pwc-archive/papers-with-abstracts huggingface dataset:
LSTOD: Latent Spatial-Temporal Origin-Destination prediction model and its applications in ride-sharing platforms
Authors: Fan Zhou, Haibo Zhou, Hongtu Zhu
Date: 2019-09-25 00:00:00
Tasks: Prediction

Origin-Destination (OD) flow data is an important instrument in transportation studies. Precise prediction of customer demands from each original location to a destination given a series of previous snapshots helps  ride-sharing platforms to better understand their market mechanism. However, most existing prediction methods ignore the network structure of OD flow data and fail to utilize the topological dependencies among related OD pairs. In this paper, we propose a latent spatial-temporal origin-destination (LSTOD) model, with a novel convolutional neural network (CNN) filter to learn the spatial features of OD pairs from a graph perspective and an attention structure to capture their long-term periodicity. Experiments on a real customer request dataset with available OD information from a ride-sharing platform demonstrate the advantage of LSTOD in achieving at least 6.5% improvement in prediction accuracy over the second best model.
Unsupervised Universal Self-Attention Network for Graph Classification
Authors: Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung
Date: 2019-09-25 00:00:00
Tasks: Classification, Graph Classification, Graph Embedding

Existing graph embedding models often have weaknesses in exploiting graph structure similarities, potential dependencies among nodes and global network properties. To this end, we present U2GAN, a novel unsupervised model leveraging on the strength of the recently introduced universal self-attention network (Dehghani et al., 2019), to learn low-dimensional embeddings of graphs which can be used for graph classification. In particular, given an input graph, U2GAN first applies a self-attention computation, which is then followed by a recurrent transition to iteratively memorize its attention on vector representations of each node and its neighbors across each iteration. Thus, U2GAN can address the weaknesses in the existing models in order to produce plausible node embeddings whose sum is the final embedding of the whole graph. Experimental results show that our unsupervised U2GAN produces new state-of-the-art performances on a range of well-known benchmark datasets for the graph classification task. It even outperforms supervised methods in most of benchmark cases.
CURSOR-BASED ADAPTIVE QUANTIZATION FOR DEEP NEURAL NETWORK
Authors: Bapu Li(*), Yanwen Fan(*), Zhiyu Cheng, Yingze Bao (* means equal contribution)
Date: 2019-09-25 00:00:00
Tasks: Quantization

Deep neural network (DNN) has rapidly found many applications in different scenarios.
However, its large computational cost and memory consumption are barriers
to computing restrained applications. DNN model quantization is a widely
used method to reduce the DNN storage and computation burden by decreasing
the bit width. In this paper, we propose a novel cursor based adaptive quantization
method using differentiable architecture search (DAS). The multiple bits’
quantization mechanism is formulated as a DAS process with a continuous cursor
that represents the possible quantization bit. The cursor-based DAS adaptively
searches for the desired quantization bit for each layer. The DAS process can
be solved via an alternative approximate optimization process, which is designed
for mixed quantization scheme of a DNN model. We further devise a new loss
function in the search process to simultaneously optimize accuracy and parameter
size of the model. In the quantization step, based on a new strategy, the closest
two integers to the cursor are adopted as the bits to quantize the DNN together to
reduce the quantization noise and avoid the local convergence problem. Comprehensive
experiments on benchmark datasets show that our cursor based adaptive
quantization approach achieves the new state-of-the-art for multiple bits’ quantization
and can efficiently obtain lower size model with comparable or even better
classification accuracy.
Hyperparameter Tuning and Implicit Regularization in Minibatch SGD
Authors: Samuel L Smith, Erich Elsen, Soham De
Date: 2019-09-25 00:00:00

This paper makes two contributions towards understanding how the hyperparameters of stochastic gradient descent affect the final training loss and test accuracy of neural networks. First, we argue that stochastic gradient descent exhibits two regimes with different behaviours; a noise dominated regime which typically arises for small or moderate batch sizes, and a curvature dominated regime which typically arises when the batch size is large. In the noise dominated regime, the optimal learning rate increases as the batch size rises, and the training loss and test accuracy are independent of batch size under a constant epoch budget. In the curvature dominated regime, the optimal learning rate is independent of batch size, and the training loss and test accuracy degrade as the batch size rises. We support these claims with experiments on a range of architectures including ResNets, LSTMs and autoencoders. We always perform a grid search over learning rates at all batch sizes. Second, we demonstrate that small or moderately large batch sizes continue to outperform very large batches on the test set, even when both models are trained for the same number of steps and reach similar training losses. Furthermore, when training Wide-ResNets on CIFAR-10 with a constant batch size of 64, the optimal learning rate to maximize the test accuracy only decays by a factor of 2 when the epoch budget is increased by a factor of 128, while the optimal learning rate to minimize the training loss decays by a factor of 16. These results confirm that the noise in stochastic gradients can introduce beneficial implicit regularization.
Variational Hashing-based Collaborative Filtering with Self-Masking
Authors: Casper Hansen, Christian Hansen, Jakob Grue Simonsen, Stephen Alstrup, Christina Lioma
Date: 2019-09-25 00:00:00
Tasks: Collaborative Filtering

Hashing-based collaborative filtering learns binary vector representations (hash codes) of users and items, such that recommendations can be computed very efficiently using the Hamming distance, which is simply the sum of differing bits between two hash codes. A problem with hashing-based collaborative filtering using the Hamming distance, is that each bit is equally weighted in the distance computation, but in practice some bits might encode more important properties than other bits, where the importance depends on the user. 
To this end, we propose an end-to-end trainable variational hashing-based collaborative filtering approach that uses the novel concept of self-masking: the user hash code acts as a mask on the items (using the Boolean AND operation), such that it learns to encode which bits are important to the user, rather than the user's preference towards the underlying item property that the bits represent. This allows a binary user-level importance weighting of each item without the need to store additional weights for each user. We experimentally evaluate our approach against state-of-the-art baselines on 4 datasets, and obtain significant gains of up to 12% in NDCG. We also make available an efficient implementation of self-masking, which experimentally yields <4% runtime overhead compared to the standard Hamming distance.
A Data-Efficient Mutual Information Neural Estimator for Statistical Dependency Testing
Authors: Xiao Lin, Indranil Sur, Samuel A. Nastase, Uri Hasson, Ajay Divakaran, Mohamed R. Amer
Date: 2019-09-25 00:00:00
Tasks: Meta-Learning

Measuring Mutual Information (MI) between high-dimensional, continuous, random variables from observed samples has wide theoretical and practical applications. Recent works have developed accurate MI estimators through provably low-bias approximations and tight variational lower bounds assuming abundant supply of samples, but require an unrealistic number of samples to guarantee statistical significance of the estimation. In this work, we focus on improving data efficiency and propose a Data-Efficient MINE Estimator (DEMINE) that can provide a tight lower confident interval of MI under limited data, through adding cross-validation to the MINE lower bound (Belghazi et al., 2018). Hyperparameter search is employed and a novel meta-learning approach with task augmentation is developed to increase robustness to hyperparamters, reduce overfitting and improve accuracy. With improved data-efficiency, our DEMINE estimator enables statistical testing of dependency at practical dataset sizes. We demonstrate the effectiveness of DEMINE on synthetic benchmarks and a real world fMRI dataset, with application of inter-subject correlation analysis.
SMiRL: Surprise Minimizing RL in Entropic Environments
Authors: Glen Berseth, Daniel Geng, Coline Devin, Dinesh Jayaraman, Chelsea Finn, Sergey Levine
Date: 2019-09-25 00:00:00
Tasks: Unsupervised Pre-training, Unsupervised Reinforcement Learning

All living organisms struggle against the forces of nature to carve out niches where
they can maintain relative stasis. We propose that such a search for order amidst
chaos might offer a unifying principle for the emergence of useful behaviors in
artificial agents. We formalize this idea into an unsupervised reinforcement learning
method called surprise minimizing RL (SMiRL). SMiRL trains an agent with the
objective of maximizing the probability of observed states under a model trained on
all previously seen states. The resulting agents acquire several proactive behaviors
to seek and maintain stable states such as balancing and damage avoidance, that
are closely tied to the affordances of the environment and its prevailing sources
of entropy, such as winds, earthquakes, and other agents.  We demonstrate that
our surprise minimizing agents can successfully play Tetris, Doom, and control
a  humanoid  to  avoid  falls,  without  any  task-specific  reward  supervision.   We
further show that SMiRL can be used as an unsupervised pre-training objective
that substantially accelerates subsequent reward-driven learning
Semi-supervised Learning by Coaching
Authors: Hieu Pham, Quoc V. Le
Date: 2019-09-25 00:00:00

Recent semi-supervised learning (SSL) methods often have a teacher to train a student in order to propagate labels from labeled data to unlabeled data. We argue that a weakness of these methods is that the teacher does not learn from the student’s mistakes during the course of student’s learning.  To address this weakness, we introduce Coaching, a framework where a teacher generates pseudo labels for unlabeled data, from which a student will learn and the student’s performance on labeled data will be used as reward to train the teacher using policy gradient.

Our experiments show that Coaching significantly improves over state-of-the-art SSL baselines. For instance, on CIFAR-10, with only 4,000 labeled examples, a WideResNet-28-2 trained by Coaching achieves 96.11% accuracy, which is better than 94.9% achieved by the same architecture trained with 45,000 labeled. On ImageNet with 10% labeled examples, Coaching trains a ResNet-50 to 72.94% top-1 accuracy, comfortably outperforming the existing state-of-the-art by more than 4%. Coaching also scales successfully to the high data regime with full ImageNet. Specifically, with additional 9 million unlabeled images from OpenImages, Coaching trains a ResNet-50 to 82.34% top-1 accuracy, setting a new state-of-the-art for the architecture on ImageNet without using extra labeled data.
Stochastic Neural Physics Predictor
Authors: Piotr Tatarczyk, Damian Mrowca, Li Fei-Fei, Daniel L. K. Yamins, Nils Thuerey
Date: 2019-09-25 00:00:00

Recently,  neural-network  based  forward  dynamics  models  have  been  proposed that  attempt  to  learn  the  dynamics  of  physical  systems  in  a  deterministic  way. While near-term motion can be predicted accurately, long-term predictions suffer from accumulating input and prediction errors which can lead to plausible but different trajectories that diverge from the ground truth.  A system that predicts distributions of the future physical states for long time horizons based on its uncertainty is thus a promising solution.  In this work, we introduce a novel robust Monte Carlo sampling based graph-convolutional dropout method that allows us to sample multiple plausible trajectories for an initial state given a neural-network based forward dynamics predictor.  By introducing a new shape preservation loss and training our dynamics model recurrently, we stabilize long-term predictions. We show that our model’s long-term forward dynamics prediction errors on complicated physical interactions of rigid and deformable objects of various shapes are significantly lower than existing strong baselines. Lastly, we demonstrate how generating multiple trajectories with our Monte Carlo dropout method can be used to train model-free reinforcement learning agents faster and to better solutions on simple manipulation tasks.
Spectral Nonlocal Block for Neural Network
Authors: Lei Zhu, Qi She, Lidan Zhang, Ping Guo
Date: 2019-09-25 00:00:00
Tasks: Video Classification

The nonlocal network is designed for capturing long-range spatial-temporal dependencies in several computer vision tasks. Although having shown excellent performances, it needs an elaborate preparation for both the number and position of the building blocks. In this paper, we propose a new formulation of the nonlocal block and interpret it from the general graph signal processing perspective, where we view it as a fully-connected graph filter approximated by Chebyshev polynomials. The proposed nonlocal block is more efficient and robust, which is a generalized form of existing nonlocal blocks (e.g. nonlocal block, nonlocal stage). Moreover, we give the stable hypothesis and show that the steady-state of the deeper nonlocal structure should meet with it. Based on the stable hypothesis,  a full-order approximation of the nonlocal block is derived for consecutive connections. Experimental results illustrate the clear-cut improvement and practical applicability of the generalized nonlocal block on both image and video classification tasks.
Diving into Optimization of Topology in Neural Networks
Authors: Kun Yuan, Quanquan Li, Yucong Zhou, Jing Shao, Junjie Yan
Date: 2019-09-25 00:00:00
Tasks: Face Recognition, image-classification, Image Classification, object-detection, Object Detection

Seeking effective networks has become one of the most crucial and practical areas in deep learning. The architecture of a neural network can be represented as a directed acyclic graph, whose nodes denote transformation of layers and edges represent information flow. Despite the selection of \textit{micro} node operations, \textit{macro} connections among the whole network, noted as \textit{topology}, largely affects the optimization process. We first rethink the residual connections via a new \textit{topological view} and observe the benefits provided by dense connections to the optimization. Motivated by which, we propose an innovation method to optimize the topology of a neural network. The optimization space is defined as a complete graph, through assigning learnable weights which reflect the importance of connections, the optimization of topology is transformed into learning a set of  continuous variables of edges. To extend the optimization to larger search spaces, a new series of networks,
named as TopoNet, are designed. To further focus on critical edges and promote generalization ablity in dense topologies, auxiliary sparsity constraint is adopted to constrain the distribution of edges. Experiments on classical networks prove the effectiveness of the optimization of topology. Experiments with TopoNets further verify both availability and transferability of the proposed method in
different tasks e.g. image classification, object detection and face recognition.
Generative Restricted Kernel Machines
Authors: Arun Pandey, Joachim Schreurs, Johan A.K. Suykens
Date: 2019-09-25 00:00:00

We introduce a novel framework for generative models based on Restricted Kernel Machines (RKMs) with multi-view generation and uncorrelated feature learning capabilities, called Gen-RKM. To incorporate multi-view generation, this mechanism uses a shared representation of data from various views. The mechanism is flexible to incorporate both kernel-based, (deep) neural network and convolutional based models within the same setting. To update the parameters of the network, we propose a novel training procedure which jointly learns the features and shared representation. Experiments demonstrate the potential of the framework through qualitative evaluation of generated samples.
Distillation $\approx$ Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized NN
Authors: Bin Dong, Jikai Hou, Yiping Lu, Zhihua Zhang
Date: 2019-09-25 00:00:00
Tasks: Information Retrieval, Retrieval

Distillation is a method to transfer knowledge from one model to another and often achieves higher accuracy with the same capacity. In this paper, we aim to provide a theoretical understanding on what mainly helps with the distillation. Our answer is "early stopping". Assuming that the teacher network is overparameterized, we argue that the teacher network is essentially harvesting dark knowledge from the data via early stopping. This can be justified by a new concept, Anisotropic In- formation Retrieval (AIR), which means that the neural network tends to fit the informative information first and the non-informative information (including noise) later. Motivated by the recent development on theoretically analyzing overparame- terized neural networks, we can characterize AIR by the eigenspace of the Neural Tangent Kernel(NTK). AIR facilities a new understanding of distillation. With that, we further utilize distillation to refine noisy labels. We propose a self-distillation al- gorithm to sequentially distill knowledge from the network in the previous training epoch to avoid memorizing the wrong labels. We also demonstrate, both theoret- ically and empirically, that self-distillation can benefit from more than just early stopping. Theoretically, we prove convergence of the proposed algorithm to the ground truth labels for randomly initialized overparameterized neural networks in terms of l2 distance, while the previous result was on convergence in 0-1 loss. The theoretical result ensures the learned neural network enjoy a margin on the training data which leads to better generalization. Empirically, we achieve better testing accuracy and entirely avoid early stopping which makes the algorithm more user-friendly.
Support-guided Adversarial Imitation Learning
Authors: Ruohan Wang, Carlo Ciliberto, Pierluigi Amadori, Yiannis Demiris
Date: 2019-09-25 00:00:00
Tasks: Imitation Learning

We propose Support-guided Adversarial Imitation Learning (SAIL), a generic imitation learning framework that unifies support estimation of the expert policy with the family of Adversarial Imitation Learning (AIL) algorithms. SAIL addresses two important challenges of AIL, including the implicit reward bias and potential training instability. We also show that SAIL is at least as efficient as standard AIL. In an extensive evaluation, we demonstrate that the proposed method effectively handles the reward bias and achieves better performance and training stability than other baseline methods on a wide range of benchmark control tasks.
IS THE LABEL TRUSTFUL: TRAINING BETTER DEEP LEARNING MODEL VIA UNCERTAINTY MINING NET
Authors: Yang Sun, Abhishek Kolagunda, Steven Eliuk, Xiaolong Wang
Date: 2019-09-25 00:00:00

In this work, we consider a new problem of training deep neural network on partially labeled data with label noise.  As far as we know, 
there have been very few efforts to tackle such problems.
We present a novel end-to-end deep generative pipeline for improving classifier performance when dealing with such data problems.  We call it 
Uncertainty Mining Net (UMN).  
 During the training stage, we utilize all the available data (labeled and unlabeled) to train the classifier via a semi-supervised generative framework. 
 During training, UMN estimates the uncertainly of the labels’ to focus on clean data for  learning. More precisely, UMN applies the sample-wise label uncertainty estimation scheme. 
 Extensive experiments and comparisons against state-of-the-art methods on several popular benchmark datasets demonstrate that UMN can reduce the effects of label noise and significantly improve classifier performance.
Learning with Social Influence through Interior Policy Differentiation
Authors: Hao Sun, Bo Dai, Jiankai Sun, Zhenghao Peng, Guodong Xu, Dahua Lin, Bolei Zhou
Date: 2019-09-25 00:00:00
Tasks: Reinforcement Learning (RL)

Animals develop novel skills not only through the interaction with the environment but also from the influence of the others. In this work we model the social influence into the scheme of reinforcement learning, enabling the agents to learn both from the environment and from their peers. Specifically, we first define a metric to measure the distance between policies then quantitatively derive the definition of uniqueness. Unlike previous precarious joint optimization approaches, the social uniqueness motivation in our work is imposed as a constraint to encourage the agent to learn a policy different from the existing agents while still solve the primal task. The resulting algorithm, namely Interior Policy Differentiation (IPD), brings about performance improvement as well as a collection of policies that solve a given task with distinct behaviors
Selfish Emergent Communication
Authors: Michael Noukhovitch, Travis LaCroix, Aaron Courville
Date: 2019-09-25 00:00:00

Current literature in machine learning holds that unaligned, self-interested agents do not learn to use an emergent communication channel. We introduce a new sender-receiver game to study emergent communication for this spectrum of partially-competitive scenarios and put special care into evaluation. We find that communication can indeed emerge in partially-competitive scenarios, and we discover three things that are tied to improving it. First, that selfish communication is proportional to cooperation, and it naturally occurs for situations that are more cooperative than competitive. Second, that stability and performance are improved by using LOLA (Foerster et al, 2018), especially in more competitive scenarios. And third, that discrete protocols lend themselves better to learning cooperative communication than continuous ones.
DS-VIC: Unsupervised Discovery of Decision States for Transfer in RL
Authors: Nirbhay Modhe, Prithvijit Chattopadhyay, Mohit Sharma, Abhishek Das, Devi Parikh, Dhruv Batra, Ramakrishna Vedantam
Date: 2019-09-25 00:00:00

We learn to identify decision states, namely the parsimonious set of states where decisions meaningfully affect the future states an agent can reach in an environment. We utilize the VIC framework, which maximizes an agent’s `empowerment’, ie the ability to reliably reach a diverse set of states -- and formulate a sandwich bound on the empowerment objective that allows identification of decision states. Unlike previous work, our decision states are discovered without extrinsic rewards -- simply by interacting with the world. Our results show that our decision states are: 1) often interpretable, and 2) lead to better exploration on downstream goal-driven tasks in partially observable environments.