Improving BERT with Self-Supervised Attention

One of the most popular paradigms of applying large, pre-trained NLP models such as BERT is to fine-tune it on a smaller dataset. However, one challenge remains as the fine-tuned model often overfits on smaller datasets. A symptom of this phenomenon is that irrelevant words in the sentences, even when they are obvious to humans, can substantially degrade the performance of these fine-tuned BERT models. In this paper, we propose a novel technique, called Self-Supervised Attention (SSA) to help facilitate this generalization challenge. Specifically, SSA automatically generates weak, token-level attention labels iteratively by "probing" the fine-tuned model from the previous iteration. We investigate two different ways of integrating SSA into BERT and propose a hybrid approach to combine their benefits. Empirically, on a variety of public datasets, we illustrate significant performance improvement using our SSA-enhanced BERT model.

Provable Robust Learning Based on Transformation-Specific Smoothing

As machine learning systems become pervasive, safeguarding their security is critical. Recent work has demonstrated that motivated adversaries could manipulate the test data to mislead ML systems to make arbitrary mistakes. So far, most research has focused on providing provable robustness guarantees for a specific $\ell_p$ norm bounded adversarial perturbation. However, in practice there are more adversarial transformations that are realistic and of semantic meaning, requiring to be analyzed and ideally certified. In this paper we aim to provide a unified framework for certifying ML model robustness against general adversarial transformations. First, we leverage the function smoothing strategy to certify robustness against a series of adversarial transformations such as rotation, translation, Gaussian blur, etc. We then provide sufficient conditions and strategies for certifying certain transformations. For instance, we propose a novel sampling based interpolation approach with the estimated Lipschitz upper bound to certify the robustness against rotation transformation. In addition, we theoretically optimize the smoothing strategies for certifying the robustness of ML models against different transformations. For instance, we show that smoothing by sampling from exponential distribution provides tighter robustness bound than Gaussian. We also prove two generalization gaps for the proposed framework to understand its theoretic barrier. Extensive experiments show that our proposed unified framework significantly outperforms the state-of-the-art certified robustness approaches on several datasets including ImageNet.

RAB: Provable Robustness Against Backdoor Attacks

Recent studies have shown that deep neural networks (DNNs) are vulnerable to various attacks, including evasion attacks and poisoning attacks. On the defense side, there have been intensive interests in provable robustness against evasion attacks. In this paper, we focus on improving model robustness against more diverse threat models. Specifically, we provide the first unified framework using smoothing functional to certify the model robustness against general adversarial attacks. In particular, we propose the first robust training process RAB to certify against backdoor attacks. We theoretically prove the robustness bound for machine learning models based on the RAB training process, analyze the tightness of the robustness bound, as well as proposing different smoothing noise distributions such as Gaussian and Uniform distributions. Moreover, we evaluate the certified robustness of a family of "smoothed" DNNs which are trained in a differentially private fashion. In addition, we theoretically show that for simpler models such as K-nearest neighbor models, it is possible to train the robust smoothed models efficiently. For K=1, we propose an exact algorithm to smooth the training process, eliminating the need to sample from a noise distribution.Empirically, we conduct comprehensive experiments for different machine learning models such as DNNs, differentially private DNNs, and KNN models on MNIST, CIFAR-10 and ImageNet datasets to provide the first benchmark for certified robustness against backdoor attacks. In particular, we also evaluate KNN models on a spambase tabular dataset to demonstrate its advantages. Both the theoretic analysis for certified model robustness against arbitrary backdoors, and the comprehensive benchmark on diverse ML models and datasets would shed light on further robust learning strategies against training time or even general adversarial attacks on ML models.

End-to-end Robustness for Sensing-Reasoning Machine Learning Pipelines

As machine learning (ML) being applied to many mission-critical scenarios, certifying ML model robustness becomes increasingly important. Many previous works focuses on the robustness of independent ML and ensemble models, and can only certify a very small magnitude of the adversarial perturbation. In this paper, we take a different viewpoint and improve learning robustness by going beyond independent ML and ensemble models. We aim at promoting the generic Sensing-Reasoning machine learning pipeline which contains both the sensing (e.g. deep neural networks) and reasoning (e.g. Markov logic networks (MLN)) components enriched with domain knowledge. Can domain knowledge help improve learning robustness? Can we formally certify the end-to-end robustness of such an ML pipeline? We first theoretically analyze the computational complexity of checking the provable robustness in the reasoning component. We then derive the provable robustness bound for several concrete reasoning components. We show that for reasoning components such as MLN and a specific family of Bayesian networks it is possible to certify the robustness of the whole pipeline even with a large magnitude of perturbation which cannot be certified by existing work. Finally, we conduct extensive real-world experiments on large scale datasets to evaluate the certified robustness for Sensing-Reasoning ML pipelines.

A Robust Imbalanced SAR Image Change Detection Approach Based on Deep Difference Image and PCANet

In this research, a novel robust change detection approach is presented for imbalanced multi-temporal synthetic aperture radar (SAR) image based on deep learning. Our main contribution is to develop a novel method for generating difference image and a parallel fuzzy c-means (FCM) clustering method. The main steps of our proposed approach are as follows: 1) Inspired by convolution and pooling in deep learning, a deep difference image (DDI) is obtained based on parameterized pooling leading to better speckle suppression and feature enhancement than traditional difference images. 2) Two different parameter Sigmoid nonlinear mapping are applied to the DDI to get two mapped DDIs. Parallel FCM are utilized on these two mapped DDIs to obtain three types of pseudo-label pixels, namely, changed pixels, unchanged pixels, and intermediate pixels. 3) A PCANet with support vector machine (SVM) are trained to classify intermediate pixels to be changed or unchanged. Three imbalanced multi-temporal SAR image sets are used for change detection experiments. The experimental results demonstrate that the proposed approach is effective and robust for imbalanced SAR data, and achieve up to 99.52% change detection accuracy superior to most state-of-the-art methods.

Two-Phase Object-Based Deep Learning for Multi-temporal SAR Image Change Detection

Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a much negative effect on change detection. In this research, a novel two-phase object-based deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighboring pixels into segmented into superpixel objects (from pixels) such as to exploit local spatial context. Two phases are designed in the methodology: 1) Generate objects based on the simple linear iterative clustering algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. 2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery.

TextNAS: A Neural Architecture Search Space tailored for Text Representation

Learning text representation is crucial for text classification and other language related tasks. There are a diverse set of text representation networks in the literature, and how to find the optimal one is a non-trivial problem. Recently, the emerging Neural Architecture Search (NAS) techniques have demonstrated good potential to solve the problem. Nevertheless, most of the existing works of NAS focus on the search algorithms and pay little attention to the search space. In this paper, we argue that the search space is also an important human prior to the success of NAS in different applications. Thus, we propose a novel search space tailored for text representation. Through automatic search, the discovered network architecture outperforms state-of-the-art models on various public datasets on text classification and natural language inference tasks. Furthermore, some of the design principles found in the automatic network agree well with human intuition.

Data Science through the looking glass and what we found there

The recent success of machine learning (ML) has led to an explosive growth both in terms of new systems and algorithms built in industry and academia, and new applications built by an ever-growing community of data science (DS) practitioners. This quickly shifting panorama of technologies and applications is challenging for builders and practitioners alike to follow. In this paper, we set out to capture this panorama through a wide-angle lens, by performing the largest analysis of DS projects to date, focusing on questions that can help determine investments on either side. Specifically, we download and analyze: (a) over 6M Python notebooks publicly available on GITHUB, (b) over 2M enterprise DS pipelines developed within COMPANYX, and (c) the source code and metadata of over 900 releases from 12 important DS libraries. The analysis we perform ranges from coarse-grained statistical characterizations to analysis of library imports, pipelines, and comparative studies across datasets and time. We report a large number of measurements for our readers to interpret, and dare to draw a few (actionable, yet subjective) conclusions on (a) what systems builders should focus on to better serve practitioners, and (b) what technologies should practitioners bet on given current trends. We plan to automate this analysis and release associated tools and results periodically.