nils-herrmann/clean_scirepeval huggingface dataset:
Learning Representation from Neural Fisher Kernel with Low-rank Approximation
Authors: Ruixiang ZHANG, Shuangfei Zhai, Etai Littwin, Joshua M. Susskind

In this paper, we study the representation of neural networks from the view of kernels. We first define the Neural Fisher Kernel (NFK), which is the Fisher Kernel applied to neural networks. We show that NFK can be computed for both supervised and unsupervised learning models, which can serve as a unified tool for representation extraction. Furthermore, we show that practical NFKs exhibit low-rank structures. We then propose an efficient algorithm that computes a low-rank approximation of NFK, which scales to large datasets and networks. We show that the low-rank approximation of NFKs derived from unsupervised generative models and supervised learning models gives rise to high-quality compact representations of data, achieving competitive results on a variety of machine learning tasks.
SRDGAN: learning the noise prior for Super Resolution with Dual Generative Adversarial Networks
Authors: Jingwei GUAN, Cheng PAN, Songnan LI and Dahai YU

Single Image Super Resolution (SISR) is the task of producing a high resolution (HR) image from a given low-resolution (LR) image. It is a well researched prob- lem with extensive commercial applications like digital camera, video compres- sion, medical imaging, etc. Most recent super resolution works focus on the fea- ture learning architecture, like Chao Dong (2016); Dong et al. (2016); Wang et al. (2018b); Ledig et al. (2017). However, these works suffer from the following chal- lenges: (1) The low-resolution (LR) training images are artificially synthesized us- ing HR images with bicubic downsampling, which have much more information than real demosaic-upscaled images. The mismatch between training and realistic mobile data heavily blocks the effect on practical SR problem. (2) These methods cannot effectively handle the blind distortions during super resolution in practical applications. In this work, an end-to-end novel framework, including high-to-low network and low-to-high network, is proposed to solve the above problems with dual Generative Adversarial Networks (GAN). First, the above mismatch prob- lems are well explored with the high-to-low network, where clear high-resolution image and the corresponding realistic low-resolution image pairs can be gener- ated. With high-to-low network, a large-scale General Mobile Super Resolution Dataset, GMSR, is proposed, which can be utilized for training or as a bench- mark for super resolution methods. Second, an effective low-to-high network (super resolution network) is proposed in the framework. Benefiting from the GMSR dataset and novel training strategies, the proposed super resolution model can effectively handle detail recovery and denoising at the same time.
Generating Realistic Stock Market Order Streams
Authors: Junyi Li, Xintong Wang, Yaoyang Lin, Arunesh Sinha, Michael P. Wellman

We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks.
We model the order stream as a stochastic process with finite history dependence, and employ a conditional Wasserstein GAN to capture history dependence of orders in a stock market. 
We test our approach with actual market and synthetic data on a number of different statistics, and find the generated data to be close to real data.
Spectral Normalization for Generative Adversarial Networks
Authors: Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida

One of the challenges in the study of generative adversarial networks is the instability of its training. 
In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator.
Our new normalization technique is computationally light and easy to incorporate into existing implementations. 
We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques.
Geomstats: a Python Package for Riemannian Geometry in Machine Learning
Authors: Nina Miolane, Johan Mathe, Claire Donnat, Mikael Jorda, Xavier Pennec

We introduce geomstats, a Python package for Riemannian modelization and optimization over manifolds such as hyperspheres, hyperbolic spaces, SPD matrices or Lie groups of transformations. Our contribution is threefold. First, geomstats allows the flexible modeling of many a machine learning problem through an efficient and extensively unit-tested implementations of these manifolds, as well as the set of useful Riemannian metrics, exponential and logarithm maps that we provide. Moreover, the wide choice of loss functions and our implementation of the corresponding gradients allow fast and easy optimization over manifolds. Finally, geomstats is the only package to provide a unified framework for Riemannian geometry, as the operations implemented in geomstats are available with different computing backends (numpy,tensorflow and keras), as well as with a GPU-enabled mode–-thus considerably facilitating the application of Riemannian geometry in machine learning. In this paper, we present geomstats through a review of the utility and advantages of manifolds in machine learning, using the concrete examples that they span to show the efficiency and practicality of their implementation using our package
Learning Embeddings into Entropic Wasserstein Spaces
Authors: Charlie Frogner, Farzaneh Mirzazadeh, Justin Solomon

Despite their prevalence, Euclidean embeddings of data are fundamentally limited in their ability to capture latent semantic structures, which need not conform to Euclidean spatial assumptions. Here we consider an alternative, which embeds data as discrete probability distributions in a Wasserstein space, endowed with an optimal transport metric. Wasserstein spaces are much larger and more flexible than Euclidean spaces, in that they can successfully embed a wider variety of metric structures. We propose to exploit this flexibility by learning an embedding that captures the semantic information in the Wasserstein distance between embedded distributions. We examine empirically the representational capacity of such learned Wasserstein embeddings, showing that they can embed a wide variety of complex metric structures with smaller distortion than an equivalent Euclidean embedding. We also investigate an application to word embedding, demonstrating a unique advantage of Wasserstein embeddings: we can directly visualize the high-dimensional embedding, as it is a probability distribution on a low-dimensional space. This obviates the need for dimensionality reduction techniques such as t-SNE for visualization.
The Logical Options Framework
Authors: Brandon Araki, Xiao Li, Kiran Vodrahalli, Jonathan DeCastro, J Micah Fry, Daniela Rus

Learning composable policies for environments with complex rules and tasks is a challenging problem. We introduce a hierarchical reinforcement learning framework called the Logical Options Framework (LOF) that learns policies that are satisfying, optimal, and composable. LOF efficiently learns policies that satisfy tasks by representing the task as an automaton and integrating it into learning and planning. We provide and prove conditions under which LOF will learn satisfying, optimal policies. And lastly, we show how LOF's learned policies can be composed to satisfy unseen tasks with only 10-50 retraining steps. We evaluate LOF on four tasks in discrete and continuous domains.
REFINING MONTE CARLO TREE SEARCH AGENTS BY MONTE CARLO TREE SEARCH
Authors: Katsuki Ohto

Reinforcement learning methods that continuously learn neural networks by episode generation with game tree search have been successful in two-person complete information deterministic games such as chess, shogi, and Go. However, there are only reports of practical cases and there are little evidence to guarantee the stability and the final performance of learning process. In this research, the coordination of episode generation was focused on. By means of regarding the entire system as game tree search, the new method can handle the trade-off between exploitation and exploration during episode generation. The experiments with a small problem showed that it had robust performance compared to the existing method,
Variational Auto-Encoder Architectures that Excel at Causal Inference
Authors: Negar Hassanpour, Russell Greiner

This paper provides a generative approach for causal inference using data from observational studies. Inspired by the work of Kingma et al. (2014), we propose a sequence of three architectures (namely Series, Parallel, and Hybrid) that each incorporate their M1 and M2 models as building blocks. Each architecture is an improvement over the previous one in terms of estimating causal effect, culminating in the Hybrid model. The Hybrid model is designed to encourage decomposing the underlying factors of any observational dataset; this in turn, helps to accurately estimate all treatment outcomes. Our empirical results demonstrate the superiority of all three proposed architectures compared to both state-of-the-art discriminative as well as other generative approaches in the literature.
Certifying Distributional Robustness using Lipschitz Regularisation
Authors: Zac Cranko, Zhan Shi, Xinhua Zhang, Simon Kornblith, Richard Nock

Distributional robust risk (DRR) minimisation has arisen as a flexible and effective framework for machine learning. Approximate solutions based on dualisation have become particularly favorable in addressing the semi-infinite optimisation, and they also provide a certificate of the robustness for the worst-case population loss. However existing methods are restricted to either linear models or very small perturbations, and cannot find the globally optimal solution for restricted nonlinear models such as kernel methods. In this paper we resolved these limitations by upper bounding DRRs with an empirical risk regularised by the Lipschitz constant of the model, including deep neural networks and kernel methods. As an application, we showed that it also provides a certificate for adversarial training, and global solutions can be achieved on product kernel machines in polynomial time.
Improving Sequence Generative Adversarial Networks with Feature Statistics Alignment
Authors: Yekun Chai, Qiyue Yin, Junge Zhang

Generative Adversarial Networks (GAN) are facing great challenges in synthesizing sequences of discrete elements, such as mode dropping and unstable training. The binary classifier in the discriminator may limit the capacity of learning signals and thus hinder the advance of adversarial training. To address such issues, apart from the binary classification feedback, we harness a Feature Statistics Alignment (FSA) paradigm to deliver fine-grained signals in the latent high-dimensional representation space. Specifically, FSA forces the mean statistics of the fake data distribution to approach that of real data as close as possible in a finite-dimensional feature space. Experiments on synthetic and real benchmark datasets show the superior performance in quantitative evaluation and demonstrate the effectiveness of our approach to discrete sequence generation. To the best of our knowledge, the proposed architecture is the first that employs feature alignment regularization in the Gumbel-Softmax based GAN framework for sequence generation.
The Laplacian in RL: Learning Representations with Efficient Approximations
Authors: Yifan Wu, George Tucker, Ofir Nachum

The smallest eigenvectors of the graph Laplacian are well-known to provide a succinct representation of the geometry of a weighted graph. In reinforcement learning (RL), where the weighted graph may be interpreted as the state transition process induced by a behavior policy acting on the environment, approximating the eigenvectors of the Laplacian provides a promising approach to state representation learning. However, existing methods for performing this approximation are ill-suited in general RL settings for two main reasons:  First, they are computationally expensive, often requiring operations on large matrices. Second, these methods lack adequate justification beyond simple, tabular, finite-state settings. In this paper, we present a fully general and scalable method for approximating the eigenvectors of the Laplacian in a model-free RL context. We systematically evaluate our approach and empirically show that it generalizes beyond the tabular, finite-state setting. Even in tabular, finite-state settings, its ability to approximate the eigenvectors outperforms previous proposals. Finally, we show the potential benefits of using a Laplacian representation learned using our method in goal-achieving RL tasks, providing evidence that our technique can be used to significantly improve the performance of an RL agent.
Hidden Incentives for Auto-Induced Distributional Shift
Authors: David Krueger, Tegan Maharaj, Jan Leike

Decisions made by machine learning systems have increasing influence on the world, yet it is common for machine learning algorithms to assume that no such influence exists.  An example is the use of the i.i.d. assumption in content recommendation.  In fact, the (choice of) content displayed can change users' perceptions and preferences, or even drive them away, causing a shift in the distribution of users.  We introduce the term auto-induced distributional shift (ADS) to describe the phenomenon of an algorithm causing a change in the distribution of its own inputs.  Our goal is to ensure that machine learning systems do not leverage ADS to increase performance when doing so could be undesirable.  We demonstrate that changes to the learning algorithm, such as the introduction of meta-learning, can cause hidden incentives for auto-induced distributional shift (HI-ADS) to be revealed.  To address this issue, we introduce `unit tests' and a mitigation strategy for HI-ADS, as well as a toy environment for modelling real-world issues with HI-ADS in content recommendation, where we demonstrate that strong meta-learners achieve gains in performance via ADS.  We show meta-learning and Q-learning both sometimes fail unit tests, but pass when using our mitigation strategy.
CHEMICAL NAMES STANDARDIZATION USING NEURAL SEQUENCE TO SEQUENCE MODEL
Authors: Junlang Zhan, Hai Zhao

Chemical information extraction is to convert chemical knowledge in text into true chemical database, which is a text processing task heavily relying on chemical compound name identification and standardization. Once a systematic name for a chemical compound is given, it will naturally and much simply convert the name into the eventually required molecular formula. However, for many chemical substances, they have been shown in many other names besides their systematic names which poses a great challenge for this task. In this paper, we propose a framework to do the auto standardization from the non-systematic names to the corresponding systematic names by using the spelling error correction, byte pair encoding tokenization and neural sequence to sequence model. Our framework is trained end to end and is fully data-driven. Our standardization accuracy on the test dataset achieves 54.04% which has a great improvement compared to previous state-of-the-art result.
Evaluating the Robustness of Time Series Anomaly and Intrusion Detection Methods against Adversarial Attacks
Authors: Shahroz Tariq, Simon S. Woo

Time series anomaly and intrusion detection are extensively studied in statistics, economics, and computer science. Over the years, numerous methods have been proposed for time series anomaly and intrusion detection using deep learning-based methods. Many of these methods demonstrate state-of-the-art performance on benchmark datasets, giving the false impression that these systems are robust and deployable in practical and industrial scenarios. In this paper, we demonstrate that state-of-the-art anomaly and intrusion detection methods can be easily fooled by adding adversarial perturbations to the sensor data. We use different scoring metrics such as prediction errors, anomaly, and classification scores over several public and private datasets belong to aerospace applications, automobiles, server machines, and cyber-physical systems. We evaluate state-of-the-art deep neural networks (DNNs) and graph neural networks (GNNs) methods, which claim to be robust against anomalies and intrusions, and find their performance can drop to as low as 0\% under adversarial attacks from Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) methods.  To the best of our knowledge, we are the first to demonstrate the vulnerabilities of anomaly and intrusion detection systems against adversarial attacks. Our code is available here:
Sparse-Complementary Convolution for Efficient Model Utilization on CNNs
Authors: Chun-Fu (Richard) Chen, Jinwook Oh, Quanfu Fan, Marco Pistoia, Gwo Giun (Chris) Lee

We introduce an efficient way to increase the accuracy of convolution neural networks (CNNs) based on high model utilization without increasing any computational complexity.
The proposed sparse-complementary convolution replaces regular convolution with sparse and complementary shapes of kernels, covering the same receptive field. 
By the nature of deep learning, high model utilization of a CNN can be achieved with more simpler kernels rather than fewer complex kernels.
This simple but insightful model reuses of recent network architectures, ResNet and DenseNet, can provide better accuracy for most classification tasks (CIFAR-10/100 and ImageNet) compared to their baseline models. By simply replacing the convolution of a CNN with our sparse-complementary convolution, at the same FLOPs and parameters, we can improve top-1 accuracy on ImageNet by 0.33% and 0.18% for ResNet-101 and ResNet-152, respectively. A similar accuracy improvement could be gained by increasing the number of layers in those networks by ~1.5x.
Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints
Authors: Mengtian Li, Ersin Yumer, Deva Ramanan

In most practical settings and theoretical analyses, one assumes that a model can be trained until convergence. However, the growing complexity of machine learning datasets and models may violate such assumptions. Indeed, current approaches for hyper-parameter tuning and neural architecture search tend to be limited by practical resource constraints. Therefore, we introduce a formal setting for studying training under the non-asymptotic, resource-constrained regime, i.e., budgeted training. We analyze the following problem: "given a dataset, algorithm, and fixed resource budget, what is the best achievable performance?" We focus on the number of optimization iterations as the representative resource. Under such a setting, we show that it is critical to adjust the learning rate schedule according to the given budget. Among budget-aware learning schedules, we find simple linear decay to be both robust and high-performing. We support our claim through extensive experiments with state-of-the-art models on ImageNet (image classification), Kinetics (video classification), MS COCO (object detection and instance segmentation), and Cityscapes (semantic segmentation). We also analyze our results and find that the key to a good schedule is budgeted convergence, a phenomenon whereby the gradient vanishes at the end of each allowed budget. We also revisit existing approaches for fast convergence and show that budget-aware learning schedules readily outperform such approaches under (the practical but under-explored) budgeted training setting.
Differential-Critic GAN: Generating What You Want by a Cue of Preferences
Authors: Yinghua Yao, Yuangang Pan, Ivor Tsang, Xin Yao

This paper proposes Differential-Critic Generative Adversarial Network (DiCGAN) to learn the distribution of user-desired data when only partial instead of the entire dataset possesses the desired properties. Existing approaches select the desired samples first and train regular GANs on the selected samples to derive the user-desired data distribution. DiCGAN introduces a differential critic that can learn the preference direction from the pairwise preferences over the entire dataset. The resultant critic would guide the generation of the desired data instead of the whole data. Specifically, apart from the Wasserstein GAN loss, a ranking loss of the pairwise preferences is defined over the critic. It endows the difference of critic values between each pair of samples with the pairwise preference relation. The higher critic value indicates that the sample is preferred by the user. Thus training the generative model for higher critic values would encourage generating the user-preferred samples. Extensive experiments show that our DiCGAN can learn the user-desired data distributions.
Poisoned classifiers are not only backdoored, they are fundamentally broken
Authors: Mingjie Sun, Siddhant Agarwal, J Zico Kolter

Under a commonly-studied backdoor poisoning attack against classification models, an attacker adds a small trigger to a subset of the training data, such that the presence of this trigger at test time causes the classifier to always predict some target class. It is often implicitly assumed that the poisoned classifier is vulnerable exclusively to the adversary who possesses the trigger. In this paper, we show empirically that this view of backdoored classifiers is incorrect. We describe a new threat model for poisoned classifier, where one without knowledge of the original trigger, would want to control the poisoned classifier. Under this threat model, we propose a test-time, human-in-the-loop attack method to generate multiple effective alternative triggers without access to the initial backdoor and the training data. We construct these alternative triggers by first generating adversarial examples for a smoothed version of the classifier, created with a procedure called Denoised Smoothing, and then extracting colors or cropped portions of smoothed adversarial images with human interaction. We demonstrate the effectiveness of our attack through extensive experiments on high-resolution datasets: ImageNet and TrojAI. We also compare our approach to previous work on modeling trigger distributions and find that our method are more scalable and efficient in generating effective triggers. Last, we include a user study which demonstrates that our method allows users to easily determine the existence of such backdoors in existing poisoned classifiers. Thus, we argue that there is no such thing as a secret backdoor in poisoned classifiers: poisoning a classifier invites attacks not just by the party that possesses the trigger, but from anyone with access to the classifier.