pwc-archive/papers-with-abstracts huggingface dataset:
Driving through the Lens: Improving Generalization of Learning-based Steering using Simulated Adversarial Examples
Authors: Yu Shen, Laura Yu Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming Lin
Date: 2021-01-01 00:00:00
Tasks: Autonomous Driving, Data Augmentation, Decision Making, Self-Driving Cars, Sensitivity

To ensure the wide adoption and safety of autonomous driving, the vehicles need to be able to drive under various lighting, weather, and visibility conditions in different environments. These external and environmental factors, along with internal factors associated with sensors, can pose significant challenges to perceptual data processing, hence affecting the decision-making of the vehicle. In this work, we address this critical issue by analyzing the sensitivity of the learning algorithm with respect to varying quality in the image input for autonomous driving.  Using the results of sensitivity analysis, we further propose an algorithm to improve the overall performance of the task of ``learning to steer''. The results show that our approach is able to enhance the learning outcomes up to 48%. A comparative study drawn between our approach and other related techniques, such as data augmentation and adversarial training, confirms the effectiveness of our algorithm as a way to improve the robustness and generalization of neural network training for self-driving cars.
AR-ELBO: Preventing Posterior Collapse Induced by Oversmoothing in Gaussian VAE
Authors: Yuhta Takida, Wei-Hsiang Liao, Toshimitsu Uesaka, Shusuke Takahashi, Yuki Mitsufuji
Date: 2021-01-01 00:00:00

Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon that the learned latent space becomes uninformative. This is related to local optima introduced by a fixed hyperparameter resembling the data variance in the objective function. We suggest that this variance parameter regularizes the VAE and affects its smoothness, which is the magnitude of its gradient. An inappropriate choice of this parameter causes oversmoothness and leads to posterior collapse. This is shown theoretically by analysis on the linear approximated objective function and empirically in general cases. We propose AR-ELBO, which stands for adaptively regularized ELBO~(Evidence Lower BOund). It controls the strength of regularization by adapting the variance parameter, and thus avoids oversmoothing the model. Generation models trained by proposed objectives show improved Fréchet inception distance~(FID) of images generated from the MNIST and CelebA datasets.
Multiplicative Filter Networks
Authors: Rizal Fathony, Anit Kumar Sahu, Devin Willmott, J Zico Kolter
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1

Although deep networks are typically used to approximate functions over high dimensional inputs, recent work has increased interest in neural networks as function approximators for low-dimensional-but-complex functions, such as representing images as a function of pixel coordinates, solving differential equations, or representing signed distance fields or neural radiance fields.  Key to these recent successes has been the use of new elements such as sinusoidal nonlinearities, or Fourier features in positional encodings, which vastly outperform simple ReLU networks.  In this paper, we propose and empirically demonstrate that an arguably simpler class of function approximators can work just as well for such problems: multiplicative filter networks.  In these networks, we avoid traditional compositional depth altogether, and simply multiply together (linear functions of) sinusoidal or Gabor wavelet functions applied to the input.  This representation has the notable advantage that the entire function can simply be viewed as a linear function approximator over an exponential number of Fourier or Gabor basis functions, respectively.  Despite this simplicity, when compared to recent approaches that use Fourier features with ReLU networks or sinusoidal activation networks, we show that these multiplicative filter networks largely outperform or match the performance of these recent approaches on the domains highlighted in these past works.
On the Robustness of Sentiment Analysis for Stock Price Forecasting
Authors: Gabriel Deza, Colin Rowat, Nicolas Papernot
Date: 2021-01-01 00:00:00
Tasks: BIG-bench Machine Learning, Object Recognition, Sentiment Analysis

Machine learning (ML) models are known to be vulnerable to attacks both at training and test time. Despite the extensive literature on adversarial ML, prior efforts focus primarily on applications of computer vision to object recognition or sentiment analysis to movie reviews. In these settings, the incentives for adversaries to manipulate the model's prediction are often unclear and attacks require extensive control of direct inputs to the model. This makes it difficult to evaluate how severe the impact of vulnerabilities exposed is on systems deploying ML with little provenance guarantees for the input data. In this paper, we study adversarial ML with stock price forecasting. Adversarial incentives are clear and may be quantified experimentally through a simulated portfolio.  We replicate an industry standard pipeline, which performs a sentiment analysis of Twitter data to forecast trends in stock prices. We show that an adversary can exploit the lack of provenance to indirectly use tweets to  manipulate the model's perceived sentiment about a target company and in turn force the model to forecast price erroneously. Our attack is mounted at test time and does not modify the training data. Given past market anomalies, we conclude with a series of recommendations for the use of machine learning as input signal to trading algorithms.
Signal Coding and Reconstruction using Spike Trains
Authors: Anik Chattopadhyay, Arunava Banerjee
Date: 2021-01-01 00:00:00

In many animal sensory pathways, the transformation from external stimuli to spike trains is essentially deterministic. In this context, a new mathematical framework for coding and reconstruction, based on a biologically plausible model of the spiking neuron, is presented. The framework considers encoding of a signal through spike trains generated by an ensemble of neurons via a standard convolve-then-threshold mechanism, albeit with a wide variety of convolution kernels. Neurons are distinguished by their convolution kernels and threshold values. Reconstruction is posited as a convex optimization minimizing energy. Formal conditions under which perfect reconstruction of the signal from the spike trains is possible are then identified. Coding experiments on a large audio dataset are presented to demonstrate the strength of the framework.
Generating Plannable Lifted Action Models for Visually Generated Logical Predicates
Authors: Masataro Asai
Date: 2021-01-01 00:00:00
Tasks: Task Planning

We propose FOSAE++, an unsupervised end-to-end neural system that generates a compact discrete state transition model (dynamics / action model) from raw visual observations. Our representation can be exported to Planning Domain Description Language (PDDL), allowing symbolic state-of-the-art classical planners to perform high-level task planning on raw observations. FOSAE++ expresses states and actions in First Order Logic (FOL), a superset of so-called object-centric representation. It is the first unsupervised neural system that fully supports FOL in PDDL action modeling, while existing systems are limited to continuous, propositional, or property-based representations, and/or require manually labeled input.
A Coach-Player Framework for Dynamic Team Composition
Authors: Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Anima Anandkumar
Date: 2021-01-01 00:00:00
Tasks: Zero-shot Generalization

In real-world multi-agent teams, agents with different capabilities may join or leave "on the fly" without altering the team's overarching goals. Coordinating teams with such dynamic composition remains a challenging problem: the optimal team strategy may vary with its composition. Inspired by real-world team sports, we propose a coach-player framework to tackle this problem. We assume that the players only have a partial view of the environment, while the coach has a complete view. The coach coordinates the players by distributing individual strategies. Specifically, we 1) propose an attention mechanism for both the players and the coach; 2) incorporate a variational objective to regularize  learning; and 3) design an adaptive communication method to let the coach decide when to communicate with different players. Our attention mechanism on the players and the coach allows for a varying number of heterogeneous agents, and can thus tackle the dynamic team composition.  We validate our methods on resource collection tasks in multi-agent particle environment. We demonstrate zero-shot generalization to new team compositions with varying numbers of heterogeneous  agents. The performance of our method is comparable or even better than the  setting where all players have a full view of the environment, but no coach. Moreover, we see that the performance stays nearly the same even when the coach communicates as little as 13% of the time using our adaptive communication strategy.  These results demonstrate   the significance of a coach to coordinate players in dynamic teams.
Prior Preference Learning From Experts: Designing A Reward with Active Inference
Authors: Jin Young Shin, Cheolhyeong Kim, Hyung Ju Hwang
Date: 2021-01-01 00:00:00
Tasks: Reinforcement Learning (RL)

Active inference may be defined as Bayesian modeling of a brain with a biologically plausible model of the agent. Its primary idea relies on the free energy principle and the prior preference of the agent. An agent will choose an action that leads to its prior preference for a future observation. In this paper, we claim that active inference can be interpreted using reinforcement learning (RL) algorithms and find a theoretical connection between them. We extend the concept of expected free energy (EFE), which is a core quantity in active inference, and claim that EFE can be treated as a negative value function. Motivated by the concept of prior preference and a theoretical connection, we propose a simple but novel method for learning a prior preference from experts. This illustrates that the problem with RL can be approached with a new perspective of active inference. Experimental results of prior preference learning show the possibility of active inference with EFE-based rewards and its application to an inverse RL problem.
Dual Adversarial Training for Unsupervised Domain Adaptation
Authors: Yuan Wu, Diana Inkpen, Ahmed El-Roby
Date: 2021-01-01 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Deep neural networks obtain remarkable achievements in diverse real-world applications. However, their success relies on the availability of large amounts of labeled data. A trained model may fail to generalize well on a domain whose distribution differs from the training data distribution. Collecting abundant labeled data for all domains of interest are expensive and time-consuming, sometimes even impossible. Domain adaptation sets out to address this problem, aiming to leverage labeled data in the source domain to learn a good predictive model for the target domain whose labels are scarce or unavailable. A mainstream approach is adversarial domain adaptation, which learns domain invariant-features by performing alignment across different distributions. Most domain adaptation methods focus on reducing the divergence between two domains to make the improvement. A prerequisite of domain adaptation is the adaptability, which is measured by the expected error of the ideal joint hypothesis on the source and target domains, should be kept at a small value in the process of domain alignment. However, adversarial learning may degrade the adaptability, since it distorts the original distributions by suppressing the domain-specific information. In this paper, we propose an approach, which focuses on strengthening the model's adaptability, for domain adaptation. Our proposed dual adversarial training (DAT) method introduces class-invariant features to enhance the discriminability of the latent space without sacrificing the transferability. The class-invariant features, extracted from the source domain, can play a positive role in the classification on the target domain. We demonstrate the effectiveness of our method by yielding state-of-the-art results on several benchmarks.
ECONOMIC HYPERPARAMETER OPTIMIZATION WITH BLENDED SEARCH STRATEGY
Authors: Chi Wang, Qingyun Wu, Silu Huang, Amin Saied
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: AutoML, Hyperparameter Optimization

We study the problem of using low cost to search for hyperparameter configurations in a large search space with heterogeneous evaluation cost and model quality.
We propose a blended search strategy to combine the strengths of global and local search, and prioritize them on the fly with the goal of minimizing the total cost spent in finding good configurations. Our approach demonstrates robust performance for tuning both tree-based models and deep neural networks on a large AutoML benchmark, as well as superior performance in model quality, time, and resource consumption for a production NLP model fine-tuning task.
Precondition Layer and Its Use for GANs
Authors: Tiantian Fang, Alex Schwing, Ruoyu Sun
Date: 2021-01-01 00:00:00

One of the major challenges when training generative adversarial nets (GANs) is instability.  To address this instability spectral normalization (SN) is remarkably successful.  However, SN-GAN still suffers from training instabilities, especially when working with higher-dimensional data.  We find that those instabilities are accompanied by large condition numbers of the discriminator weight matrices. To improve training stability we study common linear-algebra practice and employ preconditioning.   Specifically,  we introduce a  preconditioning layer  (PC-layer)that performs a  low-degree polynomial preconditioning.   We use this  PC-layer in two ways:  1)  fixed preconditioning  (FPC)  adds a  fixed  PC-layer to all layers, and 2)  adaptive preconditioning  (APC)  adaptively controls the strength of preconditioning. Empirically, we show that FPC and APC stabilize the training of un-conditional GANs using classical architectures.  On LSUN256×256 data, APC improves FID scores by around 5 points over baselines.
Multinomial Variational Autoencoders can recover Principal Components
Authors: James Morton, Justin Silverman, Gleb Tikhonov, Harri Lähdesmäki, Rich Bonneau
Date: 2021-01-01 00:00:00

Covariance estimation on high dimensional data is a central challenge across multiple scientific disciplines. Sparse high-dimensional count data frequently encountered in biological applications such as DNA sequencing and proteomics are often well modeled using multinomial logistic-normal models.  In many cases these datasets are also compositional, presented item-wise as fractions of a normalized total, necessitated by measurement and instrument constraints. Yet three key challenge prove limiting in covariance estimation with these models: (1) the computational complexity of inverting high-dimensional covariance matrices, (2) non-exchangability introduced from the summation constraint on multinomial parameters, (3) the irreducibility of the component multinomial logistic-normal distribution that necessitates the use of parameter augmentation, or similar techniques, during inference.  We show that a variational autoencoder augmented with a fast Isometric Log-ratio (ILR) transform can address these issues and accurately estimate principal components from multinomially logistic-normal distributed data.
This model can be optimized on GPUs and modified to handle mini-batching, with the ability to scale across thousands of dimensions and thousands of samples.
Accelerating DNN Training through Selective Localized Learning
Authors: Sarada Krithivasan, Sanchari Sen, Swagath Venkataramani, Anand Raghunathan
Date: 2021-01-01 00:00:00
Tasks: GPU

Training Deep Neural Networks (DNNs) places immense compute requirements on the underlying hardware platforms, expending large amounts of time and energy. We proposeLoCal+SGD, a new algorithmic approach to accelerate DNN train-ing by selectively combining localized or Hebbian learning within a StochasticGradient Descent (SGD) based training framework. Back-propagation is a computationally expensive process that requires 2 Generalized Matrix Multiply (GEMM)operations to compute the error and weight gradients for each layer. We alleviate this by selectively updating some layers’ weights using localized learning rules that require only 1 GEMM operation per layer. Further, since the weight update is performed during the forward pass itself, the layer activations for the mini-batch do not need to be stored until the backward pass, resulting in a reduced memory footprint. Localized updates can substantially boost training speed, but need to be used selectively and judiciously in order to preserve accuracy and convergence. We address this challenge through the design of a Learning Mode Selection Algorithm, where all layers start with SGD, and as epochs progress, layers gradually transition to localized learning. Specifically, for each epoch, the algorithm identifies a Localized→SGDtransition layer, which delineates the network into two regions. Layers before the transition layer use localized updates, while the transition layer and later layers use gradient-based updates. The trend in the weight updates made to the transition layer across epochs is used to determine how the boundary betweenSGD and localized updates is shifted in future epochs. We also propose a low-cost weak supervision mechanism by controlling the learning rate of localized updates based on the overall training loss. We appliedLoCal+SGDto 8 image recognition CNNs (including ResNet50 and MobileNetV2) across 3 datasets (Cifar10, Cifar100and ImageNet). Our measurements on a Nvidia GTX 1080Ti GPU demonstrate upto 1.5×improvement in end-to-end training time with∼0.5% loss in Top-1classification accuracy.
Nonconvex Continual Learning with Episodic Memory
Authors: Sungyeob Han, Yeongmo Kim, Jungwoo Lee
Date: 2021-01-01 00:00:00
Tasks: Continual Learning, image-classification, Image Classification

Continual learning aims to prevent catastrophic forgetting while learning a new task without accessing data of previously learned tasks. 
The memory for such learning scenarios build a small subset of the data for previous tasks and is used in various ways such as quadratic programming and sample selection. 
Current memory-based continual learning algorithms are formulated as a constrained optimization problem and rephrase constraints as a gradient-based approach.
However, previous works have not provided the theoretical proof on convergence to previously learned tasks.
In this paper, we propose a theoretical convergence analysis of continual learning based on stochastic gradient descent method.
Our method, nonconvex continual learning (NCCL), can achieve the same convergence rate when the proposed catastrophic forgetting term is suppressed at each iteration.
We also show that memory-based approaches have an inherent problem of overfitting to memory, which degrades the performance on previously learned tasks, namely catastrophic forgetting.
We empirically demonstrate that NCCL successfully performs continual learning with episodic memory by scaling learning rates adaptive to mini-batches on several image classification tasks.
Robust Overfitting may be mitigated by properly learned smoothening
Authors: Tianlong Chen, Zhenyu Zhang, Sijia Liu, Shiyu Chang, Zhangyang Wang
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: Knowledge Distillation

A recent study (Rice et al.,  2020) revealed overfitting to be a dominant phenomenon in adversarially robust training of deep networks, and that appropriate early-stopping of adversarial training (AT) could match the performance gains of most recent algorithmic improvements. This intriguing problem of robust overfitting motivates us to seek more remedies. As a pilot study, this paper investigates two empirical means to inject more learned smoothening during AT: one leveraging knowledge distillation and self-training to smooth the logits, the other performing stochastic weight averaging (Izmailov et al., 2018) to smooth the weights. Despite the embarrassing simplicity, the two approaches are surprisingly effective and hassle-free in mitigating robust overfitting. Experiments demonstrate that by plugging in them to AT, we can simultaneously boost the standard accuracy by 3.72%~6.68% and robust accuracy by 0.22%~2 .03%, across multiple datasets (STL-10, SVHN, CIFAR-10, CIFAR-100, and Tiny ImageNet), perturbation types ($\ell_{\infty}$ and $\ell_2$), and robustified methods (PGD, TRADES, and FSGM), establishing the new state-of-the-art bar in AT. We present systematic visualizations and analysis to dive into their possible working mechanisms. We also carefully exclude the possibility of gradient masking by evaluating our models' robustness against transfer attacks. We promise to publicly release our codes and pre-trained models.
Learning Task Decomposition with Order-Memory Policy Network
Authors: Yuchen Lu, Yikang Shen, Siyuan Zhou, Aaron Courville, Joshua B. Tenenbaum, Chuang Gan
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: Imitation Learning, Inductive Bias

Many complex real-world tasks are composed of several levels of sub-tasks. Humans leverage these hierarchical structures to accelerate the learning process and achieve better generalization. To simulate this process, we introduce Ordered Memory Policy Network (OMPN) to discover task decomposition by imitation learning from demonstration. OMPN has an explicit inductive bias to model a hierarchy of sub-tasks. Experiments on Craft world and Dial demonstrate that our model can more accurately recover the task boundaries with behavior cloning under both unsupervised and weakly supervised setting than previous methods. OMPN can also be directly applied to partially observable environments and still achieve high performance. Our visualization further confirms the intuition that OMPN can learn to expand the memory at higher levels when one subtask is close to completion.
Variational saliency maps for explaining model's behavior
Authors: Jae Myung Kim, Eunji Kim, Seokhyeon Ha, Sungroh Yoon, Jungwoo Lee
Date: 2021-01-01 00:00:00

Saliency maps have been widely used to explain the behavior of an image classifier. We introduce a new interpretability method which considers a saliency map as a random variable and aims to calculate the posterior distribution over the saliency map. The likelihood function is designed to measure the distance between the classifier's predictive probability of an image and that of locally perturbed image. For the prior distribution, we make attributions of adjacent pixels have a positive correlation. We use a variational approximation, and show that the approximate posterior is effective in explaining the classifier's behavior. It also has benefits of providing uncertainty over the explanation, giving auxiliary information to experts on how much the explanation is trustworthy.