nils-herrmann/clean_scirepeval huggingface dataset:
Robustness and Equivariance of Neural Networks
Authors: Amit Deshpande, Sandesh Kamath, K.V.Subrahmanyam

Neural networks models are known to be vulnerable to geometric transformations
as well as small pixel-wise perturbations of input. Convolutional Neural Networks
(CNNs) are translation-equivariant but can be easily fooled using rotations and
small pixel-wise perturbations. Moreover, CNNs require sufficient translations in
their training data to achieve translation-invariance. Recent work by Cohen &
Welling (2016), Worrall et al. (2016), Kondor & Trivedi (2018), Cohen & Welling
(2017), Marcos et al. (2017), and Esteves et al. (2018) has gone beyond translations,
and constructed rotation-equivariant or more general group-equivariant
neural network models. In this paper, we do an extensive empirical study of various
rotation-equivariant neural network models to understand how effectively they
learn rotations. This includes Group-equivariant Convolutional Networks (GCNNs)
by Cohen & Welling (2016), Harmonic Networks (H-Nets) by Worrall et al.
(2016), Polar Transformer Networks (PTN) by Esteves et al. (2018) and 
equivariant vector field networks by Marcos et al. (2017). We empirically compare
the ability of these networks to learn rotations efficiently in terms of their
number of parameters, sample complexity, rotation augmentation used in training.
We compare them against each other as well as Standard CNNs. We observe
that as these rotation-equivariant neural networks learn rotations, they instead become
more vulnerable to small pixel-wise adversarial attacks, e.g., Fast Gradient
Sign Method (FGSM) and Projected Gradient Descent (PGD), in comparison with
Standard CNNs. In other words, robustness to geometric transformations in these
models comes at the cost of robustness to small pixel-wise perturbations.
Accuracy-Privacy Trade-off in Deep Ensemble: A Membership Inference Perspective
Authors: Shahbaz Rezaei, Zubair Shafiq, Xin Liu

Deep ensemble learning has been shown to improve accuracy by training multiple neural networks and fusing their outputs. Ensemble learning has also been used to defend against membership inference attacks that undermine privacy. In this paper, we empirically demonstrate a trade-off between these two goals, namely accuracy and privacy (in terms of membership inference attacks), in deep ensembles. Using a wide range of datasets and model architectures, we show that the effectiveness of membership inference attacks also increases when ensembling improves accuracy. To better understand this trade-off, we study the impact of various factors such as prediction confidence and agreement between models that constitute the ensemble. Finally, we evaluate defenses against membership inference attacks based on regularization and differential privacy. We show that while these defenses can mitigate the effectiveness of the membership inference attack, they simultaneously degrade ensemble accuracy. We illustrate similar trade-off in more advanced and state-of-the-art ensembling techniques, such as snapshot ensembles and diversified ensemble networks. The source code is available in supplementary materials.
Universality, Robustness, and Detectability of Adversarial Perturbations under Adversarial Training
Authors: Jan Hendrik Metzen

Classifiers such as deep neural networks have been shown to be vulnerable against adversarial perturbations on problems with high-dimensional input space. While adversarial training improves the robustness of classifiers against such adversarial perturbations, it leaves classifiers sensitive to them on a non-negligible fraction of the inputs. We argue that there are two different kinds of adversarial perturbations: shared perturbations which fool a classifier on many inputs and singular perturbations which only fool the classifier on a small fraction of the data. We find that adversarial training increases the robustness of classifiers against shared perturbations. Moreover, it is particularly effective in removing universal perturbations, which can be seen as an extreme form of shared perturbations. Unfortunately, adversarial training does not consistently increase the robustness against singular perturbations on unseen inputs. However, we find that adversarial training decreases robustness of the remaining perturbations against image transformations such as changes to contrast and brightness or  Gaussian blurring. It thus makes successful attacks on the classifier in the physical world less likely. Finally, we show that even singular perturbations can be easily detected and must thus exhibit generalizable patterns even though the perturbations are specific for certain inputs.
Code Synthesis with Priority Queue Training
Authors: Daniel A. Abolafia, Quoc V. Le, Mohammad Norouzi

We consider the task of program synthesis in the presence of a reward function over the output of programs, where the goal is to find programs with maximal rewards. We introduce a novel iterative optimization scheme, where we train an RNN on a dataset of K best programs from a priority queue of the generated programs so far. Then, we synthesize new programs and add them to the priority queue by sampling from the RNN. We benchmark our algorithm called priority queue training (PQT) against genetic algorithm and reinforcement learning baselines on a simple but expressive Turing complete programming language called BF. Our experimental results show that our deceptively simple PQT algorithm significantly outperforms the baselines. By adding a program length penalty to the reward function, we are able to synthesize short, human readable programs.
On the Generalization Effects of DenseNet Model Structures 
Authors: Yin Liu, Vincent Chen

Modern neural network architectures take advantage of increasingly deeper layers, and various advances in their structure to achieve better performance. While traditional explicit regularization techniques like dropout, weight decay, and data augmentation are still being used in these new models, little about the regularization and generalization effects of these new structures have been studied. 
Besides being deeper than their predecessors, could newer architectures like ResNet and DenseNet also benefit from their structures' implicit regularization properties? 
In this work, we investigate the skip connection's effect on network's generalization features. Through experiments, we show that certain neural network architectures contribute to their generalization abilities. Specifically, we study the effect that low-level features have on generalization performance when they are introduced to deeper layers in DenseNet, ResNet as well as networks with 'skip connections'. We show that these low-level representations do help with generalization in multiple settings when both the quality and quantity of training data is decreased.
Stochastic Learning of Additive Second-Order Penalties with  Applications to Fairness
Authors: Heinrich Jiang, Yifan Wu, Ofir Nachum

Many notions of fairness may be expressed as linear constraints, and the resulting constrained objective is often optimized by transforming the problem into its Lagrangian dual with additive linear penalties. In non-convex settings, the resulting problem may be difficult to solve as the Lagrangian is not guaranteed to have a deterministic saddle-point equilibrium.  In this paper, we propose to modify the linear penalties to second-order ones, and we argue that this results in a more practical training procedure in non-convex, large-data settings. For one, the use of second-order penalties allows training the penalized objective with a fixed value of the penalty coefficient, thus avoiding the instability and potential lack of convergence associated with two-player min-max games. Secondly, we derive a method for efficiently computing the gradients associated with the second-order penalties in stochastic mini-batch settings. Our resulting algorithm performs well empirically, learning an appropriately fair classifier on a number of standard benchmarks.
Iterative Graph Self-Distillation
Authors: Hanlin Zhang, Shuai Lin, Weiyang Liu, Pan Zhou, Jian Tang, Xiaodan Liang, Eric Xing

How to discriminatively vectorize graphs is a fundamental challenge that attracts increasing attentions in recent years. Motivated by the recent success of unsupervised contrastive learning, we aim to learn graph-level representation in an unsupervised manner. Specifically, we propose a novel unsupervised graph learning paradigm called Iterative Graph Self-Distillation (IGSD) which iteratively performs the teacher-student distillation with graph augmentations. Different from conventional knowledge distillation, IGSD constructs the teacher with an exponential moving average of the student model and distills the knowledge of itself. The intuition behind IGSD is to predict the teacher network representation of the graph pairs under different augmented views. As a natural extension, we also apply IGSD to semi-supervised scenarios by jointly regularizing the network with both supervised and unsupervised contrastive loss. Finally, we show that finetuning the IGSD-trained models with self-training can further improve the graph representation power. Empirically, we achieve significant and consistent performance gain on various graph datasets in both unsupervised and semi-supervised settings, which well validates the superiority of IGSD.
Prototypical Representation Learning for Relation Extraction
Authors: Ning Ding, Xiaobin Wang, Yao Fu, Guangwei Xu, Rui Wang, Pengjun Xie, Ying Shen, Fei Huang, Hai-Tao Zheng, Rui Zhang

Recognizing relations between entities is a pivotal task of relational learning.  
Learning relation representations from distantly-labeled datasets is difficult because of the abundant label noise and complicated expressions in human language.  
This paper aims to learn predictive, interpretable, and robust relation representations from distantly-labeled data that are effective in different settings, including supervised, distantly supervised, and few-shot learning. 
Instead of solely relying on the supervision from noisy labels, we propose to learn prototypes for each relation from contextual information to best explore the intrinsic semantics of relations. 
Prototypes are representations in the feature space abstracting the essential semantics of relations between entities in sentences.
We learn prototypes based on objectives with clear geometric interpretation, where the prototypes are unit vectors uniformly dispersed in a unit ball, and statement embeddings are centered at the end of their corresponding prototype vectors on the surface of the ball. 
This approach allows us to learn meaningful, interpretable prototypes for the final classification.
Results on several relation learning tasks show that our model significantly outperforms the previous state-of-the-art models.
We further demonstrate the robustness of the encoder and the interpretability of prototypes with extensive experiments.
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks
Authors: Anne Harrington, Arturo Deza

Recent work suggests that feature constraints in the training datasets of deep neural networks (DNNs) drive robustness to adversarial noise (Ilyas et al., 2019). The representations learned by such adversarially robust networks have also been shown to be more human perceptually-aligned than non-robust networks via image manipulations (Santurkar et al., 2019, Engstrom et al., 2019). Despite appearing closer to human visual perception, it is unclear if the constraints in robust DNN representations match biological constraints found in human vision. Human vision seems to rely on texture-based/summary statistic representations in the periphery, which have been shown to explain phenomena such as crowding (Balas et al., 2009) and performance on visual search tasks (Rosenholtz et al., 2012). To understand how adversarially robust optimizations/representations compare to human vision, we performed a psychophysics experiment using a metamer task similar to Freeman \& Simoncelli, 2011, Wallis et al., 2016 and Deza et al., 2019 where we evaluated how well human observers could distinguish between images synthesized to match adversarially robust representations compared to non-robust representations and a texture synthesis model of peripheral vision (Texforms a la Long et al., 2018).  We found that the discriminability of robust representation and texture model images decreased to near chance performance as stimuli were presented farther in the periphery.  Moreover, performance on robust and texture-model images showed similar trends within participants, while performance on non-robust representations changed minimally across the visual field.  These results together suggest that (1) adversarially robust representations capture peripheral computation better than non-robust representations and (2) robust representations capture peripheral computation similar to current state-of-the-art texture peripheral vision models. More broadly, our findings support the idea that localized texture summary statistic representations may drive human invariance to adversarial perturbations and that the incorporation of such representations in DNNs could give rise to useful properties like adversarial robustness.
Time-Agnostic Prediction: Predicting Predictable Video Frames
Authors: Dinesh Jayaraman, Frederik Ebert, Alexei Efros, Sergey Levine

Prediction is arguably one of the most basic functions of an intelligent system. In general, the problem of predicting events in the future or between two waypoints is exceedingly difficult. However, most phenomena naturally pass through relatively predictable bottlenecks---while we cannot predict the precise trajectory of a robot arm between being at rest and holding an object up, we can be certain that it must have picked the object up. To exploit this, we decouple visual prediction from a rigid notion of time. While conventional approaches predict frames at regularly spaced temporal intervals, our time-agnostic predictors (TAP) are not tied to specific times so that they may instead discover predictable "bottleneck" frames no matter when they occur. We evaluate our approach for future and intermediate frame prediction across three robotic manipulation tasks. Our predictions are not only of higher visual quality, but also correspond to coherent semantic subgoals in temporally extended tasks.
Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations
Authors: Anuroop Sriram, Abhishek Das, Brandon M Wood, Siddharth Goyal, C. Lawrence Zitnick

Recent progress in Graph Neural Networks (GNNs) for modeling atomic simulations has the potential to revolutionize catalyst discovery, which is a key step in making progress towards the energy breakthroughs needed to combat climate change. However, the GNNs that have proven most effective for this task are memory intensive as they model higher-order interactions in the graphs such as those between triplets or quadruplets of atoms, making it challenging to scale these models. In this paper, we introduce Graph Parallelism, a method to distribute input graphs across multiple GPUs, enabling us to train very large GNNs with hundreds of millions or billions of parameters. We empirically evaluate our method by scaling up the recently proposed DimeNet++ and GemNet models by over an order of magnitude in the number of parameters. On the large-scale Open Catalyst 2020 (OC20) dataset, these graph-parallelized models lead to relative improvements of 1) 15% on the force MAE metric on the S2EF task and 2) 21% on the AFbT metric on the IS2RS task, establishing new state-of-the-art results.
COCO-GAN: Conditional Coordinate Generative Adversarial Network
Authors: Chieh Hubert Lin, Chia-Che Chang, Yu-Sheng Chen, Da-Cheng Juan, Wei Wei, Hwann-Tzong Chen

Recent advancements on Generative Adversarial Network (GAN) have inspired a wide range of works that generate synthetic images. However, the current processes have to generate an entire image at once, and therefore resolutions are limited by memory or computational constraints. In this work, we propose COnditional COordinate GAN (COCO-GAN), which generates a specific patch of an image conditioned on a spatial position rather than the entire image at a time. The generated patches are later combined together to form a globally coherent full-image. With this process, we show that the generated image can achieve competitive quality to state-of-the-arts and the generated patches are locally smooth between consecutive neighbors. One direct implication of the COCO-GAN is that it can be applied onto any coordinate systems including the cylindrical systems which makes it feasible for generating panorama images. The fact that the patch generation process is independent to each other inspires a wide range of new applications: firstly, "Patch-Inspired Image Generation" enables us to generate the entire image based on a single patch. Secondly, "Partial-Scene Generation" allows us to generate images within a customized target region. Finally, thanks to COCO-GAN's patch generation and massive parallelism, which enables combining patches for generating a full-image with higher resolution than state-of-the-arts.
NEWSQA: A MACHINE COMPREHENSION DATASET
Authors: Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, Kaheer Suleman

We present NewsQA, a challenging machine comprehension dataset of over 100,000 question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting in spans of text from the corresponding articles. We collect this dataset through a four- stage process designed to solicit exploratory questions that require reasoning. A thorough analysis confirms that NewsQA demands abilities beyond simple word matching and recognizing entailment. We measure human performance on the dataset and compare it to several strong neural models. The performance gap between humans and machines (25.3% F1) indicates that significant progress can be made on NewsQA through future research. The dataset is freely available at datasets.maluuba.com/NewsQA.
Iterated learning for emergent systematicity in VQA
Authors: Ankit Vani, Max Schwarzer, Yuchen Lu, Eeshan Dhekane, Aaron Courville

Although neural module networks have an architectural bias towards compositionality, they require gold standard layouts to generalize systematically in practice. When instead learning layouts and modules jointly, compositionality does not arise automatically and an explicit pressure is necessary for the emergence of layouts exhibiting the right structure. We propose to address this problem using iterated learning, a cognitive science theory of the emergence of compositional languages in nature that has primarily been applied to simple referential games in machine learning. Considering the layouts of module networks as samples from an emergent language, we use iterated learning to encourage the development of structure within this language. We show that the resulting layouts support systematic generalization in neural agents solving the more complex task of visual question-answering. Our regularized iterated learning method can outperform baselines without iterated learning on SHAPES-SyGeT (SHAPES Systematic Generalization Test), a new split of the SHAPES dataset we introduce to evaluate systematic generalization, and on CLOSURE, an extension of CLEVR also designed to test systematic generalization. We demonstrate superior performance in recovering ground-truth compositional program structure with limited supervision on both SHAPES-SyGeT and CLEVR.
More or Less: When and How to Build Convolutional Neural Network Ensembles
Authors: Abdul Wasay, Stratos Idreos

Convolutional neural networks are utilized to solve increasingly more complex problems and with more data. As a  result, researchers and practitioners seek to scale the representational power of such models by adding more parameters. However, increasing parameters requires additional critical resources in terms of memory and compute,  leading to increased training and inference cost. Thus a consistent challenge is to obtain as high as possible accuracy within a parameter budget. As neural network designers navigate this complex landscape, they are guided by conventional wisdom that is informed from past empirical studies. We identify a critical part of this design space that is not well-understood: How to decide between the alternatives of expanding a single convolutional network model or increasing the number of networks in the form of an ensemble. We study this question in detail across various network architectures and data sets. We build an extensive experimental framework that captures numerous angles of the possible design space in terms of how a new set of parameters can be used in a model.  We consider a holistic set of metrics such as training time, inference time, and memory usage. The framework provides a robust assessment by making sure it controls for the number of parameters. Contrary to conventional wisdom, we show that when we perform a holistic and robust assessment, we uncover a wide design space, where ensembles provide better accuracy, train faster, and deploy at speed comparable to single convolutional networks with the same total number of parameters.
Whitening for Self-Supervised Representation Learning
Authors: Aleksandr Ermolov, Aliaksandr Siarohin, Enver Sangineto, Nicu Sebe

Most of the self-supervised representation learning methods are based on the contrastive loss and the instance-discrimination task, where augmented versions of the same image instance  ("positives") are contrasted with instances extracted from other images ("negatives"). For the learning to be effective, a lot of negatives should be compared with a positive pair, which is computationally demanding. In this paper, we propose a different direction and a new loss function for self-supervised representation learning which is based on the whitening of the latent-space features. The whitening operation has a "scattering" effect on the batch samples, which compensates the use of negatives, avoiding degenerate solutions where all the sample representations collapse to a single point. Our Whitening MSE (W-MSE) loss does not require special heuristics (e.g. additional networks) and it is conceptually simple. Since negatives are not needed, we can extract multiple positive pairs from the same image instance. We  empirically show that W-MSE is competitive with respect to popular, more complex self-supervised methods. The source code of the method and all the experiments is included in the Supplementary Material.
BayesAdapter: Being Bayesian, Inexpensively and Robustly, via Bayesian Fine-tuning
Authors: Zhijie Deng, Xiao Yang, Hao Zhang, Yinpeng Dong, Jun Zhu

Despite their theoretical appealingness, Bayesian neural networks (BNNs) are falling far behind in terms of adoption in real-world applications compared with normal NNs, mainly due to their limited scalability in training, and low fidelity in their uncertainty estimates. In this work, we develop a new framework, named BayesAdapter, to address these issues and bring Bayesian deep learning to the masses. The core notion of BayesAdapter is to adapt pre-trained deterministic NNs to be BNNs via Bayesian fine-tuning. We implement Bayesian fine-tuning with a plug-and-play instantiation of stochastic variational inference, and propose exemplar reparameterization to reduce gradient variance and stabilize the fine-tuning. Together, they enable training BNNs as if one were training deterministic NNs with minimal added overheads. During Bayesian fine-tuning, we further propose an uncertainty regularization to supervise and calibrate the uncertainty quantification of learned BNNs at low cost. To empirically evaluate BayesAdapter, we conduct extensive experiments on a diverse set of challenging benchmarks, and observe satisfactory training efficiency, competitive predictive performance, and calibrated and faithful uncertainty estimates.