pwc-archive/papers-with-abstracts huggingface dataset:
Policy-Driven Attack: Learning to Query for Hard-label Black-box Adversarial Examples
Authors: Ziang Yan, Yiwen Guo, Jian Liang, ChangShui Zhang
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: image-classification, Image Classification

To craft black-box adversarial examples, adversaries need to query the victim model and take proper advantage of its feedback. Existing black-box attacks generally suffer from high query complexity, especially when only the top-1 decision (i.e., the hard-label prediction) of the victim model is available. In this paper, we propose a novel hard-label black-box attack named Policy-Driven Attack, to reduce the query complexity. Our core idea is to learn promising search directions of the adversarial examples using a well-designed policy network in a novel reinforcement learning formulation, in which the queries become more sensible. Experimental results demonstrate that our method can significantly reduce the query complexity in comparison with existing state-of-the-art hard-label black-box attacks on various image classification benchmark datasets. Anonymous code and models for reproducing our results are available in the supplementary material.
Hierarchical Meta Reinforcement Learning for Multi-Task Environments
Authors: Dongyang Zhao, Yue Huang, Changnan Xiao, Yue Li, Shihong Deng
Date: 2021-01-01 00:00:00
Tasks: Deep Reinforcement Learning, Hierarchical Reinforcement Learning, Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning algorithms aim to achieve human-level intelligence by solving practical decisions-making problems, which are often composed of multiple sub-tasks. Complex and subtle relationships between sub-tasks make traditional methods hard to give a promising solution. We implement a first-person shooting environment with random spatial structures to illustrate a typical representative of this kind. A desirable agent should be capable of balancing between different sub-tasks: navigation to find enemies and shooting to kill them. To address the problem brought by the environment, we propose a Meta Soft Hierarchical reinforcement learning framework (MeSH), in which each low-level sub-policy focuses on a specific sub-task respectively and high-level policy automatically learns to utilize low-level sub-policies through meta-gradients. The proposed framework is able to disentangle multiple sub-tasks and discover proper low-level policies under different situations. The effectiveness and efficiency of the framework are shown by a series of comparison experiments. Both environment and algorithm code will be provided for open source to encourage further research.
EMPIRICAL UPPER BOUND IN OBJECT DETECTION
Authors: Ali Borji
Date: 2021-01-01 00:00:00
Tasks: Instance Segmentation, Object, object-detection, Object Detection, Object Recognition, Object Tracking, Semantic Segmentation

Object detection remains one of the most notorious open problems in computer
vision. Despite large strides in accuracy and speed in recent years, modern object
detectors have started to saturate on popular benchmarks. How far can we push
the detection accuracy with the current deep learning tools and tricks? In this
work, by employing two popular state-of-the-art object detection benchmarks,
MMDetection and Detectron2, and analyzing more than 15 models over 4
large-scale datasets, we systematically determine the upper bound in AP, which is
91.6% on PASCAL VOC (test2007), 78.2% on MS COCO (val2017), and 58.9%
on OpenImages (V4 validation set), regardless of the IOU. These numbers are
much higher than the mAP of the best model (e.g., 58% on MS COCO according
to the most recent results). Interestingly, the gap seems to be almost closed at
IOU=0.5. We also analyze the role of context in object recognition and detection
and find that the canonical object size leads to the best recognition accuracy.
Finally, we carefully characterize the sources of errors in deep object detectors and
find that classification error (confusion with other classes and misses) explains the
largest fraction of errors and weighs more than localization error. Further, models
frequently miss small objects, more often than medium and large ones. Our work
taps into the tight relationship between object recognition and detection and offers
insights to build better object detectors. Similar analyses can also be conducted
for other tasks in computer vision such as for instance segmentation and object
tracking. The code is available at [TBA].
Modal Uncertainty Estimation via Discrete Latent Representations
Authors: Di Qiu, Zhanghan Ke, Peng Su, Lok Ming Lui
Date: 2021-01-01 00:00:00
Tasks: Informativeness

Many important problems in the real world don't have unique solutions. It is thus important for machine learning models to be capable of proposing different plausible solutions with meaningful probability measures.
In this work we propose a novel deep learning based framework, named {\it modal uncertainty estimation}  (MUE), to learn the one-to-many mappings between the inputs and outputs, together with faithful uncertainty estimation.
Motivated by the multi-modal posterior collapse problem in current conditional generative models, MUE uses a set of discrete latent variables, each representing a latent mode hypothesis that explains one type of input-output relationship, to generate the one-to-many mappings. Benefit from the discrete nature of the latent representations, MUE can estimate any input the conditional probability distribution of the outputs effectively. Moreover, MUE is efficient during training since the discrete latent space and its uncertainty estimation are jointly learned.
We also develop the theoretical background of MUE and extensively validate it on both synthetic and realistic tasks. MUE demonstrates (1) significantly more accurate uncertainty estimation than the current state-of-the-art, and (2) its informativeness for practical use.
Apollo: An Adaptive Parameter-wised Diagonal Quasi-Newton Method for Nonconvex Stochastic Optimization
Authors: Xuezhe Ma
Date: 2021-01-01 00:00:00
Tasks: Stochastic Optimization

In this paper, we introduce Apollo, a quasi-newton method for noncovex stochastic optimization, which dynamically incorporates the curvature of the loss function by approximating the Hessian via a diagonal matrix. Algorithmically, Apollo requires only first-order gradients and updates the approximation of the Hessian diagonally such that it satisfies the weak secant relation. To handle nonconvexity, we replace the Hessian with its absolute value, the computation of which is also efficient under our diagonal approximation, yielding an optimization algorithm with linear complexity for both time and memory. Experimentally, through three tasks on vision and language we show that Apollo achieves significant improvements over other stochastic optimization methods, including SGD and variants of Adam, in term of both convergence speed and generalization performance.
Improving Tail Label Prediction for Extreme Multi-label Learning
Authors: Tong Wei, Wei-Wei Tu, Yu-Feng Li
Date: 2021-01-01 00:00:00
Tasks: Multi-Label Learning

Extreme multi-label learning (XML) works to annotate objects with relevant labels from an extremely large label set. Many previous methods treat labels uniformly such that the learned model tends to perform better on head labels, while the performance is severely deteriorated for tail labels. However, it is often desirable to predict more tail labels in many real-world applications. To alleviate this problem, in this work, we show theoretical and experimental evidence for the inferior performance of representative XML methods on tail labels. Our finding is that the norm of label classifier weights typically follows a long-tailed distribution similar to the label frequency, which results in the over-suppression of tail labels. Base on this new finding, we present two new modules: (1)~\algoa~learns to re-rank the predictions by optimizing a population-aware loss, which predicts tail labels with high rank; (2)~\algob~augments tail labels via a decoupled learning scheme, which can yield more balanced classification boundary. We conduct experiments on commonly used XML benchmarks with hundreds of thousands of labels, showing that the proposed methods improve the performance of many state-of-the-art XML models by a considerable margin (6\% performance gain with respect to PSP@1 on average).
GN-Transformer: Fusing AST and Source Code information in Graph Networks
Authors: Junyan Cheng, Iordanis Fostiropoulos, Barry Boehm
Date: 2021-01-01 00:00:00
Tasks: Code Summarization, Source Code Summarization

As opposed to natural languages, source code understanding is influenced by grammar relations between tokens regardless of their identifier name. Considering graph representation of source code such as Abstract Syntax Tree (AST) and Control Flow Graph (CFG), can capture a token’s grammatical relationships that are not obvious from the source code. Most existing methods are late fusion and underperform when supplementing the source code text with a graph representation. We propose a novel method called GN-Transformer to fuse representations learned from graph and text modalities under the Graph Networks (GN) framework with attention mechanism. Our method learns the embedding on a constructed graph called Syntax-Code Graph (SCG). We perform experiments on the structure of SCG, an ablation study on the model design and the hyper-paramaters to conclude that the performance advantage is from the fusion method and not the specific details of the model. The proposed method achieved state of the art performance in two code summarization datasets and across three metrics.
Learn2Weight: Weights Transfer Defense against Similar-domain Adversarial Attacks
Authors: Siddhartha Datta
Date: 2021-01-01 00:00:00
Tasks: Adversarial Attack, Domain Adaptation, Multi-Domain Sentiment Classification, Sentiment Analysis, Sentiment Classification

Recent work in black-box adversarial attacks for NLP systems has attracted attention. Prior black-box attacks assume that attackers can observe output labels from target models based on selected inputs. In this work, inspired by  adversarial transferability, we propose a new type of black-box NLP adversarial attack that an attacker can choose a similar domain and transfer the adversarial examples to the target domain and cause poor performance in target model. Based on domain adaptation theory, we then propose a defensive strategy, called Learn2Weight, which trains to predict the weight adjustments for target model in order to defense the attack of similar-domain adversarial examples. Using Amazon multi-domain sentiment classification dataset, we empirically show that Learn2Weight model is effective against the attack compared to standard black-box defense methods such as adversarial training and defense distillation. This work contributes to the growing literature on machine learning safety.
Necessary and Sufficient Conditions for Compositional Representations
Authors: Yuanpeng Li
Date: 2021-01-01 00:00:00

Humans naturally use compositional representations for flexible recognition and expression, but current machine learning lacks such ability. Despite many efforts in specific cases, there is still absence of theories and tools to study it systematically. In this paper, we leverage group theory to mathematically prove necessary and sufficient conditions for two fundamental questions of compositional representations. (1) What are the properties for a set of components to be expressed compositionally. (2) What are the properties for mappings between compositional and original representations. We provide examples to better understand the borders of the conditions. We also use the theory to provide a new explanation of why attention mechanism helps compositionality. We hope this work will help to advance understanding of compositionality and improvement of artificial intelligence towards human level.
A Numbers Game: Numeric Encoding Options with Automunge
Authors: Nicholas Teague
Date: 2021-01-01 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering

Mainstream practice in machine learning with tabular data may take for granted that any feature engineering beyond scaling for numeric sets is superfluous in context of deep neural networks. This paper will offer arguments for potential benefits of extended encodings of numeric streams in deep learning by way of a survey of options for numeric transformations as available in the Automunge open source python library platform for tabular data pipelines, where transformations may be applied to distinct columns in “family tree” sets with generations and branches of derivations. Automunge transformation options include normalization, binning, noise injection, derivatives, and more. The aggregation of these methods into family tree sets of transformations are demonstrated for use to present numeric features to machine learning in multiple configurations of varying information content, as may be applied to encode numeric sets of unknown interpretation.
Revisiting Point Cloud Classification with a Simple and Effective Baseline
Authors: Ankit Goyal, Hei Law, Bowei Liu, Alejandro Newell, Jia Deng
Date: 2021-01-01 00:00:00
Tasks: 3D Point Cloud Classification, Classification, General Classification, Point Cloud Classification

Processing point cloud data is an important component of many real-world systems. As such, a wide variety of point-based approaches have been proposed, reporting steady benchmark improvements over time. We study the key ingredients of this progress and uncover two critical results. First, we find that auxiliary factors like different evaluation schemes, data augmentation strategies, and loss functions, which are independent of the model architecture, make a large difference in performance. The differences are large enough that they obscure the effect of architecture. When these factors are controlled for, PointNet++, a relatively older network, performs competitively with recent methods. Second, a very simple projection-based method, which we refer to as SimpleView, performs surprisingly well. It achieves on par or better results than sophisticated state-of-the-art methods on ModelNet40, while being half the size of PointNet++. It also outperforms state-of-the-art methods on ScanObjectNN, a real-world point cloud benchmark, and demonstrates better cross-dataset generalization.
Unified analytic forms for Convolutional Neural Networks and Wavelet Filter Banks
Authors: Abdourrahmane M ATTO
Date: 2021-01-01 00:00:00

This paper provides a unified analytic framework integrating expressions of several variants of convolutional neural networks and wavelet filter banks. The expressions are derived recursively, from dowstream to upstream layers, for the sequences of features returned at the nodes of a general form of network architecture. The inspiring framework for the derivation of these expressions is that of the so-called M-band convolution filter banks. In addition with the inter-layer inter-node expressions, activation sequences of convolutional neural networks  also are mathematically described by suitable algebraic path representations.
Towards Generalized Artificial Intelligence by Assessment Aggregation with Applications to Standard and Extreme Classifications
Authors: Abdourrahmane M ATTO
Date: 2021-01-01 00:00:00

The paper studies the impacts of an aggregative Convolutional Neural Network (CNN) for the derivation of generalized artificial intelligence when starting from a specialized set of CNNs. The aggregation proposed relies on machine learning from specific examples being the categorical probabilities fed by the downstream specialized CNNs under consideration. Aggregation proof of concepts  are provided in terms of multimodel, multimodal and distributed schemes. The multimodel framework is such that different CNN models operating on the same modality cooperate for decision purpose. The multimodal framework  implies specializations of CNNs with respect to  different input modalities. The distributed framework proposed is associated with assessment exchanges: it such that the aggregation aims at determining relevant joint assessments for mapping a given input to a single or a multiple output category. Performance of these aggregation frameworks are shown to be outstanding for both standard and extreme classification issues.
Visible and Invisible: Causal Variable Learning and its Application in a Cancer Study
Authors: Jiqing Wu, Inti Zlobec, Viktor Kölzer
Date: 2021-01-01 00:00:00
Tasks: Program Synthesis

Causal visual discovery is a fundamental yet challenging problem in many research fields. Given visual data and the outcome of interest, the goal is to infer the cause-effect relation. Aside from rich visual ('visible') variables, oftentimes, the outcome is also determined by 'invisible' variables, i.e. the variables from non-visual modalities that do not have visual counterparts. This $(\textbf{visible}, \textbf{invisible})$ combination is particularly common in the clinical domain.

Built upon the promising invariant causal prediction (ICP) framework, we propose a novel $\varepsilon$-ICP algorithm to resolve the (visible, invisible) setting. To efficiently discover $\varepsilon$-plausible causal variables and to estimate the cause-effect relation, the $\varepsilon$-ICP is learned under a min-min optimisation scheme. Driven by the need for clinical reliability and interpretability, the $\varepsilon$-ICP is implemented with a typed neural-symbolic functional language. With the built-in program synthesis method, we can synthesize a type-safe program that is comprehensible to the clinical experts.

For concept validation of the $\varepsilon$-ICP, we carefully design a series of synthetic experiments on the type of visual-perception tasks that are encountered in daily life. To further substantiate the proposed method, we demonstrate the application of $\varepsilon$-ICP on a real-world cancer study dataset, Swiss CRC. This population-based cancer study has spanned over two decades, including 25$k$ fully annotated tissue micro-array (TMA) images with at least $3k \times 3k$ resolution and a broad spectrum of clinical meta data for 533 patients. Both the synthetic and clinical experiments demonstrate the advantages of $\varepsilon$-ICP over the state-of-the-art methods. Finally, we discuss the limitations and challenges to be addressed in the future.
Transformers satisfy
Authors: Feng Shi, Chen Li, Shijie Bian, Yiqiao Jin, Ziheng Xu, Tian Han, Song-Chun Zhu
Date: 2021-01-01 00:00:00

The Propositional Satisfiability Problem (SAT), and more generally, the Constraint Satisfaction Problem (CSP), are mathematical questions defined as finding an assignment to a set of objects that satisfies a series of constraints. The modern approach is trending to solve CSP through neural symbolic methods. Most recent works are sequential model-based and adopt neural embedding, i.e., reinforcement learning with neural graph networks, and graph recurrent neural networks. This work proposes a one-shot model derived from the eminent Transformer architecture for factor graph structure to solve the CSP problem. We define the heterogeneous attention mechanism based on meta-paths for the self-attention between literals, the cross-attention based on the bipartite graph links from literal to clauses, or vice versa. This model takes advantage of parallelism. Our model achieves high speed and very high accuracy on the factor graph for CSPs with arbitrary size.
Meta-k: Towards Unsupervised Prediction of Number of Clusters
Authors: Azade Farshad, Samin Hamidi, Nassir Navab
Date: 2021-01-01 00:00:00
Tasks: Clustering, Prediction

Data clustering is a well-known unsupervised learning approach. Despite the recent advances in clustering using deep neural networks, determining the number of clusters without any information about the given dataset remains an existing problem. There have been classical approaches based on data statistics that require the manual analysis of a data scientist to calculate the probable number of clusters in a dataset. In this work, we propose a new method for unsupervised prediction of the number of clusters in a dataset given only the data without any labels. We evaluate our method extensively on randomly generated datasets using the scikit-learn package and multiple computer vision datasets and show that our method is able to determine the number of classes in a dataset effectively without any supervision.
Ranking Cost: One-Stage Circuit Routing by Directly Optimizing Global Objective Function
Authors: Shiyu Huang, Bin Wang, Dong Li, Jianye Hao, Jun Zhu, Ting Chen
Date: 2021-01-01 00:00:00

Circuit routing has been a historically challenging problem in designing electronic systems such as very large-scale integration (VLSI) and printed circuit boards (PCBs). The main challenge is that connecting a large number of electronic components under specific design rules and constraints involves a very large search space, which is proved to be NP-complete. 
Early solutions are typically designed with hard-coded heuristics, which suffer from problems of non-optimum solutions and lack of flexibility for new design needs. Although a few learning-based methods have been proposed recently, their methods are cumbersome and hard to extend to large-scale applications. In this work, we propose a new algorithm for circuit routing, named as Ranking Cost (RC),  which innovatively combines search-based methods (i.e., A* algorithm) and learning-based methods (i.e., Evolution Strategies) to form an efficient and trainable router under a proper parameterization. Different from two-stage routing methods ( i.e., first global routing and then detailed routing), our method involves a one-stage procedure that directly optimizes the global objective function, thus it can be easy to adapt to new routing rules and constraints. In our method, we introduce a new set of variables called cost maps, which can help the A* router to find out proper paths to achieve the global object. We also train a ranking parameter, which can produce the ranking order and further improve the performance of our method. Our algorithm is trained in an end-to-end manner and does not use any artificial data or human demonstration. In the experiments, we compare our method with the sequential A* algorithm and a canonical reinforcement learning approach, and results show that our method outperforms these baselines with higher connectivity rates and better scalability. Our ablation study shows that our trained cost maps can capture the global information and guide the routing result to approach global optimum.