bluuebunny/crossref_metadata_2025 huggingface dataset:
Visual Similarity Attention
Authors: Zheng Meng , Karanam Srikrishna , Chen Terrence , Radke Richard J. , Wu Ziyan
Published: July 2022

While there has been substantial progress in learning suitable distance metrics, these techniques in general lack transparency and decision reasoning, i.e., explaining why the input set of images is similar or dissimilar. In this work, we solve this key problem by proposing the first method to generate generic visual similarity explanations with gradient-based attention. We demonstrate that our technique is agnostic to the specific similarity model type, e.g., we show applicability to Siamese, triplet, and quadruplet models. Furthermore, we make our proposed similarity attention a principled part of the learning process, resulting in a new paradigm for learning similarity functions. We demonstrate that our learning mechanism results in more generalizable, as well as explainable, similarity models. Finally, we demonstrate the generality of our framework by means of experiments on a variety of tasks, including image retrieval, person re-identification, and low-shot semantic segmentation.
Imperceptible Backdoor Attack: From Input Space to Feature Representation
Authors: Zhong Nan , Qian Zhenxing , Zhang Xinpeng
Published: July 2022

Backdoor attacks are rapidly emerging threats to deep neural networks (DNNs). In the backdoor attack scenario, attackers usually implant the backdoor into the target model by manipulating the training dataset or training process. Then, the compromised model behaves normally for benign input yet makes mistakes when the pre-defined trigger appears. In this paper, we analyze the drawbacks of existing attack approaches and propose a novel imperceptible backdoor attack. We treat the trigger pattern as a special kind of noise following a multinomial distribution. A U-net-based network is employed to generate concrete parameters of multinomial distribution for each benign input. This elaborated trigger ensures that our approach is invisible to both humans and statistical detection. Besides the design of the trigger, we also consider the robustness of our approach against model diagnose-based defences. We force the feature representation of malicious input stamped with the trigger to be entangled with the benign one. We demonstrate the effectiveness and robustness against multiple state-of-the-art defences through extensive datasets and networks. Our trigger only modifies less than 1\% pixels of a benign image while the modification magnitude is 1. Our source code is available at https://github.com/Ekko-zn/IJCAI2022-Backdoor.
Rainy WCity: A Real Rainfall Dataset with Diverse Conditions for Semantic Driving Scene Understanding
Authors: Zhong Xian , Tu Shidong , Ma Xianzheng , Jiang Kui , Huang Wenxin , Wang Zheng
Published: July 2022

Scene understanding in adverse weather conditions (e.g. rainy and foggy days) has drawn increasing attention, arising some specific benchmarks and algorithms. However, scene segmentation under rainy weather is still challenging and under-explored due to the following limitations on the datasets and methods: 1) Manually synthetic rainy samples with empirically settings and human subjective assumptions; 2) Limited rainy conditions, including the rain patterns, intensity, and degradation factors; 3) Separated training manners for image deraining and semantic segmentation. To break these limitations, we pioneer a real, comprehensive, and well-annotated scene understanding dataset under rainy weather, named Rainy WCity. It covers various rain patterns and their bring-in negative visual effects, covering wiper, droplet, reflection, refraction, shadow, windshield-blurring, etc. In addition, to alleviate dependence on paired training samples, we design an unsupervised contrastive learning network for real image deraining and the final rainy scene semantic segmentation via multi-task joint optimization. A comprehensive comparison analysis is also provided, which shows that scene understanding in rainy weather is a largely open problem. Finally, we summarize our general observations, identify open research challenges, and point out future directions.
A Solver + Gradient Descent Training Algorithm for Deep Neural Networks
Authors: Ashok Dhananjay , Nagisetty Vineel , Srinivasa Christopher , Ganesh Vijay
Published: July 2022

We present a novel hybrid algorithm for training Deep Neural Networks that combines the state-of-the-art Gradient Descent (GD) method with a Mixed Integer Linear Programming (MILP) solver, outperforming GD and variants in terms of accuracy, as well as resource and data efficiency for both regression and classification tasks.

Our GD+Solver hybrid algorithm, called GDSolver, works as follows: given a DNN D as input, GDSolver invokes GD to partially train D until it gets stuck in a local minima, at which point GDSolver invokes an MILP solver to exhaustively search a region of the loss landscape around the weight assignments of D’s final layer parameters with the goal of tunnelling through and escaping the local minima. The process is repeated until desired accuracy is achieved.

In our experiments, we find that GDSolver not only scales well to additional data and very large model sizes, but also outperforms all other competing methods in terms of rates of convergence and data efficiency. For regression tasks, GDSolver produced models that, on average, had 31.5% lower MSE in 48% less time, and for classification tasks on MNIST and CIFAR10, GDSolver was able to achieve the highest accuracy over all competing methods, using only 50% of the training data that GD baselines required.
On the Convergence of Fictitious Play: A Decomposition Approach
Authors: Chen Yurong , Deng Xiaotie , Li Chenchen , Mguni David , Wang Jun , Yan Xiang , Yang Yaodong
Published: July 2022

Fictitious play (FP) is one of the most fundamental game-theoretical learning frameworks for computing Nash equilibrium in n-player games, which builds the foundation for modern multi-agent learning algorithms. Although FP has provable convergence guarantees on zero-sum games and potential games, many real-world problems are often a mixture of both and the convergence property of FP has not been fully studied yet. In this paper, we extend the convergence results of FP to the combinations of such games and beyond. Specifically, we derive new conditions for FP to converge by leveraging game decomposition techniques. We further develop a linear relationship unifying cooperation and competition in the sense that these two classes of games are mutually transferable. Finally, we analyse a non-convergent example of FP, the Shapley game, and develop sufficient conditions for FP to converge.
Entity Alignment with Reliable Path Reasoning and Relation-aware Heterogeneous Graph Transformer
Authors: Cai Weishan , Ma Wenjun , Zhan Jieyu , Jiang Yuncheng
Published: July 2022

Entity Alignment (EA) has attracted widespread attention in both academia and industry, which aims to seek entities with same meanings from different Knowledge Graphs (KGs). There are substantial multi-step relation paths between entities in KGs, indicating the semantic relations of entities. However, existing methods rarely consider path information because not all natural paths facilitate for EA judgment. In this paper, we propose a more effective entity alignment framework, RPR-RHGT, which integrates relation and path structure information, as well as the heterogeneous information in KGs. Impressively, an initial reliable path reasoning algorithm is developed to generate the paths favorable for EA task from the relation structures of KGs. This is the first algorithm in the literature to successfully use unrestricted path information. In addition, to efficiently capture heterogeneous features in entity neighborhoods, a relation-aware heterogeneous graph transformer is designed to model the relation and path structures of KGs. Extensive experiments on three well-known datasets show RPR-RHGT significantly outperforms 10 state-of-the-art methods, exceeding the best performing baseline up to 8.62% on Hits@1. We also show its better performance than the baselines on different ratios of training set, and harder datasets.
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification
Authors: Chen Chaochao , Zhou Jun , Zheng Longfei , Wu Huiwen , Lyu Lingjuan , Wu Jia , Wu Bingzhe , Liu Ziqi , Wang Li , Zheng Xiaolin
Published: July 2022

Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between different nodes. High-performance GNN models always depend on both rich features and complete edge information in graph. However, such information could possibly be isolated by different data holders in practice, which is the so-called data isolation problem. To solve this problem, in this paper, we propose VFGNN, a federated GNN learning paradigm for privacy-preserving node classification task under data vertically partitioned setting, which can be generalized to existing GNN models. Specifically, we split the computation graph into two parts. We leave the private data (i.e., features, edges, and labels) related computations on data holders, and delegate the rest of computations to a semi-honest server. We also propose to apply differential privacy to prevent potential information leakage from the server. We conduct experiments on three benchmarks and the results demonstrate the effectiveness of VFGNN.
Self-supervised Graph Neural Networks for Multi-behavior Recommendation
Authors: Gu Shuyun , Wang Xiao , Shi Chuan , Xiao Ding
Published: July 2022

Traditional recommendation usually focuses on utilizing only one target user behavior (e.g., purchase) but ignoring other auxiliary behaviors (e.g., click, add to cart). Early efforts of multi-behavior recommendation often emphasize the differences between multiple behaviors, i.e., they aim to extract useful information by distinguishing different behaviors. However, the commonality between them, which reflects user's common preference for items associated with different behaviors, is largely ignored. Meanwhile, the multi-behavior recommendation still severely suffers from limited supervision signal issue. In this paper, we propose a novel self-supervised graph collaborative filtering model for multi-behavior recommendation named S-MBRec. Specifically, for each behavior, we execute the GCNs to learn the user and item embeddings. Then we design a supervised task, distinguishing the importance of different behaviors, to capture the differences between embeddings. Meanwhile, we propose a star-style contrastive learning task to capture the embedding commonality between target and auxiliary behaviors, so as to alleviate the sparsity of supervision signal, reduce the redundancy among auxiliary behavior, and extract the most critical information. Finally, we jointly optimize the above two tasks. Extensive experiments, in comparison with state-of-the-arts, well demonstrate the effectiveness of S-MBRec, where the maximum improvement can reach to 20%.
Community Question Answering Entity Linking via Leveraging Auxiliary Data
Authors: Li Yuhan , Shen Wei , Gao Jianbo , Wang Yadong
Published: July 2022

Community Question Answering (CQA) platforms contain plenty of CQA texts (i.e., questions and answers corresponding to the question) where named entities appear ubiquitously. In this paper, we define a new task of CQA entity linking (CQAEL) as linking the textual entity mentions detected from CQA texts with their corresponding entities in a knowledge base. This task can facilitate many downstream applications including expert finding and knowledge base enrichment. Traditional entity linking methods mainly focus on linking entities in news documents, and are suboptimal over this new task of CQAEL since they cannot effectively leverage various informative auxiliary data involved in the CQA platform to aid entity linking, such as parallel answers and two types of meta-data (i.e., topic tags and users). To remedy this crucial issue, we propose a novel transformer-based framework to effectively harness the knowledge delivered by different kinds of auxiliary data to promote the linking performance. We validate the superiority of our framework through extensive experiments over a newly released CQAEL data set against state-of-the-art entity linking methods.
Discrete Listwise Personalized Ranking for Fast Top-N Recommendation with Implicit Feedback
Authors: Luo Fangyuan , Wu Jun , Wang Tao
Published: July 2022

We address the efficiency problem of personalized ranking from implicit feedback by hashing users and items with binary codes, so that top-N recommendation can be fast executed in a Hamming space by bit operations. However, current hashing methods for top-N recommendation fail to align their learning objectives (such as pointwise or pairwise loss) with the benchmark metrics for ranking quality (e.g. Average Precision, AP), resulting in sub-optimal accuracy. To this end, we propose a Discrete Listwise Personalized Ranking (DLPR) model that optimizes AP under discrete constraints for fast and accurate top-N recommendation. To resolve the challenging DLPR problem, we devise an efficient algorithm that can directly learn binary codes in a relaxed continuous solution space. Specifically, theoretical analysis shows that the optimal solution to the relaxed continuous optimization problem is exactly the same as that of the original discrete DLPR problem. Through extensive experiments on two real-world datasets, we show that DLPR consistently surpasses state-of-the-art hashing methods for top-N recommendation.
Continual Federated Learning Based on Knowledge Distillation
Authors: Ma Yuhang , Xie Zhongle , Wang Jue , Chen Ke , Shou Lidan
Published: July 2022

Federated learning (FL) is a promising approach for learning a shared global model on decentralized data owned by multiple clients without exposing their privacy. In real-world scenarios, data accumulated at the client-side varies in distribution over time. As a consequence, the global model tends to forget the knowledge obtained from previous tasks while learning new tasks, showing signs of "catastrophic forgetting". Previous studies in centralized learning use techniques such as data replay and parameter regularization to mitigate catastrophic forgetting. Unfortunately, these techniques cannot adequately solve the non-trivial problem in FL. We propose Continual Federated Learning with Distillation (CFeD) to address catastrophic forgetting under FL. CFeD performs knowledge distillation on both the clients and the server, with each party independently having an unlabeled surrogate dataset, to mitigate forgetting. Moreover, CFeD assigns different learning objectives, namely learning the new task and reviewing old tasks, to different clients, aiming to improve the learning ability of the model. The results show that our method performs well in mitigating catastrophic forgetting and achieves a good trade-off between the two objectives.
Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features
Authors: Shang Xinyi , Lu Yang , Huang Gang , Wang Hanzi
Published: July 2022

Federated learning (FL) provides a privacy-preserving solution for distributed machine learning tasks. One challenging problem that severely damages the performance of FL models is the co-occurrence of data heterogeneity and long-tail distribution, which frequently appears in real FL applications. In this paper, we reveal an intriguing fact that the biased classifier is the primary factor leading to the poor performance of the global model. Motivated by the above finding, we propose a novel and privacy-preserving FL method for heterogeneous and long-tailed data via Classifier Re-training with Federated Features (CReFF). The classifier re-trained on federated features can produce comparable performance as the one re-trained on real data in a privacy-preserving manner without information leakage of local data or class distribution. Experiments on several benchmark datasets show that the proposed CReFF is an effective solution to obtain a promising FL model under heterogeneous and long-tailed data. Comparative results with the state-of-the-art FL methods also validate the superiority of CReFF. Our code is available at https://github.com/shangxinyi/CReFF-FL.
Augmenting Knowledge Graphs for Better Link Prediction
Authors: Wang Jiang , Ilievski Filip , Szekely Pedro , Yao Ke-Thia
Published: July 2022

Embedding methods have demonstrated robust performance on the task of link prediction in knowledge graphs, by mostly encoding entity relationships. Recent methods propose to enhance the loss function with a literal-aware term. In this paper, we propose KGA: a knowledge graph augmentation method that incorporates literals in an embedding model without modifying its loss function. KGA discretizes quantity and year values into bins, and chains these bins both horizontally, modeling neighboring values, and vertically, modeling multiple levels of granularity. KGA is scalable and can be used as a pre-processing step for any existing knowledge graph embedding model. Experiments on legacy benchmarks and a new large benchmark, DWD, show that augmenting the knowledge graph with quantities and years is beneficial for predicting both entities and numbers, as KGA outperforms the vanilla models and other relevant baselines. Our ablation studies confirm that both quantities and years contribute to KGA's performance, and that its performance depends on the discretization and binning settings. We make the code, models, and the DWD benchmark publicly available to facilitate reproducibility and future research.
Approval with Runoff
Authors: Delemazure Théo , Lang Jérôme , Laslier Jean-François , Sanver M. Remzi
Published: July 2022

We define a family of runoff rules that work as follows: voters cast approval ballots over candidates; two finalists are selected; and the winner is decided by majority. With approval-type ballots, there are various ways to select the finalists. We leverage known approval-based committee rules and study the obtained runoff rules from an axiomatic point of view. Then we analyze the outcome of these rules on single-peaked profiles, and on real data.
Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks
Authors: Zhang Jiaqiang , Wang Senzhang , Chen Songcan
Published: July 2022

Detecting abnormal nodes from attributed networks is of great importance in many real applications, such as financial fraud detection and cyber security. This task is challenging due to both the complex interactions between the anomalous nodes with other counterparts and their inconsistency in terms of attributes. This paper proposes a self-supervised learning framework that jointly optimizes a multi-view contrastive learning-based module and an attribute reconstruction-based module to more accurately detect anomalies on attributed networks. Specifically, two contrastive learning views are firstly established, which allow the model to better encode rich local and global information related to the abnormality. Motivated by the attribute consistency principle between neighboring nodes, a masked autoencoder-based reconstruction module is also introduced to identify the nodes which have large reconstruction errors, then are regarded as anomalies. Finally, the two complementary modules are integrated for more accurately detecting the anomalous nodes. Extensive experiments conducted on five benchmark datasets show our model outperforms current state-of-the-art models.
Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand Prediction
Authors: Zhang Ruixing , Han Liangzhe , Liu Boyi , Zeng Jiayuan , Sun Leilei
Published: July 2022

Recent years have witnessed a rapid growth of applying deep spatiotemporal methods in traffic forecasting. However, the prediction of origin-destination (OD) demands is still a challenging problem since the number of OD pairs is usually quadratic to the number of stations. In this case, most of the existing spatiotemporal methods fail to handle spatial relations on such a large scale. To address this problem, this paper provides a dynamic graph representation learning framework for OD demands prediction. In particular, a hierarchical memory updater is first proposed to maintain a time-aware representation for each node, and the representations are updated according to the most recently observed OD trips in continuous-time and multiple discrete-time ways. Second, a spatiotemporal propagation mechanism is provided to aggregate representations of neighbor nodes along a random spatiotemporal route which treats origin and destination as two different semantic entities. Last, an objective function is designed to derive the future OD demands according to the most recent node representations, and also to tackle the data sparsity problem in OD prediction. Extensive experiments have been conducted on two real-world datasets, and the experimental results demonstrate the superiority of the proposed method. The code and data are available at https://github.com/Rising0321/HMOD.
Rethinking InfoNCE: How Many Negative Samples Do You Need?
Authors: Wu Chuhan , Wu Fangzhao , Huang Yongfeng
Published: July 2022

InfoNCE is a widely used contrastive training loss. It aims to estimate the mutual information between a pair of variables by discriminating between each positive pair and its associated K negative pairs. It is proved that when the sample labels are clean, the lower bound of mutual information estimation is tighter when more negative samples are incorporated, which usually yields better model performance. However, in practice the labels often contain noise, and incorporating too many noisy negative samples into model training may be suboptimal. In this paper, we study how many negative samples are optimal for InfoNCE in different scenarios via a semi-quantitative theoretical framework. More specifically, we first propose a probabilistic model to analyze the influence of the negative sampling ratio K on training sample informativeness. Then, we design a training effectiveness function to measure the overall influence of training samples based on their informativeness. We estimate the optimal negative sampling ratio using the K value that maximizes the training effectiveness function. Based on our framework, we further propose an adaptive negative sampling method that can dynamically adjust the negative sampling ratio to improve InfoNCE-based model training. Extensive experiments in three different tasks show our framework can accurately predict the optimal negative sampling ratio, and various models can benefit from our adaptive negative sampling method.
Abstract Argumentation Frameworks with Marginal Probabilities
Authors: Fazzinga Bettina , Flesca Sergio , Furfaro Filippo
Published: July 2022

In the context of probabilistic AAFs, we intro-

duce AAFs with marginal probabilities (mAAFs)

requiring only marginal probabilities of argu-

ments/attacks to be specified and not relying on the

independence assumption. Reasoning over mAAFs

requires taking into account multiple probability

distributions over the possible worlds, so that the

probability of extensions is not determined by a

unique value, but by an interval. We focus on the

problems of computing the max and min probabil-

ities of extensions over mAAFs under Dung’s se-

mantics, characterize their complexity, and provide

closed formulas for polynomial cases.
Plausibility Reasoning via Projected Answer Set Counting - A Hybrid Approach
Authors: Fichte Johannes K. , Hecher Markus , Nadeem Mohamed A.
Published: July 2022

Answer set programming is a form of declarative programming widely used to solve difficult search problems. Probabilistic applications however require to go beyond simple search for one solution and need counting. One such application is plausibility reasoning, which provides more fine-grained reasoning mode between simple brave and cautious reasoning. When modeling with ASP, we oftentimes introduce auxiliary atoms in the program. If these atoms are functionally independent of the atoms of interest, we need to hide the auxiliary atoms and project the count to the atoms of interest resulting in the problem projected answer set counting. In practice, counting becomes quickly infeasible with standard systems such as clasp. In this paper, we present a novel hybrid approach for plausibility reasoning under projections, thereby relying on projected answer set counting as basis. Our approach combines existing systems with fast dynamic programming, which in our experiments shows advantages over existing ASP systems.
Linear Temporal Logic Modulo Theories over Finite Traces
Authors: Geatti Luca , Gianola Alessandro , Gigante Nicola
Published: July 2022

This paper studies Linear Temporal Logic over Finite Traces (LTLf) where proposition letters are replaced with first-order formulas interpreted over arbitrary theories, in the spirit of Satisfiability Modulo Theories. The resulting logic, called LTLf Modulo Theories (LTLfMT), is semi-decidable. Nevertheless, its high expressiveness comes useful in a number of use cases, such as model-checking of data-aware processes and data-aware planning. Despite the general undecidability of these problems, being able to solve satisfiable instances is a compromise worth studying. After motivating and describing such use cases, we provide a sound and complete semi-decision procedure for LTLfMT based on the SMT encoding of a one-pass tree-shaped tableau system. The algorithm is implemented in the BLACK satisfiability checking tool, and an experimental evaluation shows the feasibility of the approach on novel benchmarks.
Invasion Dynamics in the Biased Voter Process
Authors: Durocher Loke , Karras Panagiotis , Pavlogiannis Andreas , Tkadlec Josef
Published: July 2022

The voter process is a classic stochastic process that models the invasion of a mutant trait A (e.g., a new opinion, belief, legend, genetic mutation, magnetic spin) in a population of agents (e.g., people, genes, particles) who share a resident trait B, spread over the nodes of a graph. An agent may adopt the trait of one of its neighbors at any time, while the invasion bias r quantifies the stochastic preference towards (r>1) or against (r<1) adopting A over B. Success is measured in terms of the fixation probability, i.e., the probability that eventually all agents have adopted the mutant trait A. In this paper we study the problem of fixation probability maximization under this model: given a budget k, find a set of k agents to initiate the invasion that maximizes the fixation probability. We show that the problem is NP-hard for both regimes r>1 and r<1, while the latter case is also inapproximable within any multiplicative factor that is independent of r. On the positive side, we show that when r>1, the optimization function is submodular and thus can be greedily approximated within a factor 1-1/e. An experimental evaluation of some proposed heuristics corroborates our results.
Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention
Authors: Xu Yichong , Zhu Chenguang , Wang Shuohang , Sun Siqi , Cheng Hao , Liu Xiaodong , Gao Jianfeng , He Pengcheng , Zeng Michael , Huang Xuedong
Published: July 2022

Most of today's AI systems focus on using self-attention mechanisms and transformer architectures on large amounts of diverse data to achieve impressive performance gains. In this paper, we propose to augment the transformer architecture with an external attention mechanism to bring external knowledge and context to bear.

By integrating external information into the prediction process, we hope to reduce the need for ever-larger models and increase the democratization of AI systems.

We find that the proposed external attention mechanism can significantly improve the performance of existing AI systems, allowing practitioners to easily customize foundation AI models to many diverse downstream applications.

In particular, we focus on the task of Commonsense Reasoning, demonstrating that the proposed external attention mechanism can augment existing transformer models and significantly improve the model's reasoning capabilities. The proposed system, Knowledgeable External Attention for commonsense Reasoning (KEAR), reaches human parity on the open CommonsenseQA research benchmark with an accuracy of 89.4% in comparison to the human accuracy of 88.9%.
Synthesis of Maximally Permissive Strategies for LTLf Specifications
Authors: Zhu Shufang , De Giacomo Giuseppe
Published: July 2022

In this paper, we study synthesis of maximally permissive strategies for Linear Temporal Logic on finite traces (LTLf) specifications. That is, instead of computing a single strategy (aka plan, or policy), we aim at computing the entire set of strategies at once and then choosing among them while in execution, without committing to a single one beforehand. Maximally permissive strategies have been introduced and investigated for safety properties, especially in the context of Discrete Event Control Theory. However, the available results for safety properties do not apply to reachability properties (eventually reach a given state of affair) nor to LTLf properties in general. In this paper, we show that maximally permissive strategies do exist also for reachability and general LTLf properties, and can in fact be computed with minimal overhead wrt the computation of a single strategy using state-of-the-art tools.
Efficient Resource Allocation with Secretive Agents
Authors: Ebadian Soroush , Freeman Rupert , Shah Nisarg
Published: July 2022

We consider the allocation of homogeneous divisible goods to agents with linear additive valuations. Our focus is on the case where some agents are secretive and reveal no preference information, while the remaining agents reveal full preference information. We study distortion, which is the worst-case approximation ratio when maximizing social welfare given such partial information about agent preferences. As a function of the number of secretive agents k relative to the overall number of agents n, we identify the exact distortion for every p-mean welfare function, which includes the utilitarian welfare (p=1), the Nash welfare (p -> 0), and the egalitarian welfare (p -> -Inf).
Logit Mixing Training for More Reliable and Accurate Prediction
Authors: Bang Duhyeon , Baek Kyungjune , Kim Jiwoo , Jeon Yunho , Kim Jin-Hwa , Kim Jiwon , Lee Jongwuk , Shim Hyunjung
Published: July 2022

When a person solves the multi-choice problem, she considers not only what is the answer but also what is not the answer. Knowing what choice is not the answer and utilizing the relationships between choices, she can improve the prediction accuracy. Inspired by this human reasoning process, we propose a new training strategy to fully utilize inter-class relationships, namely LogitMix. Our strategy is combined with recent data augmentation techniques, e.g., Mixup, Manifold Mixup, CutMix, and PuzzleMix. Then, we suggest using a mixed logit, i.e., a mixture of two logits, as an auxiliary training objective. Since the logit can preserve both positive and negative inter-class relationships, it can impose a network to learn the probability of wrong answers correctly. Our extensive experimental results on the image- and language-based tasks demonstrate that LogitMix achieves state-of-the-art performance among recent data augmentation techniques regarding calibration error and prediction accuracy.
Option Transfer and SMDP Abstraction with Successor Features
Authors: Han Dongge , Tschiatschek Sebastian
Published: July 2022

Abstraction plays an important role in the generalisation of knowledge and skills and is key to sample efficient learning. In this work, we study joint temporal and state abstraction in reinforcement learning, where temporally-extended actions in the form of options induce temporal abstractions, while aggregation of similar states with respect to abstract options induces state abstractions. Many existing abstraction schemes ignore the interplay of state and temporal abstraction. Consequently, the considered option policies often cannot be directly transferred to new environments due to changes in the state space and transition dynamics. To address this issue, we propose a novel abstraction scheme building on successor features. This includes an algorithm for transferring abstract options across different environments and a state abstraction mechanism that allows us to perform efficient planning with the transferred options.
Reconstructing Diffusion Networks from Incomplete Data
Authors: Huang Hao , Han Keqi , Xu Beicheng , Gan Ting
Published: July 2022

To reconstruct the topology of a diffusion network, existing approaches customarily demand not only eventual infection statuses of nodes, but also the exact times when infections occur. In real-world settings, such as the spread of epidemics, tracing the exact infection times is often infeasible; even obtaining the eventual infection statuses of all nodes is a challenging task. In this work, we study topology reconstruction of a diffusion network with incomplete observations of the node infection statuses. To this end, we iteratively infer the network topology based on observed infection statuses and estimated values for unobserved infection statuses by investigating the correlation of node infections, and learn the most probable probabilities of the infection propagations among nodes w.r.t. current inferred topology, as well as the corresponding probability distribution of each unobserved infection status, which in turn helps update the estimate of unobserved data. Extensive experimental results on both synthetic and real-world networks verify the effectiveness and efficiency of our approach.
On the Channel Pruning using Graph Convolution Network for Convolutional Neural Network Acceleration
Authors: Jiang Di , Cao Yuan , Yang Qiang
Published: July 2022

Network pruning is considered efficient for sparsification and acceleration of Convolutional Neural Network (CNN) based models that can be adopted in re-source-constrained environments. Inspired by two popular pruning criteria, i.e. magnitude and similarity, this paper proposes a novel structural pruning method based on Graph Convolution Network (GCN) to further promote compression performance. The channel features are firstly extracted by Global Average Pooling (GAP) from a batch of samples, and a graph model for each layer is generated based on the similarity of features. A set of agents for individual CNN layers are implemented by GCN and utilized to synthesize comprehensive channel information and determine the pruning scheme for the overall CNN model. The training process of each agent is carried out using Reinforcement Learning (RL) to ensure their convergence and adaptability to various network architectures. The proposed solution is assessed based on a range of image classification datasets i.e., CIFAR and Tiny-ImageNet. The numerical results indicate that the proposed pruning method outperforms the pure magnitude-based or similarity-based pruning solutions and other SOTA methods (e.g., HRank and SCP). For example, the proposed method can prune VGG16 by removing 93% of the model parameters without any accuracy reduction in the CIFAR10 dataset.
Ridgeless Regression with Random Features
Authors: Li Jian , Liu Yong , Zhang Yingying
Published: July 2022

Recent theoretical studies illustrated that kernel ridgeless regression can guarantee good generalization ability without an explicit regularization. In this paper, we investigate the statistical properties of ridgeless regression with random features and stochastic gradient descent. We explore the effect of factors in the stochastic gradient and random features, respectively. Specifically, random features error exhibits the double-descent curve. Motivated by the theoretical findings, we propose a tunable kernel algorithm that optimizes the spectral density of kernel during training. Our work bridges the interpolation theory and practical algorithm.
Deep Graph Matching for Partial Label Learning
Authors: Lyu Gengyu , Wu Yanan , Feng Songhe
Published: July 2022

Partial Label Learning (PLL) aims to learn from training data where each instance is associated with a set of candidate labels, among which only one is correct. In this paper, we formulate the task of PLL problem as an ``instance-label'' matching selection problem, and propose a DeepGNN-based graph matching PLL approach to solve it. Specifically, we first construct all instances and labels as graph nodes into two different graphs respectively, and then integrate them into a unified matching graph by connecting each instance to its candidate labels. Afterwards, the graph attention mechanism is adopted to aggregate and update all nodes state on the instance graph to form structural representations for each instance. Finally, each candidate label is embedded into its corresponding instance and derives a matching affinity score for each instance-label correspondence with a progressive cross-entropy loss. Extensive experiments on various data sets have demonstrated the superiority of our proposed method.
Weakly-supervised Text Classification with Wasserstein Barycenters Regularization
Authors: Ouyang Jihong , Wang Yiming , Li Ximing , Li Changchun
Published: July 2022

Weakly-supervised text classification aims to train predictive models with unlabeled texts and a few representative words of classes, referred to as category words, rather than labeled texts. These weak supervisions are much more cheaper and easy to collect in real-world scenarios. To resolve this task, we propose a novel deep classification model, namely Weakly-supervised Text Classification with Wasserstein Barycenter Regularization (WTC-WBR). Specifically, we initialize the pseudo-labels of texts by using the category word occurrences, and formulate a weakly self-training framework to iteratively update the weakly-supervised targets by combining the pseudo-labels with the sharpened predictions. Most importantly, we suggest a Wasserstein barycenter regularization with the weakly-supervised targets on the deep feature space. The intuition is that the texts tend to be close to the corresponding Wasserstein barycenter indicated by weakly-supervised targets. Another benefit is that the regularization can capture the geometric information of deep feature space to boost the discriminative power of deep features. Experimental results demonstrate that WTC-WBR outperforms the existing weakly-supervised baselines, and achieves comparable performance to semi-supervised and supervised baselines.
Dynamic Sparse Training for Deep Reinforcement Learning
Authors: Sokar Ghada , Mocanu Elena , Mocanu Decebal Constantin , Pechenizkiy Mykola , Stone Peter
Published: July 2022

Deep reinforcement learning (DRL) agents are trained through trial-and-error interactions with the environment. This leads to a long training time for dense neural networks to achieve good performance. Hence, prohibitive computation and memory resources are consumed. Recently, learning efficient DRL agents has received increasing attention. Yet, current methods focus on accelerating inference time. In this paper, we introduce for the first time a dynamic sparse training approach for deep reinforcement learning to accelerate the training process. The proposed approach trains a sparse neural network from scratch and dynamically adapts its topology to the changing data distribution during training. Experiments on continuous control tasks show that our dynamic sparse agents achieve higher performance than the equivalent dense methods, reduce the parameter count and floating-point operations (FLOPs) by 50%, and have a faster learning speed that enables reaching the performance of dense agents with 40−50% reduction in the training steps.
Information Augmentation for Few-shot Node Classification
Authors: Wu Zongqian , Zhou Peng , Wen Guoqiu , Wan Yingying , Ma Junbo , Cheng Debo , Zhu Xiaofeng
Published: July 2022

Although meta-learning and metric learning have been widely applied for few-shot node classification (FSNC), some limitations still need to be addressed, such as expensive time costs for the meta-train and difficult of exploring the complex structure inherent the graph data. To address in issues, this paper proposes a new data augmentation method to conduct FSNC on the graph data including parameter initialization and parameter fine-tuning. Specifically, parameter initialization only conducts a multi-classification task on the base classes, resulting in good generalization ability and less time cost. Parameter fine-tuning designs two data augmentation methods (i.e., support augmentation and shot augmentation) on the novel classes to generate sufficient node features so that any traditional supervised classifiers can be used to classify the query set. As a result, the proposed method is the first work of data augmentation for FSNC. Experiment results show the effectiveness and the efficiency of our proposed method, compared to state-of-the-art methods, in terms of different classification tasks.
Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble
Authors: Yang Zhengyu , Ren Kan , Luo Xufang , Liu Minghuan , Liu Weiqing , Bian Jiang , Zhang Weinan , Li Dongsheng
Published: July 2022

It is challenging for reinforcement learning (RL) algorithms to succeed in real-world applications. Take financial trading as an example, the market information is noisy yet imperfect and the macroeconomic regulation or other factors may shift between training and evaluation, thus it requires both generalization and high sample efficiency for resolving the task. However, directly applying typical RL algorithms can lead to poor performance in such scenarios. To derive a robust and applicable RL algorithm, in this work, we design a simple but effective method named Ensemble Proximal Policy Optimization (EPPO), which learns ensemble policies in an end-to-end manner. Notably, EPPO combines each policy and the policy ensemble organically and optimizes both simultaneously. In addition, EPPO adopts a diversity enhancement regularization over the policy space which helps to generalize to unseen states and promotes exploration. We theoretically prove that EPPO can increase exploration efficacy, and through comprehensive experimental evaluations on various tasks, we demonstrate that EPPO achieves higher efficiency and is robust for real-world applications compared with vanilla policy optimization algorithms and other ensemble methods. Code and supplemental materials are available at https://seqml.github.io/eppo.
Masked Feature Generation Network for Few-Shot Learning
Authors: Yu Yunlong , Zhang Dingyi , Ji Zhong
Published: July 2022

In this paper, we present a feature-augmentation approach called Masked Feature Generation Network (MFGN) for Few-Shot Learning (FSL), a challenging task that attempts to recognize the novel classes with a few visual instances for each class. Most of the feature-augmentation approaches tackle FSL tasks via modeling the intra-class distributions. We extend this idea further to explicitly capture the intra-class variations in a one-to-many manner. Specifically, MFGN consists of an encoder-decoder architecture, with an encoder that performs as a feature extractor and extracts the feature embeddings of the available visual instances (the unavailable instances are seen to be masked), along with a decoder that performs as a feature generator and reconstructs the feature embeddings of the unavailable visual instances from both the available feature embeddings and the masked tokens. Equipped with this generative architecture, MFGN produces nontrivial visual features for the novel classes with limited visual instances. In extensive experiments on four FSL benchmarks, MFGN performs competitively and outperforms the state-of-the-art competitors on most of the few-shot classification tasks.
Towards Joint Intent Detection and Slot Filling via Higher-order Attention
Authors: Chen Dongsheng , Huang Zhiqi , Wu Xian , Ge Shen , Zou Yuexian
Published: July 2022

Recently, attention-based models for joint intent detection and slot filling have achieved state-of-the-art performance. However, we think the conventional attention can only capture the first-order feature interaction between two tasks and is insufficient. To address this issue, we propose a unified BiLinear attention block, which leverages bilinear pooling to synchronously explore both the contextual and channel-wise bilinear attention distributions to capture the second-order interactions between the input intent and slot features. Higher-order interactions are constructed by combining many such blocks and exploiting Exponential Linear activations. Furthermore, we present a Higher-order Attention Network (HAN) to jointly model them. The experimental results show that our approach outperforms the state-of-the-art results. We also conduct experiments on the new SLURP dataset, and give a discussion on HAN’s properties, i.e., robustness and generalization.
MuiDial: Improving Dialogue Disentanglement with Intent-Based Mutual Learning
Authors: Jiang Ziyou , Shi Lin , Chen Celia , Mu Fangwen , Zhang Yumin , Wang Qing
Published: July 2022

The main goal of dialogue disentanglement is to separate the mixed utterances from a chat slice into independent dialogues. Existing models often utilize either an utterance-to-utterance (U2U) prediction to determine whether two utterances that have the “reply-to” relationship belong to one dialogue, or an utterance-to-thread (U2T) prediction to determine which dialogue-thread a given utterance should belong to. Inspired by mutual leaning, we propose MuiDial, a novel dialogue disentanglement model, to exploit the intent of each utterance and feed the intent to a mutual learning U2U-U2T disentanglement model. Experimental results and in-depth analysis on several benchmark datasets demonstrate the effectiveness and generalizability of our approach.
Taylor, Can You Hear Me Now? A Taylor-Unfolding Framework for Monaural Speech Enhancement
Authors: Li Andong , You Shan , Yu Guochen , Zheng Chengshi , Li Xiaodong
Published: July 2022

While the deep learning techniques promote the rapid development of the speech enhancement (SE) community, most schemes only pursue the performance in a black-box manner and lack adequate model interpretability. Inspired by Taylor's approximation theory, we propose an interpretable decoupling-style SE framework, which disentangles the complex spectrum recovery into two separate optimization problems i.e., magnitude and complex residual estimation. Specifically, serving as the 0th-order term in Taylor's series, a filter network is delicately devised to suppress the noise component only in the magnitude domain and obtain a coarse spectrum. To refine the phase distribution, we estimate the sparse complex residual, which is defined as the difference between target and coarse spectra, and measures the phase gap. In this study, we formulate the residual component as the combination of various high-order Taylor terms and propose a lightweight trainable module to replace the complicated derivative operator between adjacent terms. Finally, following Taylor's formula, we can reconstruct the target spectrum by the superimposition between 0th-order and high-order terms. Experimental results on two benchmark datasets show that our framework achieves state-of-the-art performance over previous competing baselines in various evaluation metrics. The source code is available at https://github.com/Andong-Li-speech/TaylorSENet.