pwc-archive/papers-with-abstracts huggingface dataset:
Sequence-level Intrinsic Exploration Model for Partially Observable Domains
Authors: Haiyan Yin, Jianda Chen, Sinno Jialin Pan
Date: 2019-09-25 00:00:00
Tasks: Prediction, reinforcement-learning, Reinforcement Learning (RL)

Training reinforcement learning policies in partially observable domains with sparse reward signal is an important and open problem for the research community. In this paper, we introduce a new sequence-level intrinsic novelty model to tackle the challenge of training reinforcement learning policies in sparse rewarded partially observable domains. First, we propose a new reasoning paradigm to infer the novelty for the partially observable states, which is built upon forward dynamics prediction. Different from conventional approaches that perform self-prediction or one-step forward prediction, our proposed approach engages open-loop multi-step prediction, which enables the difficulty of novelty prediction to flexibly scale and thus results in high-quality novelty scores. Second, we propose a novel dual-LSTM architecture to facilitate the sequence-level reasoning over the partially observable state space.  Our proposed architecture efficiently synthesizes information from an observation sequence and an action sequence to derive meaningful latent representations for inferring the novelty for states. To evaluate the efficiency of our proposed approach, we conduct extensive experiments on several challenging 3D navigation tasks from ViZDoom and DeepMind Lab. We also present results on two hard-exploration domains from Atari 2600 series in Appendix to demonstrate our proposed approach could generalize beyond partially observable navigation tasks. Overall, the experiment results reveal that our proposed intrinsic novelty model could outperform several state-of-the-art curiosity baselines with considerable significance in the testified domains.
EINS: Long Short-Term Memory with Extrapolated Input Network Simplification
Authors: Nicholas I-Hsien Kuo, Mehrtash T. Harandi, Nicolas Fourrier, Gabriela Ferraro, Christian Walder, Hanna Suominen
Date: 2019-09-25 00:00:00
Tasks: Image Generation, Imputation, Language Modelling, Sentence

This paper contrasts the two canonical recurrent neural networks (RNNs) of long short-term memory (LSTM) and gated recurrent unit (GRU) to propose our novel light-weight RNN of Extrapolated Input for Network Simplification (EINS). We treat LSTMs and GRUs as differential equations, and our analysis highlights several auxiliary components in the standard LSTM design that are secondary in importance. Guided by these insights, we present a design that abandons the LSTM redundancies, thereby introducing EINS. We test EINS against the LSTM over a carefully chosen range of tasks from language modelling and medical data imputation-prediction through a sentence-level variational autoencoder and image generation to learning to learn to optimise another neural network. Despite having both a simpler design and fewer parameters, this simplification either performs comparably, or better, than the LSTM in each task.
Connectivity-constrained interactive annotations for panoptic segmentation
Authors: Ruobing Shen, Bo Tang, Ismail Ben Ayed, Andrea Lodi, Thomas Guthier
Date: 2019-09-25 00:00:00
Tasks: Panoptic Segmentation, Segmentation, Weakly-supervised Learning

Large-scale ground truth data sets are of crucial importance for deep learning
based segmentation models, but annotating per-pixel
masks is prohibitively time consuming. In this paper, we investigate interactive graph-based segmentation algorithms that enforce connectivity. To be more precise, we introduce an instance-aware heuristic of a discrete Potts model, and a class-aware Integer Linear Programming (ILP) formulation that ensures global optimum. Both algorithms can take RGB, or utilize the feature maps from any DCNN, whether trained on the target dataset or not, as input. We present competitive semantic (and panoptic) segmentation results on the PASCAL VOC 2012 and Cityscapes dataset given initial scribbles. We also demonstrate that our interactive approach can reach $90.6\%$ mIoU on VOC validation set with an overhead of just $3$ correction scribbles. They are thus suitable for  interactive annotation on new or existing datasets, or can be used inside any weakly supervised learning framework on new datasets.
Frustratingly easy quasi-multitask learning
Authors: Gábor Berend, Norbert Kis-Szabó
Date: 2019-09-25 00:00:00

We propose the technique of quasi-multitask learning (Q-MTL), a simple and easy to implement modification of standard multitask learning, in which the  tasks to be modeled are identical. We illustrate it through a series of sequence labeling experiments over a diverse set of languages, that applying Q-MTL consistently increases the generalization ability of the applied models. The proposed architecture can be regarded as a new regularization technique encouraging the model to develop an internal representation of the problem at hand that is beneficial to multiple output units of the classifier at the same time. This property hampers the convergence to such internal representations which are highly specific and tailored for a classifier with a particular set of parameters. Our experiments corroborate that by relying on the proposed algorithm, we can approximate the quality of an ensemble of classifiers at a fraction of computational resources required. Additionally, our results suggest that Q-MTL handles the presence of noisy training labels better than ensembles.
Towards Effective 2-bit Quantization: Pareto-optimal Bit Allocation for Deep CNNs Compression
Authors: Zhe Wang, Jie Lin, Mohamed M. Sabry Aly, Sean I Young, Vijay Chandrasekhar, Bernd Girod
Date: 2019-09-25 00:00:00
Tasks: Quantization

State-of-the-art quantization methods can compress deep neural networks down to 4 bits without losing accuracy. However, when it comes to 2 bits, the performance drop is still noticeable. One problem in these methods is that they assign equal bit rate to quantize weights and activations in all layers, which is not reasonable in the case of high rate compression (such as 2-bit quantization), as some of layers in deep neural networks are sensitive to quantization and performing coarse quantization on these layers can hurt the accuracy. In this paper, we address an important problem of how to optimize the bit allocation of weights and activations for deep CNNs compression. We first explore the additivity of output error caused by quantization and find that additivity property holds for deep neural networks which are continuously differentiable in the layers. Based on this observation, we formulate the optimal bit allocation problem of weights and activations in a joint framework and propose a very efficient method to solve the optimization problem via Lagrangian Formulation. Our method obtains excellent results on deep neural networks. It can compress deep CNN ResNet-50 down to 2 bits with only 0.7% accuracy loss. To the best our knowledge, this is the first paper that reports 2-bit results on deep CNNs without hurting the accuracy.
Domain-invariant Learning using Adaptive Filter Decomposition
Authors: Ze Wang, Xiuyuan Cheng, Guillermo Sapiro, Qiang Qiu
Date: 2019-09-25 00:00:00
Tasks: Domain Adaptation

Domain shifts are frequently encountered in real-world scenarios. In this paper, we consider the problem of domain-invariant deep learning by explicitly modeling domain shifts with only a small amount of domain-specific parameters in a Convolutional Neural Network (CNN). By exploiting the observation that a convolutional filter can be well approximated as a linear combination of a small set of basis elements, we show for the first time, both empirically and theoretically, that domain shifts can be effectively handled by decomposing a regular convolutional layer into a domain-specific basis layer and a domain-shared basis coefficient layer, while both remain convolutional. An input channel will now first convolve spatially only with each respective domain-specific basis to ``absorb" domain variations, and then output channels are linearly combined using common basis coefficients trained to promote shared semantics across domains. We use toy examples, rigorous analysis, and real-world examples to show the framework's effectiveness in cross-domain performance and domain adaptation. With the proposed architecture, we need only a small set of basis elements to model each additional domain, which brings a negligible amount of additional parameters, typically a few hundred.
Gradient-free Neural Network Training by Multi-convex Alternating Optimization
Authors: Junxiang Wang, Fuxun Yu, Xiang Chen, Liang Zhao
Date: 2019-09-25 00:00:00

In recent years, stochastic gradient descent (SGD) and its variants have been the dominant optimization methods for training deep neural networks. However, SGD suffers from limitations such as the lack of theoretical guarantees, vanishing gradients, excessive sensitivity to input, and difficulties solving highly non-smooth constraints and functions. To overcome these drawbacks, alternating minimization-based methods for deep neural network optimization have attracted fast-increasing attention recently. As an emerging and open domain, however, several new challenges need to be addressed, including 1) Convergence depending on the choice of hyperparameters, and 2) Lack of unified theoretical frameworks with general conditions. We, therefore, propose a novel Deep Learning Alternating Minimization (DLAM) algorithm to deal with these two challenges. Our innovative inequality-constrained formulation infinitely approximates the original problem with non-convex equality constraints, enabling our proof of global convergence of the DLAM algorithm under mild, practical conditions, regardless of the choice of hyperparameters and wide range of various activation functions. Experiments on benchmark datasets demonstrate the effectiveness of DLAM.
Policy Optimization In the Face of Uncertainty
Authors: Tung-Long Vuong, Han Nguyen, Hai Pham, Kenneth Tran
Date: 2019-09-25 00:00:00
Tasks: continuous-control, Continuous Control, Model-based Reinforcement Learning

Model-based reinforcement learning has the potential to be more sample efficient than model-free approaches. However, existing model-based methods are vulnerable to model bias, which leads to poor generalization and asymptotic performance compared to model-free counterparts. In this paper, we propose a novel policy optimization framework using an uncertainty-aware objective function to handle those issues. In this framework, the agent simultaneously learns an uncertainty-aware dynamics model and optimizes the policy according to these learned models. Under this framework, the objective function can represented end-to-end as a single computational graph, which allows seamless policy gradient computation via backpropagation through the models. In addition to being theoretically sound, our approach shows promising results on challenging continuous control benchmarks with competitive asymptotic performance and sample complexity compared to state-of-the-art baselines.
Discrete InfoMax Codes for Meta-Learning
Authors: Yoonho Lee, Wonjae Kim, Seungjin Choi
Date: 2019-09-25 00:00:00
Tasks: Meta-Learning, Metric Learning

This paper analyzes how generalization works in meta-learning. Our core contribution is an information-theoretic generalization bound for meta-learning, which identifies the expressivity of the task-specific learner as the key factor that makes generalization to new datasets difficult. Taking inspiration from our bound, we present Discrete InfoMax Codes (DIMCO), a novel meta-learning model that trains a stochastic encoder to output discrete codes. Experiments show that DIMCO requires less memory and less time for similar performance to previous metric learning methods and that our method generalizes particularly well in a challenging small-data setting.
Monte Carlo Deep Neural Network Arithmetic
Authors: Julian Faraone, Philip Leong
Date: 2019-09-25 00:00:00
Tasks: image-classification, Image Classification, Quantization, Sensitivity

Quantization is a crucial technique for achieving low-power, low latency and high throughput hardware implementations of Deep Neural Networks. Quantized floating point representations have received recent interest due to their hardware efficiency benefits and ability to represent a higher dynamic range than fixed point representations, leading to improvements in accuracy.  We present a novel technique, Monte Carlo Deep Neural Network Arithmetic (MCA), for determining the sensitivity of Deep Neural Networks to quantization in floating point arithmetic.We do this by applying Monte Carlo Arithmetic to the inference computation and analyzing the relative standard deviation of the neural network loss.  The method makes no assumptions regarding the underlying parameter distributions. We evaluate our method on pre-trained image classification models on the CIFAR10 andImageNet datasets.  For the same network topology and dataset, we demonstrate the ability to gain the equivalent of bits of precision by simply choosing weight parameter sets which demonstrate a lower loss of significance from the Monte Carlo trials.   Additionally,  we can apply MCA to compare the sensitivity of different network topologies to quantization effects.
Language-independent Cross-lingual Contextual Representations
Authors: Xiao Zhang, Song Wang, Dejing Dou, Xien Liu, Thien Huu Nguyen, Ji Wu
Date: 2019-09-25 00:00:00
Tasks: Cross-Lingual Transfer, Transfer Learning, Zero-Shot Cross-Lingual Transfer

Contextual representation models like BERT have achieved state-of-the-art performance on a diverse range of NLP tasks. We propose a cross-lingual contextual representation model that generates language-independent contextual representations. This helps to enable zero-shot cross-lingual transfer of a wide range of NLP models, on top of contextual representation models like BERT. We provide a formulation of language-independent cross-lingual contextual representation based on mono-lingual representations. Our formulation takes three steps to align sequences of vectors: transform, extract, and reorder. We present a detailed discussion about the process of learning cross-lingual contextual representations, also about the performance in cross-lingual transfer learning and its implications.
Deep Nonlinear Stochastic Optimal Control for Systems with Multiplicative Uncertainties
Authors: Marcus Pereira, Ziyi Wang, Tianrong Chen, Evangelos Theodorou
Date: 2019-09-25 00:00:00
Tasks: LEMMA

We present a deep recurrent neural network architecture to solve a class of stochastic optimal control problems described by fully nonlinear Hamilton Jacobi Bellman partial differential equations. Such PDEs arise when one considers stochastic dynamics characterized by uncertainties that are additive and control multiplicative. Stochastic models with the aforementioned characteristics have been used in computational neuroscience, biology, finance and aerospace systems and provide a more accurate representation of actuation than models with additive uncertainty. Previous literature has established the inadequacy of the linear HJB theory and instead rely on a non-linear Feynman-Kac lemma resulting in a second order forward-backward stochastic differential equations representation. However, the proposed solutions that use this representation suffer from compounding errors and computational complexity leading to lack of scalability. In this paper, we propose a deep learning based algorithm that leverages the second order Forward-Backward SDE representation and LSTM based recurrent neural networks to not only solve such Stochastic Optimal Control problems but also overcome the problems faced by previous approaches and scales well to high dimensional systems. The resulting control algorithm is tested on non-linear systems in robotics and biomechanics to demonstrate feasibility and out-performance against previous methods.
Distilled embedding: non-linear embedding factorization using knowledge distillation
Authors: Vasileios Lioutas, Ahmad Rashid, Krtin Kumar, Md Akmal Haidar, Mehdi Rezagholizadeh
Date: 2019-09-25 00:00:00
Tasks: Knowledge Distillation, Machine Translation, Translation, Word Embeddings

Word-embeddings are a vital component of Natural Language Processing (NLP) systems and have been extensively researched. Better representations of words have come at the cost of huge memory footprints, which has made deploying NLP models on edge-devices challenging due to memory limitations. Compressing embedding matrices without sacrificing model performance is essential for successful commercial edge deployment. In this paper, we propose Distilled Embedding, an (input/output) embedding compression method based on low-rank matrix decomposition with an added non-linearity. First, we initialize the weights of our decomposition by learning to reconstruct the full word-embedding and then fine-tune on the downstream task employing knowledge distillation on the factorized embedding. We conduct extensive experimentation with various compression rates on machine translation, using different data-sets with a shared word-embedding matrix for both embedding and vocabulary projection matrices. We show that the proposed technique outperforms conventional low-rank matrix factorization, and other recently proposed word-embedding matrix compression methods.
Switched linear projections and inactive state sensitivity for deep neural network interpretability
Authors: Lech Szymanski, Brendan McCane, Craig Atkinson
Date: 2019-09-25 00:00:00
Tasks: Sensitivity

We introduce switched linear projections for expressing the activity of a neuron in a ReLU-based deep neural network in terms of a single linear projection in the input space. The method works by isolating the active subnetwork, a series of linear transformations, that completely determine the entire computation of the deep network for a given input instance. We also propose that for interpretability it is more instructive and meaningful to focus on the patterns that deactive the neurons in the network, which are ignored by the exisiting methods that implicitly track only the active aspect of the network's computation. We introduce a novel interpretability method for the inactive state sensitivity (Insens). Comparison against existing methods shows that Insens is more robust (in the presence of noise), more complete (in terms of patterns that affect the computation) and a very effective interpretability method for deep neural networks
SpectroBank: A filter-bank convolutional layer for CNN-based audio applications
Authors: Helena Peic Tukuljac, Benjamin Ricaud, Nicolas Aspert, Pierre Vandergheynst
Date: 2019-09-25 00:00:00
Tasks: Audio Classification

We propose and investigate the design of a new convolutional layer where kernels are parameterized functions. This layer aims at being the input layer of convolutional neural networks for audio applications. The kernels are defined as functions having a band-pass filter shape, with a limited number of trainable parameters. We show that networks having such an input layer can achieve state-of-the-art accuracy on several audio classification tasks. This approach, while reducing the number of weights to be trained along with network training time, enables larger kernel sizes, an advantage for audio applications. Furthermore, the learned filters bring additional interpretability and a better understanding of the data properties exploited by the network.
The advantage of using Student's t-priors in variational autoencoders
Authors: Najmeh Abiri, Mattias Ohlsson
Date: 2019-09-25 00:00:00

Is it optimal to use the standard Gaussian prior in variational autoencoders? With Gaussian distributions, which are not weakly informative priors, variational autoencoders struggle to reconstruct the actual data. We provide numerical evidence that encourages using Student's t-distributions as default priors in variational autoencoders, and we challenge the usual setup for the variational autoencoder structure by comparing Gaussian and Student's t-distribution priors with different forms of the covariance matrix.
Optimizing Loss Landscape Connectivity via Neuron Alignment
Authors: N. Joseph Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai
Date: 2019-09-25 00:00:00

The loss landscapes of deep neural networks are poorly understood due to their high nonconvexity. Empirically, the local optima of these loss functions can be connected by a simple curve in model space, along which the loss remains fairly constant. Yet, current path finding algorithms do not consider the influence of symmetry in the loss surface caused by weight permutations of the networks corresponding to the minima. We propose a framework to investigate the effect of symmetry on the landscape connectivity by directly optimizing the weight permutations of the networks being connected. Through utilizing an existing neuron alignment technique, we derive an initialization for the weight permutations. Empirically, this initialization is critical for efficiently learning a simple, planar, low-loss curve between networks that successfully generalizes. Additionally, we introduce a proximal alternating minimization scheme to address if an optimal permutation can be learned, with some provable convergence guarantees. We find that the learned parameterized curve is still a low-loss curve after permuting the weights of the endpoint models, for a subset of permutations. We also show that there is small but steady performance gain in performance of the ensembles constructed from the learned curve, when considering weight space symmetry.
Filling the Soap Bubbles: Efficient Black-Box Adversarial Certification with Non-Gaussian Smoothing
Authors: Dinghuai Zhang*, Mao Ye*, Chengyue Gong*, Zhanxing Zhu, Qiang Liu
Date: 2019-09-25 00:00:00

Randomized classifiers have been shown to provide a promising approach for achieving certified robustness against adversarial attacks in deep learning. However, most existing methods only leverage Gaussian smoothing noise and only work for $\ell_2$ perturbation. We propose a general framework of adversarial certification with non-Gaussian noise and for more general types of attacks, from a unified functional optimization perspective. Our new framework allows us to identify a key trade-off between accuracy and robustness via designing smoothing distributions, helping to design two new families of non-Gaussian smoothing distributions that work more efficiently for $\ell_2$ and $\ell_\infty$ attacks, respectively. Our proposed methods achieve better results than previous works and provide a new perspective on randomized smoothing certification.