pwc-archive/papers-with-abstracts huggingface dataset:
(Updated submission 11/20/2020) MISIM: A Novel Code Similarity System
Authors: Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Nesime Tatbul, Jesmin Jahan Tithi, Niranjan Hasabnis, Paul Petersen, Timothy G Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, Justin Gottschlich
Date: 2020-09-28 00:00:00

Semantic code similarity systems are integral to a range of applications from code recommendation to automated software defect correction. Yet, these systems still lack the maturity in accuracy for general and reliable wide-scale usage. To help address this, we present Machine Inferred Code Similarity (MISIM), a novel end-to-end code similarity system that consists of two core components. First, MISIM uses a novel context-aware semantic structure (CASS), which is designed to aid in lifting semantic meaning from code syntax. We compare CASS with the abstract syntax tree (AST) and show CASS is more accurate than AST by up to 1.67x. Second, MISIM provides a neural-based code similarity scoring algorithm, which can be implemented with various neural network architectures with learned parameters. We compare MISIM to four state-of-the-art systems: (i) Aroma, (ii) code2seq, (iii) code2vec, and (iv) Neural Code Comprehension. In our experimental evaluation across 328,155 programs (over 18 million lines of code), MISIM has 1.5x to 43.4x better accuracy across all four systems.
On The Adversarial Robustness of 3D Point Cloud Classification
Authors: Jiachen Sun, Karl Koenig, Yulong Cao, Qi Alfred Chen, Zhuoqing Mao
Date: 2020-09-28 00:00:00
Tasks: 3D Point Cloud Classification, Adversarial Robustness, Autonomous Driving, Classification, Point Cloud Classification

3D point clouds play pivotal roles in various safety-critical fields, such as autonomous driving, which desires the corresponding deep neural networks to be robust to adversarial perturbations. Though a few defenses against adversarial point cloud classification have been proposed, it remains unknown whether they can provide real robustness. To this end, we perform the first security analysis of state-of-the-art defenses and design adaptive attacks on them. Our 100% adaptive attack success rates demonstrate that current defense designs are still vulnerable. Since adversarial training (AT) is believed to be the most effective defense, we present the first in-depth study showing how AT behaves in point cloud classification and identify that the required symmetric function (pooling operation) is paramount to the model's robustness under AT. Through our systematic analysis, we find that the default used fixed pooling operations (e.g., MAX pooling) generally weaken AT's performance in point cloud classification. Still, sorting-based parametric pooling operations can significantly improve the models' robustness. Based on the above insights, we further propose DeepSym, a deep symmetric pooling operation, to architecturally advance the adversarial robustness under AT to 47.01% without sacrificing nominal accuracy, outperforming the original design and a strong baseline by 28.5% ($\sim 2.6 \times$) and 6.5%, respectively, in PointNet.
TextSETTR: Label-Free Text Style Extraction and Tunable Targeted Restyling
Authors: Parker Riley, Noah Constant, Mandy Guo, Girish Kumar, David Uthus, Zarana Parekh
Date: 2020-09-28 00:00:00
Tasks: Decoder, Style Transfer, Text Style Transfer

We present a novel approach to the challenging problem of label-free text style transfer. Unlike previous approaches that use parallel or non-parallel labeled data, our technique removes the need for labels entirely, relying instead on the implicit connection in style between adjacent sentences in unlabeled text. We show that T5 (Raffel et al., 2020), a strong pretrained text-to-text model, can be adapted to extract a style vector from arbitrary text and use this vector to condition the decoder to perform style transfer. As the resulting learned style vector space encodes many facets of textual style, we recast transfers as "targeted restyling" vector operations that adjust specific attributes of the input text while preserving others. When trained over unlabeled Amazon reviews data, our resulting TextSETTR model is competitive on sentiment transfer, even when given only four exemplars of each class. Furthermore, we demonstrate that a single model trained on unlabeled Common Crawl data is capable of transferring along multiple dimensions including dialect, emotiveness, formality, politeness, and sentiment.
Near-Optimal Regret Bounds for Model-Free RL in Non-Stationary Episodic MDPs
Authors: Weichao Mao, Kaiqing Zhang, Ruihao Zhu, David Simchi-Levi, Tamer Basar
Date: 2020-09-28 00:00:00
Tasks: Q-Learning, Reinforcement Learning (RL)

We consider model-free reinforcement learning (RL) in non-stationary Markov decision processes (MDPs). Both the reward functions and the state transition distributions are allowed to vary over time, either gradually or abruptly, as long as their cumulative variation magnitude does not exceed certain budgets. We propose an algorithm, named Restarted Q-Learning with Upper Confidence Bounds (RestartQ-UCB), for this setting, which adopts a simple restarting strategy and an extra optimism term. Our algorithm outperforms the state-of-the-art (model-based) solution in terms of dynamic regret. Specifically, RestartQ-UCB with Freedman-type bonus terms achieves a dynamic regret of $\widetilde{O}(S^{\frac{1}{3}} A^{\frac{1}{3}} \Delta^{\frac{1}{3}} H T^{\frac{2}{3}})$, where $S$ and $A$ are the numbers of states and actions, respectively, $\Delta>0$ is the variation budget, $H$ is the number of steps per episode, and $T$ is the total number of steps. We further show that our algorithm is near-optimal by establishing an information-theoretical lower bound of $\Omega(S^{\frac{1}{3}} A^{\frac{1}{3}} \Delta^{\frac{1}{3}} H^{\frac{2}{3}} T^{\frac{2}{3}})$, which to the best of our knowledge is the first impossibility result in non-stationary RL in general.
Zero-Shot Learning with Common Sense Knowledge Graphs
Authors: Nihal Nayak, Stephen Bach
Date: 2020-09-28 00:00:00
Tasks: Common Sense Reasoning, Entity Typing, intent-classification, Intent Classification, Knowledge Graphs, Zero-Shot Learning

Zero-shot learning relies on semantic class representations such as hand-engineered attributes or learned embeddings to predict classes without any labeled examples. We propose to learn class representations from common sense knowledge graphs. Common sense knowledge graphs are an untapped source of explicit high-level knowledge that requires little human effort to apply to a range of tasks. To capture the knowledge in the graph, we introduce ZSL-KG, a general-purpose framework with a novel transformer graph convolutional network (TrGCN) to generate class representations. Our proposed TrGCN architecture computes non-linear combinations of the node neighbourhood and leads to significant improvements on zero-shot learning tasks. We report new state-of-the-art accuracies on six zero-shot benchmark datasets in object classification, intent classification, and fine-grained entity typing tasks. ZSL-KG outperforms the specialized state-of-the-art method for each task by an average 1.7 accuracy points and outperforms the general-purpose method with the best average accuracy by 5.3 points. Our ablation study on ZSL-KG with alternate graph neural networks shows that our transformer-based aggregator adds up to 2.8 accuracy points improvement on these tasks.
Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Reconstruction
Authors: Karl Willis, Yewen Pu, Jieliang Luo, Hang Chu, Tao Du, Joseph Lambourne, Armando Solar-Lezama, Wojciech Matusik
Date: 2020-09-28 00:00:00
Tasks: CAD Reconstruction

Parametric computer-aided design (CAD) is a standard paradigm used for the design of manufactured objects. CAD designers perform modeling operations, such as sketch and extrude, to form a construction sequence that makes up a final design. Despite the pervasiveness of parametric CAD and growing interest from the research community, a dataset of human designed 3D CAD construction sequences has not been available to-date. In this paper we present the Fusion 360 Gallery reconstruction dataset and environment for learning CAD reconstruction. We provide a dataset of 8,625 designs, comprising sequential sketch and extrude modeling operations, together with a complementary environment called the Fusion 360 Gym, to assist with performing CAD reconstruction. We outline a standard CAD reconstruction task, together with evaluation metrics, and present results from a novel method using neurally guided search to recover a construction sequence from a target geometry.
Amortized Conditional Normalized Maximum Likelihood
Authors: Aurick Zhou, Sergey Levine
Date: 2020-09-28 00:00:00
Tasks: Bayesian Inference

While deep neural networks provide good performance for a range of challenging tasks, calibration and uncertainty estimation remain major challenges. In this paper, we propose the amortized conditional normalized maximum likelihood (ACNML)
method as a scalable general-purpose approach for uncertainty estimation, calibration, and out-of-distribution robustness with deep networks. Our algorithm builds on the conditional normalized maximum likelihood (CNML) coding scheme, which has minimax optimal properties according to the minimum description length principle, but is computationally intractable to evaluate exactly for all but the simplest of model classes. We propose to use approximate Bayesian inference technqiues to produce a tractable approximation to the CNML distribution. Our approach can be combined with any approximate inference algorithm that provides tractable posterior densities over model parameters. We demonstrate that ACNML compares favorably to a number of prior techniques for uncertainty estimation in terms of and calibration on out-of-distribution inputs.
Linear Convergence and Implicit Regularization of Generalized Mirror Descent with Time-Dependent Mirrors
Authors: Adityanarayanan Radhakrishnan, Mikhail Belkin, Caroline Uhler
Date: 2020-09-28 00:00:00

 The following questions are fundamental to understanding the properties of over-parameterization in modern machine learning: (1) Under what conditions and at what rate does training converge to a global minimum? (2) What form of implicit regularization occurs through training? While significant progress has been made in answering both of these questions for gradient descent, they have yet to be answered more completely for general optimization methods. In this work, we establish sufficient conditions for linear convergence and obtain approximate implicit regularization results for generalized mirror descent (GMD), a generalization of mirror descent with a possibly time-dependent mirror. GMD subsumes popular first order optimization methods including gradient descent, mirror descent, and preconditioned gradient descent methods such as Adagrad. By using the Polyak-Lojasiewicz inequality, we first present a simple analysis under which non-stochastic GMD converges linearly to a global minimum. We then present a novel, Taylor-series based analysis to establish sufficient conditions for linear convergence of stochastic GMD. As a corollary, our result establishes sufficient conditions and provides learning rates for linear convergence of stochastic mirror descent and Adagrad. Lastly, we obtain approximate implicit regularization results for GMD by proving that GMD converges to an interpolating solution that is approximately the closest interpolating solution to the initialization in l2-norm in the dual space, thereby generalizing the result of Azizan, Lale, and Hassibi (2019) in the full batch setting.
Equivariant Normalizing Flows for Point Processes and Sets
Authors: Marin Biloš, Stephan Günnemann
Date: 2020-09-28 00:00:00
Tasks: Point Processes

A point process describes how random sets of exchangeable points are generated. The points usually influence the positions of each other via attractive and repulsive forces. To model this behavior, it is enough to transform the samples from the uniform process with a sufficiently complex equivariant function. However, learning the parameters of the resulting process is challenging since the likelihood is hard to estimate and often intractable. This leads us to our proposed model - CONFET. Based on continuous normalizing flows, it allows arbitrary interactions between points while having tractable likelihood. Experiments on various real and synthetic datasets show the improved performance of our new scalable approach.
Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search
Authors: Binxin Ru, Clare Lyle, Lisa Schut, Mark van der Wilk, Yarin Gal
Date: 2020-09-28 00:00:00
Tasks: Model Selection, Neural Architecture Search

Reliable yet efficient evaluation of generalisation performance of a proposed architecture is crucial to the success of neural architecture search (NAS). Traditional approaches face a variety of limitations: training each architecture to completion is prohibitively expensive, early stopping estimates may correlate poorly with fully trained performance, and model-based estimators require large training sets. Instead, motivated by recent results linking training speed and generalisation with stochastic gradient descent, we propose to estimate the final test performance based on the sum of training losses. Our estimator is inspired by the marginal likelihood, which is used for Bayesian model selection. Our model-free estimator is simple, efficient, and cheap to implement, and does not require hyperparameter-tuning or surrogate training before deployment. We demonstrate empirically that our estimator consistently outperforms other baselines under various settings and can achieve a rank correlation of 0.95 with final test accuracy on the NAS-Bench201 dataset within 50 epochs.
PareCO: Pareto-aware Channel Optimization for Slimmable Neural Networks
Authors: Rudy Chin, Ari S. Morcos, Diana Marculescu
Date: 2020-09-28 00:00:00

Slimmable neural networks provide a flexible trade-off front between prediction error and computational cost (such as the number of floating-point operations or FLOPs) with the same storage cost as a single model. They have been proposed recently for resource-constrained settings such as mobile devices. However, current slimmable neural networks use a single width-multiplier for all the layers to arrive at sub-networks with different performance profiles, which neglects that different layers affect the network's prediction accuracy differently and have different FLOP requirements. Hence,  developing a principled approach for deciding width-multipliers across different layers could potentially improve the performance of slimmable networks. To allow for heterogeneous width-multipliers across different layers, we formulate the problem of optimizing slimmable networks from a multi-objective optimization lens, which leads to a novel algorithm for optimizing both the shared weights and the width-multipliers for the sub-networks. We perform extensive empirical analysis with 15 network and dataset combinations and two types of cost objectives, i.e., FLOPs and memory footprint, to demonstrate the effectiveness of the proposed method compared to existing alternatives. Quantitatively, improvements up to 1.7% and 8% in top-1 accuracy on the ImageNet dataset can be attained for MobileNetV2 considering FLOPs and memory footprint, respectively. Our results highlight the potential of optimizing the channel counts for different layers jointly with the weights for slimmable networks.
Adaptive Discretization for Continuous Control using Particle Filtering Policy Network
Authors: Pei Xu, Ioannis Karamouzas
Date: 2020-09-28 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning

Controlling the movements of highly articulated agents and robots has been a long-standing challenge to model-free deep reinforcement learning. In this paper, we propose a simple, yet general, framework for improving the performance of policy gradient algorithms by discretizing the continuous action space. Instead of using a fixed set of predetermined atomic actions, we exploit particle filtering to adaptively discretize actions during training and track the posterior policy represented as a mixture distribution. The resulting policy can replace the original continuous policy of any given policy gradient algorithm without changing its underlying model architecture. We demonstrate the applicability of our approach to state-of-the-art on-policy and off-policy baselines in challenging control tasks. Baselines using our particle-based policies achieve better final performance and speed of convergence as compared to corresponding continuous implementations and implementations that rely on fixed discretization schemes.
Reducing Class Collapse in Metric Learning with Easy Positive Sampling
Authors: Elad Levi, Tete Xiao, Xiaolong Wang, Trevor Darrell
Date: 2020-09-28 00:00:00
Tasks: Image Retrieval, Metric Learning, Retrieval, Triplet

Metric learning seeks perceptual embeddings where visually similar instances are close and dissimilar instances are apart, but learned representation can be sub-optimal when the distribution of intra-class samples is diverse and distinct sub-clusters are present. We theoretically prove and empirically show that under reasonable noise assumptions, prevalent embedding losses in metric learning, e.g., triplet loss, tend to project all samples of a class with various modes onto a single point in the embedding space, resulting in a class collapse that usually renders the space ill-sorted for classification or retrieval. To address this problem, we propose a simple modification to the embedding losses such that each sample selects its nearest same-class counterpart in a batch as the positive element in the tuple/triplet. This allows for the presence of multiple sub-clusters within each class. The adaptation can be integrated into a wide range of metric learning losses. Our method demonstrates clear benefits on various fine-grained image retrieval datasets over a variety of existing losses; qualitative retrieval results show that samples with similar visual patterns are indeed closer in the embedding space.
Non-greedy Gradient-based Hyperparameter Optimization Over Long Horizons
Authors: Paul Micaelli, Amos Storkey
Date: 2020-09-28 00:00:00
Tasks: Few-Shot Learning, Hyperparameter Optimization, Meta-Learning

Gradient-based meta-learning has earned a widespread popularity in few-shot learning, but remains broadly impractical for tasks with long horizons (many gradient steps), due to memory scaling and gradient degradation issues. A common workaround is to learn meta-parameters online, but this introduces greediness which comes with a significant performance drop. In this work, we enable non-greedy meta-learning of hyperparameters over long horizons by sharing hyperparameters that are contiguous in time, and using the sign of hypergradients rather than their magnitude to indicate convergence. We implement this with forward-mode differentiation, which we extend to the popular momentum-based SGD optimizer. We demonstrate that the hyperparameters of this optimizer can be learned non-greedily without gradient degradation over $\sim 10^4$ inner gradient steps, by only requiring $\sim 10$ outer gradient steps. On CIFAR-10, we outperform greedy and random search methods for the same computational budget by nearly $10\%$. Code will be available upon publication.
SALR: Sharpness-aware Learning Rates for Improved Generalization
Authors: Xubo Yue, Maher Nouiehed, Raed Al Kontar
Date: 2020-09-28 00:00:00

In an effort to improve generalization in deep learning, we propose SALR: a sharpness-aware learning rate update technique designed to recover flat minimizers. Our method dynamically updates the learning rate of gradient-based optimizers based on the local sharpness of the loss function. This allows optimizers to automatically increase learning rates at sharp valleys to increase the chance of escaping them. We demonstrate the effectiveness of SALR when adopted by various algorithms over a broad range of networks. Our experiments indicate that SALR improves generalization, converges faster, and drives solutions to significantly flatter regions.
With False Friends Like These, Who Can Have Self-Knowledge?
Authors: Lue Tao, Songcan Chen
Date: 2020-09-28 00:00:00
Tasks: Sensitivity

Adversarial examples arise from excessive sensitivity of a model. Commonly studied adversarial examples are malicious inputs, crafted by an adversary from correctly classified examples, to induce misclassification. This paper studies an intriguing, yet far overlooked consequence of the excessive sensitivity, that is, a misclassified example can be easily perturbed to help the model to produce correct output. Such perturbed examples look harmless, but actually can be maliciously utilized by a false friend to make the model self-satisfied. Thus we name them hypocritical examples. With false friends like these, a poorly performed model could behave like a state-of-the-art one. Once a deployer trusts the hypocritical performance and uses the "well-performed" model in real-world applications, potential security concerns appear even in benign environments. In this paper, we formalize the hypocritical risk for the first time and propose a defense method specialized for hypocritical examples by minimizing the tradeoff between natural risk and an upper bound of hypocritical risk. Moreover, our theoretical analysis reveals connections between adversarial risk and hypocritical risk. Extensive experiments verify the theoretical results and the effectiveness of our proposed methods.
Semi-Supervised Learning of Multi-Object 3D Scene Representations
Authors: Cathrin Elich, Martin R. Oswald, Marc Pollefeys, Joerg Stueckler
Date: 2020-09-28 00:00:00
Tasks: Decision Making, Object, Scene Understanding

Representing scenes at the granularity of objects is a prerequisite for scene understanding and decision making. We propose a novel approach for learning multi-object 3D scene representations from images. A recurrent encoder regresses a latent representation of 3D shapes, poses and texture of each object from an input RGB image. The 3D shapes are represented continuously in function-space as signed distance functions (SDF) which we efficiently pre-train from example shapes. By differentiable rendering, we train our model to decompose scenes self-supervised from RGB-D images. Our approach learns to decompose images into the constituent objects of the scene and to infer their shape, pose and texture properties from a single view. In experiments, we evaluate the accuracy of our model in inferring the 3D scene layout and demonstrate the capabilities of the generative 3D scene model.
Multi-agent Deep FBSDE Representation For Large Scale Stochastic Differential Games
Authors: Tianrong Chen, Ziyi Wang, Ioannis Exarchos, Evangelos Theodorou
Date: 2020-09-28 00:00:00
Tasks: Autonomous Racing, Decision Making

   In this paper we present a deep learning framework for solving large-scale multi-agent non-cooperative stochastic games using fictitious play. The Hamilton-Jacobi-Bellman (HJB)  PDE associated with each agent is reformulated into a set of Forward-Backward Stochastic Differential Equations (FBSDEs) and solved via forward sampling on a suitably defined neural network architecture. Decision-making in multi-agent systems suffers from the curse of dimensionality and strategy degeneration as the number of agents and time horizon increase. We propose a novel Deep FBSDE controller framework which is shown to outperform the current state-of-the-art deep fictitious play algorithm on a high dimensional inter-bank lending/borrowing problem. More importantly, our approach mitigates the curse of many agents and reduces computational and memory complexity, allowing us to scale up to 1,000 agents in simulation, a scale which, to the best of our knowledge, represents a new state of the art.  Finally, we showcase the framework's applicability in robotics on a belief-space autonomous racing problem.