enough to make them applicable to real-time problems. In this paper, we address these two challenges by presenting a novel framework based on deep learning. For the first challenge, we cast the problem of finding informative measurements by using a maximum likelihood (ML) formulation and show how we can build a data-driven dimensionality reduction protocol for sensing signals using convolutional architectures. For the second challenge, we discuss and analyze a novel parallelization scheme and show it significantly speeds-up the signal recovery process. We demonstrate the significant improvement our method obtains over competing methods through a series of experiments.
Neural Network Language Modeling with Letter-based Features and Importance Sampling
Authors: Hainan Xu, Ke Li, Yiming Wang, Jian Wang, Shiyin Kang, Xie Chen, Daniel Povey, Sanjeev Khudanpur
Date: 2018-04-15 00:00:00
Proceeding: ICASSP 2018 4
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

In this paper we describe an extension of the Kaldi software toolkit to support neural-based language modeling, intended
for use in automatic speech recognition (ASR) and related tasks. We combine the use of subword features (letter n-grams) and one-hot encoding of frequent words so that the models can handle large vocabularies containing infrequent
words. We propose a new objective function that allows for training of unnormalized probabilities. An importance sampling based method is supported to speed up training when the vocabulary is large. Experimental results on five corpora show that Kaldi-RNNLM rivals other recurrent neural network language model toolkits both on performance and training speed.
Purely sequence-trained neural networks for ASR based on lattice-free MMI
Authors: Daniel Povey, Vijayaditya Peddinti, Daniel Galvez, Pegah Ghahrmani, Vimal Manohar, Xingyu Na, Yiming Wang, Sanjeev Khudanpur
Date: 2016-09-08 00:00:00
Proceeding: INTERSPEECH 2016 2016 9
Tasks: Language Modeling, Language Modelling, Speech Recognition

In this paper we describe a method to perform sequence-discriminative training of neural network acoustic models without the need for frame-level cross-entropy pre-training. We use the lattice-free version of the maximum mutual information
(MMI) criterion: LF-MMI. To make its computation feasible we use a phone n-gram language model, in place of the word language model. To further reduce its space and time complexity we compute the objective function using neural network outputs at one third the standard frame rate. These changes enable us to perform the computation for the forward-backward algorithm on GPUs. Further the reduced output frame-rate also provides a significant speed-up during decoding.
We present results on 5 different LVCSR tasks with training data ranging from 100 to 2100 hours. Models trained with LFMMI provide a relative word error rate reduction of ∼11.5%, over those trained with cross-entropy objective function, and ∼8%, over those trained with cross-entropy and sMBR objective functions. A further reduction of ∼2.5%, relative, can be obtained by fine tuning these models with the word-lattice based sMBR objective function.
Are Few-Shot Learning Benchmarks too Simple ? Solving them without Task Supervision at Test-Time
Authors: Gabriel Huang, Hugo Larochelle, Simon Lacoste-Julien
Date: 2019-02-22 00:00:00
Tasks: Clustering, Few-Shot Learning, General Classification, Meta-Learning

We show that several popular few-shot learning benchmarks can be solved with varying degrees of success without using support set Labels at Test-time (LT). To this end, we introduce a new baseline called Centroid Networks, a modification of Prototypical Networks in which the support set labels are hidden from the method at test-time and have to be recovered through clustering. A benchmark that can be solved perfectly without LT does not require proper task adaptation and is therefore inadequate for evaluating few-shot methods. In practice, most benchmarks cannot be solved perfectly without LT, but running our baseline on any new combinations of architectures and datasets gives insights on the baseline performance to be expected from leveraging a good representation, before any adaptation to the test-time labels.
Multi-Armed Bandit Strategies for Non-Stationary Reward Distributions and Delayed Feedback Processes
Authors: Larkin Liu, Richard Downe, Joshua Reid
Date: 2019-02-22 00:00:00
Tasks: Thompson Sampling

A survey is performed of various Multi-Armed Bandit (MAB) strategies in order to examine their performance in circumstances exhibiting non-stationary stochastic reward functions in conjunction with delayed feedback. We run several MAB simulations to simulate an online eCommerce platform for grocery pick up, optimizing for product availability. In this work, we evaluate several popular MAB strategies, such as $\epsilon$-greedy, UCB1, and Thompson Sampling. We compare the respective performances of each MAB strategy in the context of regret minimization. We run the analysis in the scenario where the reward function is non-stationary. Furthermore, the process experiences delayed feedback, where the reward function is not immediately responsive to the arm played. We devise a new adaptive technique (AG1) tailored for non-stationary reward functions in the delayed feedback scenario. The results of the simulation show show superior performance in the context of regret minimization compared to traditional MAB strategies.
Towards Neural Mixture Recommender for Long Range Dependent User Sequences
Authors: Jiaxi Tang, Francois Belletti, Sagar Jain, Minmin Chen, Alex Beutel, Can Xu, Ed H. Chi
Date: 2019-02-22 00:00:00
Tasks: Sequential Recommendation

Understanding temporal dynamics has proved to be highly valuable for accurate
recommendation. Sequential recommenders have been successful in modeling the
dynamics of users and items over time. However, while different model
architectures excel at capturing various temporal ranges or dynamics, distinct
application contexts require adapting to diverse behaviors. In this paper we
examine how to build a model that can make use of different temporal ranges and
dynamics depending on the request context. We begin with the analysis of an
anonymized Youtube dataset comprising millions of user sequences. We quantify
the degree of long-range dependence in these sequences and demonstrate that
both short-term and long-term dependent behavioral patterns co-exist. We then
propose a neural Multi-temporal-range Mixture Model (M3) as a tailored solution
to deal with both short-term and long-term dependencies. Our approach employs a
mixture of models, each with a different temporal range. These models are
combined by a learned gating mechanism capable of exerting different model
combinations given different contextual information. In empirical evaluations
on a public dataset and our own anonymized YouTube dataset, M3 consistently
outperforms state-of-the-art sequential recommendation methods.
Capacity allocation through neural network layers
Authors: Jonathan Donier
Date: 2019-02-22 00:00:00

Capacity analysis has been recently introduced as a way to analyze how linear
models distribute their modelling capacity across the input space. In this
paper, we extend the notion of capacity allocation to the case of neural
networks with non-linear layers. We show that under some hypotheses the problem
is equivalent to linear capacity allocation, within some extended input space
that factors in the non-linearities. We introduce the notion of layer
decoupling, which quantifies the degree to which a non-linear activation
decouples its outputs, and show that it plays a central role in capacity
allocation through layers. In the highly non-linear limit where decoupling is
total, we show that the propagation of capacity throughout the layers follows a
simple markovian rule, which turns into a diffusion PDE in the limit of deep
networks with residual layers. This allows us to recover some known results
about deep neural networks, such as the size of the effective receptive field,
or why ResNets avoid the shattering problem.
A Review, Framework and R toolkit for Exploring, Evaluating, and Comparing Visualizations
Authors: Stephen L. France, Ulas Akkucuk
Date: 2019-02-22 00:00:00
Tasks: Dimensionality Reduction

This paper gives a review and synthesis of methods of evaluating
dimensionality reduction techniques. Particular attention is paid to rank-order
neighborhood evaluation metrics. A framework is created for exploring
dimensionality reduction quality through visualization. An associated toolkit
is implemented in R. The toolkit includes scatter plots, heat maps, loess
smoothing, and performance lift diagrams. The overall rationale is to help
researchers compare dimensionality reduction techniques and use visual insights
to help select and improve techniques. Examples are given for dimensionality
reduction of manifolds and for the dimensionality reduction applied to a
consumer survey dataset.
ParticleNet: Jet Tagging via Particle Clouds
Authors: Huilin Qu, Loukas Gouskos
Date: 2019-02-22 00:00:00
Tasks: BIG-bench Machine Learning, Jet Tagging

How to represent a jet is at the core of machine learning on jet physics. Inspired by the notion of point clouds, we propose a new approach that considers a jet as an unordered set of its constituent particles, effectively a "particle cloud". Such a particle cloud representation of jets is efficient in incorporating raw information of jets and also explicitly respects the permutation symmetry. Based on the particle cloud representation, we propose ParticleNet, a customized neural network architecture using Dynamic Graph Convolutional Neural Network for jet tagging problems. The ParticleNet architecture achieves state-of-the-art performance on two representative jet tagging benchmarks and is improved significantly over existing methods.
Improving Multilingual Sentence Embedding using Bi-directional Dual Encoder with Additive Margin Softmax
Authors: Yinfei Yang, Gustavo Hernandez Abrego, Steve Yuan, Mandy Guo, Qinlan Shen, Daniel Cer, Yun-Hsuan Sung, Brian Strope, Ray Kurzweil
Date: 2019-02-22 00:00:00
Tasks: NMT, Retrieval, Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings

In this paper, we present an approach to learn multilingual sentence embeddings using a bi-directional dual-encoder with additive margin softmax. The embeddings are able to achieve state-of-the-art results on the United Nations (UN) parallel corpus retrieval task. In all the languages tested, the system achieves P@1 of 86% or higher. We use pairs retrieved by our approach to train NMT models that achieve similar performance to models trained on gold pairs. We explore simple document-level embeddings constructed by averaging our sentence embeddings. On the UN document-level retrieval task, document embeddings achieve around 97% on P@1 for all experimented language pairs. Lastly, we evaluate the proposed model on the BUCC mining task. The learned embeddings with raw cosine similarity scores achieve competitive results compared to current state-of-the-art models, and with a second-stage scorer we achieve a new state-of-the-art level on this task.