paper_id string | title string | paper_url string | pdf_url string | authors list | abstract large_string | track string | primary_area string | doi string | volume string | issue string | pages string | abstract_source string | arxiv_id string | arxiv_id_source string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
10.1609/aaai.v37i5.25807 | Materialisation-Based Reasoning in DatalogMTL with Bounded Intervals | https://ojs.aaai.org/index.php/AAAI/article/view/25807 | https://ojs.aaai.org/index.php/AAAI/article/download/25807/25579 | [
"Przemysław A. Wałęga",
"Michał Zawidzki",
"Dingmin Wang",
"Bernardo Cuenca Grau"
] | DatalogMTL is a powerful extension of Datalog with operators from metric temporal logic (MTL), which has received significant attention in recent years. In this paper, we investigate materialisation-based reasoning (a.k.a. forward chaining) in the context of DatalogMTL programs and datasets with bounded intervals, wher... | main | null | 10.1609/aaai.v37i5.25807 | 37 | 5 | 6566-6574 | official | null | null |
10.1609/aaai.v37i2.25316 | Channel Regeneration: Improving Channel Utilization for Compact DNNs | https://ojs.aaai.org/index.php/AAAI/article/view/25316 | https://ojs.aaai.org/index.php/AAAI/article/download/25316/25088 | [
"Ankit Sharma",
"Hassan Foroosh"
] | Overparameterized deep neural networks have redundant neurons that do not contribute to the network's accuracy. In this paper, we introduce a novel channel regeneration technique that reinvigorates these redundant channels by re-initializing its batch normalization scaling factor gamma. This re-initialization of BN gam... | main | null | 10.1609/aaai.v37i2.25316 | 37 | 2 | 2218-2226 | official | null | null |
10.1609/aaai.v37i1.25110 | Scalable Spatial Memory for Scene Rendering and Navigation | https://ojs.aaai.org/index.php/AAAI/article/view/25110 | https://ojs.aaai.org/index.php/AAAI/article/download/25110/24882 | [
"Wen-Cheng Chen",
"Chu-Song Chen",
"Wei-Chen Chiu",
"Min-Chun Hu"
] | Neural scene representation and rendering methods have shown promise in learning the implicit form of scene structure without supervision. However, the implicit representation learned in most existing methods is non-expandable and cannot be inferred online for novel scenes, which makes the learned representation diffic... | main | null | 10.1609/aaai.v37i1.25110 | 37 | 1 | 369-377 | official | null | null |
10.1609/aaai.v37i5.25693 | Combinatorial Civic Crowdfunding with Budgeted Agents: Welfare Optimality at Equilibrium and Optimal Deviation | https://ojs.aaai.org/index.php/AAAI/article/view/25693 | https://ojs.aaai.org/index.php/AAAI/article/download/25693/25465 | [
"Sankarshan Damle",
"Manisha Padala",
"Sujit Gujar"
] | Civic Crowdfunding (CC) uses the ``power of the crowd" to garner contributions towards public projects. As these projects are non-excludable, agents may prefer to ``free-ride," resulting in the project not being funded. Researchers introduce refunds for single project CC to incentivize agents to contribute, guaranteein... | main | null | 10.1609/aaai.v37i5.25693 | 37 | 5 | 5582-5590 | official | 2211.13941 | title_snapshot |
10.1609/aaai.v37i9.26292 | Lifelong Compression Mixture Model via Knowledge Relationship Graph | https://ojs.aaai.org/index.php/AAAI/article/view/26292 | https://ojs.aaai.org/index.php/AAAI/article/download/26292/26064 | [
"Fei Ye",
"Adrian G. Bors"
] | Task-Free Continual Learning (TFCL) represents a challenging scenario for lifelong learning because the model, under this paradigm, does not access any task information. The Dynamic Expansion Model (DEM) has shown promising results in this scenario due to its scalability and generalisation power. However, DEM focuses o... | main | null | 10.1609/aaai.v37i9.26292 | 37 | 9 | 10900-10908 | official | null | null |
10.1609/aaai.v37i5.25714 | Differentially Private Condorcet Voting | https://ojs.aaai.org/index.php/AAAI/article/view/25714 | https://ojs.aaai.org/index.php/AAAI/article/download/25714/25486 | [
"Zhechen Li",
"Ao Liu",
"Lirong Xia",
"Yongzhi Cao",
"Hanpin Wang"
] | Designing private voting rules is an important and pressing problem for trustworthy democracy. In this paper, under the framework of differential privacy, we propose a novel famliy of randomized voting rules based on the well-known Condorcet method, and focus on three classes of voting rules in this family: Laplacian C... | main | null | 10.1609/aaai.v37i5.25714 | 37 | 5 | 5755-5763 | official | 2206.13081 | title_snapshot |
10.1609/aaai.v37i5.25728 | Tournament Fixing Parameterized by Feedback Vertex Set Number Is FPT | https://ojs.aaai.org/index.php/AAAI/article/view/25728 | https://ojs.aaai.org/index.php/AAAI/article/download/25728/25500 | [
"Meirav Zehavi"
] | A knockout (or single-elimination) tournament is a format of a competition that is very popular in practice (particularly in sports, elections and decision making), and which has been extensively and intensively studied from a theoretical point of view for more than a decade. Particular attention has been devoted to th... | main | null | 10.1609/aaai.v37i5.25728 | 37 | 5 | 5876-5883 | official | null | null |
10.1609/aaai.v37i2.25294 | Domain Decorrelation with Potential Energy Ranking | https://ojs.aaai.org/index.php/AAAI/article/view/25294 | https://ojs.aaai.org/index.php/AAAI/article/download/25294/25066 | [
"Sen Pei",
"Jiaxi Sun",
"Richard Yi Da Xu",
"Shiming Xiang",
"Gaofeng Meng"
] | Machine learning systems, especially the methods based on deep learning, enjoy great success in modern computer vision tasks under ideal experimental settings. Generally, these classic deep learning methods are built on the i.i.d. assumption, supposing the training and test data are drawn from the same distribution ind... | main | null | 10.1609/aaai.v37i2.25294 | 37 | 2 | 2020-2028 | official | 2207.12194 | title_snapshot |
10.1609/aaai.v37i7.25995 | Key Feature Replacement of In-Distribution Samples for Out-of-Distribution Detection | https://ojs.aaai.org/index.php/AAAI/article/view/25995 | https://ojs.aaai.org/index.php/AAAI/article/download/25995/25767 | [
"Jaeyoung Kim",
"Seo Taek Kong",
"Dongbin Na",
"Kyu-Hwan Jung"
] | Out-of-distribution (OOD) detection can be used in deep learning-based applications to reject outlier samples from being unreliably classified by deep neural networks. Learning to classify between OOD and in-distribution samples is difficult because data comprising the former is extremely diverse. It has been observed ... | main | null | 10.1609/aaai.v37i7.25995 | 37 | 7 | 8246-8254 | official | 2301.13012 | title_snapshot |
10.1609/aaai.v37i5.25746 | Evaluating and Improving Interactions with Hazy Oracles | https://ojs.aaai.org/index.php/AAAI/article/view/25746 | https://ojs.aaai.org/index.php/AAAI/article/download/25746/25518 | [
"Stephan J. Lemmer",
"Jason J. Corso"
] | Many AI systems integrate sensor inputs, world knowledge, and human-provided information to perform inference. While such systems often treat the human input as flawless, humans are better thought of as hazy oracles whose input may be ambiguous or outside of the AI system's understanding. In such situations it makes se... | main | null | 10.1609/aaai.v37i5.25746 | 37 | 5 | 6039-6047 | official | 2110.10206 | title_snapshot |
10.1609/aaai.v37i1.25101 | KT-Net: Knowledge Transfer for Unpaired 3D Shape Completion | https://ojs.aaai.org/index.php/AAAI/article/view/25101 | https://ojs.aaai.org/index.php/AAAI/article/download/25101/24873 | [
"Zhen Cao",
"Wenxiao Zhang",
"Xin Wen",
"Zhen Dong",
"Yu-Shen Liu",
"Xiongwu Xiao",
"Bisheng Yang"
] | Unpaired 3D object completion aims to predict a complete 3D shape from an incomplete input without knowing the correspondence between the complete and incomplete shapes. In this paper, we propose the novel KTNet to solve this task from the new perspective of knowledge transfer. KTNet elaborates a teacher-assistant-stud... | main | null | 10.1609/aaai.v37i1.25101 | 37 | 1 | 286-294 | official | 2111.11976 | title_judge |
10.1609/aaai.v37i9.26324 | Denoising Multi-Similarity Formulation: A Self-Paced Curriculum-Driven Approach for Robust Metric Learning | https://ojs.aaai.org/index.php/AAAI/article/view/26324 | https://ojs.aaai.org/index.php/AAAI/article/download/26324/26096 | [
"Chenkang Zhang",
"Lei Luo",
"Bin Gu"
] | Deep Metric Learning (DML) is a group of techniques that aim to measure the similarity between objects through the neural network. Although the number of DML methods has rapidly increased in recent years, most previous studies cannot effectively handle noisy data, which commonly exists in practical applications and oft... | main | null | 10.1609/aaai.v37i9.26324 | 37 | 9 | 11183-11191 | official | 2211.11751 | title_snapshot |
10.1609/aaai.v37i8.26195 | Policy-Adaptive Estimator Selection for Off-Policy Evaluation | https://ojs.aaai.org/index.php/AAAI/article/view/26195 | https://ojs.aaai.org/index.php/AAAI/article/download/26195/25967 | [
"Takuma Udagawa",
"Haruka Kiyohara",
"Yusuke Narita",
"Yuta Saito",
"Kei Tateno"
] | Off-policy evaluation (OPE) aims to accurately evaluate the performance of counterfactual policies using only offline logged data. Although many estimators have been developed, there is no single estimator that dominates the others, because the estimators' accuracy can vary greatly depending on a given OPE task such as... | main | null | 10.1609/aaai.v37i8.26195 | 37 | 8 | 10025-10033 | official | 2211.13904 | title_snapshot |
10.1609/aaai.v37i6.25929 | Reinforced Approximate Exploratory Data Analysis | https://ojs.aaai.org/index.php/AAAI/article/view/25929 | https://ojs.aaai.org/index.php/AAAI/article/download/25929/25701 | [
"Shaddy Garg",
"Subrata Mitra",
"Tong Yu",
"Yash Gadhia",
"Arjun Kashettiwar"
] | Exploratory data analytics (EDA) is a sequential decision making process where analysts choose subsequent queries that might lead to some interesting insights based on the previous queries and corresponding results. Data processing systems often execute the queries on samples to produce results with low latency. Differ... | main | null | 10.1609/aaai.v37i6.25929 | 37 | 6 | 7660-7669 | official | 2212.06225 | title_snapshot |
10.1609/aaai.v37i9.26256 | GraphPrompt: Graph-Based Prompt Templates for Biomedical Synonym Prediction | https://ojs.aaai.org/index.php/AAAI/article/view/26256 | https://ojs.aaai.org/index.php/AAAI/article/download/26256/26028 | [
"Hanwen Xu",
"Jiayou Zhang",
"Zhirui Wang",
"Shizhuo Zhang",
"Megh Bhalerao",
"Yucong Liu",
"Dawei Zhu",
"Sheng Wang"
] | In the expansion of biomedical dataset, the same category may be labeled with different terms, thus being tedious and onerous to curate these terms. Therefore, automatically mapping synonymous terms onto the ontologies is desirable, which we name as biomedical synonym prediction task. Unlike biomedical concept normaliz... | main | null | 10.1609/aaai.v37i9.26256 | 37 | 9 | 10576-10584 | official | 2112.03002 | title_snapshot |
10.1609/aaai.v37i7.26063 | Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with Distractions | https://ojs.aaai.org/index.php/AAAI/article/view/26063 | https://ojs.aaai.org/index.php/AAAI/article/download/26063/25835 | [
"Qiyuan Liu",
"Qi Zhou",
"Rui Yang",
"Jie Wang"
] | Recent work has shown that representation learning plays a critical role in sample-efficient reinforcement learning (RL) from pixels. Unfortunately, in real-world scenarios, representation learning is usually fragile to task-irrelevant distractions such as variations in background or viewpoint. To tackle this problem, ... | main | null | 10.1609/aaai.v37i7.26063 | 37 | 7 | 8843-8851 | official | 2302.12003 | title_snapshot |
10.1609/aaai.v37i5.25738 | Extracting Semantic-Dynamic Features for Long-Term Stable Brain Computer Interface | https://ojs.aaai.org/index.php/AAAI/article/view/25738 | https://ojs.aaai.org/index.php/AAAI/article/download/25738/25510 | [
"Tao Fang",
"Qian Zheng",
"Yu Qi",
"Gang Pan"
] | Brain-computer Interface (BCI) builds a neural signal to the motor command pathway, which is a prerequisite for the realization of neural prosthetics. However, a long-term stable BCI suffers from the neural data drift across days while retraining the BCI decoder is expensive and restricts its application scenarios. Rec... | main | null | 10.1609/aaai.v37i5.25738 | 37 | 5 | 5965-5973 | official | null | null |
10.1609/aaai.v37i11.26633 | Quantum-Inspired Representation for Long-Tail Senses of Word Sense Disambiguation | https://ojs.aaai.org/index.php/AAAI/article/view/26633 | https://ojs.aaai.org/index.php/AAAI/article/download/26633/26405 | [
"Junwei Zhang",
"Ruifang He",
"Fengyu Guo"
] | Data imbalance, also known as the long-tail distribution of data, is an important challenge for data-driven models. In the Word Sense Disambiguation (WSD) task, the long-tail phenomenon of word sense distribution is more common, making it difficult to effectively represent and identify Long-Tail Senses (LTSs). Therefor... | main | null | 10.1609/aaai.v37i11.26633 | 37 | 11 | 13949-13957 | official | null | null |
10.1609/aaai.v37i6.25816 | Symbolic Metamodels for Interpreting Black-Boxes Using Primitive Functions | https://ojs.aaai.org/index.php/AAAI/article/view/25816 | https://ojs.aaai.org/index.php/AAAI/article/download/25816/25588 | [
"Mahed Abroshan",
"Saumitra Mishra",
"Mohammad Mahdi Khalili"
] | One approach for interpreting black-box machine learning models is to find a global approximation of the model using simple interpretable functions, which is called a metamodel (a model of the model). Approximating the black-box with a metamodel can be used to 1) estimate instance-wise feature importance; 2) understand... | main | null | 10.1609/aaai.v37i6.25816 | 37 | 6 | 6649-6657 | official | 2302.04791 | title_snapshot |
10.1609/aaai.v37i2.25352 | Defending Black-Box Skeleton-Based Human Activity Classifiers | https://ojs.aaai.org/index.php/AAAI/article/view/25352 | https://ojs.aaai.org/index.php/AAAI/article/download/25352/25124 | [
"He Wang",
"Yunfeng Diao",
"Zichang Tan",
"Guodong Guo"
] | Skeletal motions have been heavily relied upon for human activity recognition (HAR). Recently, a universal vulnerability of skeleton-based HAR has been identified across a variety of classifiers and data, calling for mitigation. To this end, we propose the first black-box defense method for skeleton-based HAR to our be... | main | null | 10.1609/aaai.v37i2.25352 | 37 | 2 | 2546-2554 | official | 2203.04713 | title_snapshot |
10.1609/aaai.v37i11.26503 | Cogito Ergo Summ: Abstractive Summarization of Biomedical Papers via Semantic Parsing Graphs and Consistency Rewards | https://ojs.aaai.org/index.php/AAAI/article/view/26503 | https://ojs.aaai.org/index.php/AAAI/article/download/26503/26275 | [
"Giacomo Frisoni",
"Paolo Italiani",
"Stefano Salvatori",
"Gianluca Moro"
] | The automatic synthesis of biomedical publications catalyzes a profound research interest elicited by literature congestion. Current sequence-to-sequence models mainly rely on the lexical surface and seldom consider the deep semantic interconnections between the entities mentioned in the source document. Such superfici... | main | null | 10.1609/aaai.v37i11.26503 | 37 | 11 | 12781-12789 | official | null | null |
10.1609/aaai.v37i11.26577 | RINK: Reader-Inherited Evidence Reranker for Table-and-Text Open Domain Question Answering | https://ojs.aaai.org/index.php/AAAI/article/view/26577 | https://ojs.aaai.org/index.php/AAAI/article/download/26577/26349 | [
"Eunhwan Park",
"Sung-Min Lee",
"Dearyong Seo",
"Seonhoon Kim",
"Inho Kang",
"Seung-Hoon Na"
] | Most approaches used in open-domain question answering on hybrid data that comprises both tabular-and-textual contents are based on a Retrieval-Reader pipeline in which the retrieval module finds relevant “heterogenous” evidence for a given question and the reader module generates an answer from the retrieved evidence.... | main | null | 10.1609/aaai.v37i11.26577 | 37 | 11 | 13446-13456 | official | null | null |
10.1609/aaai.v37i11.26617 | MoEC: Mixture of Expert Clusters | https://ojs.aaai.org/index.php/AAAI/article/view/26617 | https://ojs.aaai.org/index.php/AAAI/article/download/26617/26389 | [
"Yuan Xie",
"Shaohan Huang",
"Tianyu Chen",
"Furu Wei"
] | Sparsely Mixture of Experts (MoE) has received great interest due to its promising scaling capability with affordable computational overhead. MoE models convert dense layers into sparse experts, and utilize a gated routing network to make experts conditionally activated. However, as the number of experts grows, MoE wit... | main | null | 10.1609/aaai.v37i11.26617 | 37 | 11 | 13807-13815 | official | 2207.09094 | title_snapshot |
10.1609/aaai.v37i10.26403 | Equity Promotion in Public Transportation | https://ojs.aaai.org/index.php/AAAI/article/view/26403 | https://ojs.aaai.org/index.php/AAAI/article/download/26403/26175 | [
"Anik Pramanik",
"Pan Xu",
"Yifan Xu"
] | There are many news articles reporting the obstacles confronting poverty-stricken households in access to public transits. These barriers create a great deal of inconveniences for these impoverished families and more importantly, they contribute a lot of social inequalities. A typical approach addressing the issue is t... | main | null | 10.1609/aaai.v37i10.26403 | 37 | 10 | 11890-11898 | official | 2211.14531 | title_snapshot |
10.1609/aaai.v37i4.25622 | GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classification | https://ojs.aaai.org/index.php/AAAI/article/view/25622 | https://ojs.aaai.org/index.php/AAAI/article/download/25622/25394 | [
"Mengting Zhou",
"Zhiguo Gong"
] | Graph neural networks (GNNs) have achieved great success in node classification tasks. However, existing GNNs naturally bias towards the majority classes with more labelled data and ignore those minority classes with relatively few labelled ones. The traditional techniques often resort over-sampling methods, but they m... | main | null | 10.1609/aaai.v37i4.25622 | 37 | 4 | 4954-4962 | official | 2302.12814 | title_snapshot |
10.1609/aaai.v37i4.25641 | Online Symbolic Regression with Informative Query | https://ojs.aaai.org/index.php/AAAI/article/view/25641 | https://ojs.aaai.org/index.php/AAAI/article/download/25641/25413 | [
"Pengwei Jin",
"Di Huang",
"Rui Zhang",
"Xing Hu",
"Ziyuan Nan",
"Zidong Du",
"Qi Guo",
"Yunji Chen"
] | Symbolic regression, the task of extracting mathematical expressions from the observed data, plays a crucial role in scientific discovery. Despite the promising performance of existing methods, most of them conduct symbolic regression in an offline setting. That is, they treat the observed data points as given ones tha... | main | null | 10.1609/aaai.v37i4.25641 | 37 | 4 | 5122-5130 | official | 2302.10539 | title_snapshot |
10.1609/aaai.v37i1.25193 | Bidirectional Domain Mixup for Domain Adaptive Semantic Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/25193 | https://ojs.aaai.org/index.php/AAAI/article/download/25193/24965 | [
"Daehan Kim",
"Minseok Seo",
"Kwanyong Park",
"Inkyu Shin",
"Sanghyun Woo",
"In So Kweon",
"Dong-Geol Choi"
] | Mixup provides interpolated training samples and allows the model to obtain smoother decision boundaries for better generalization. The idea can be naturally applied to the domain adaptation task, where we can mix the source and target samples to obtain domain-mixed samples for better adaptation. However, the extension... | main | null | 10.1609/aaai.v37i1.25193 | 37 | 1 | 1114-1123 | official | 2303.09779 | title_snapshot |
10.1609/aaai.v37i3.25479 | Learning Semantic Degradation-Aware Guidance for Recognition-Driven Unsupervised Low-Light Image Enhancement | https://ojs.aaai.org/index.php/AAAI/article/view/25479 | https://ojs.aaai.org/index.php/AAAI/article/download/25479/25251 | [
"Naishan Zheng",
"Jie Huang",
"Man Zhou",
"Zizheng Yang",
"Qi Zhu",
"Feng Zhao"
] | Low-light images suffer severe degradation of low lightness and noise corruption, causing unsatisfactory visual quality and visual recognition performance. To solve this problem while meeting the unavailability of paired datasets in wide-range scenarios, unsupervised low-light image enhancement (ULLIE) techniques have ... | main | null | 10.1609/aaai.v37i3.25479 | 37 | 3 | 3678-3686 | official | null | null |
10.1609/aaai.v37i2.25261 | DQ-DETR: Dual Query Detection Transformer for Phrase Extraction and Grounding | https://ojs.aaai.org/index.php/AAAI/article/view/25261 | https://ojs.aaai.org/index.php/AAAI/article/download/25261/25033 | [
"Shilong Liu",
"Shijia Huang",
"Feng Li",
"Hao Zhang",
"Yaoyuan Liang",
"Hang Su",
"Jun Zhu",
"Lei Zhang"
] | In this paper, we study the problem of visual grounding by considering both phrase extraction and grounding (PEG). In contrast to the previous phrase-known-at-test setting, PEG requires a model to extract phrases from text and locate objects from image simultaneously, which is a more practical setting in real applicati... | main | null | 10.1609/aaai.v37i2.25261 | 37 | 2 | 1728-1736 | official | 2211.15516 | title_snapshot |
10.1609/aaai.v37i8.26160 | Representation Learning by Detecting Incorrect Location Embeddings | https://ojs.aaai.org/index.php/AAAI/article/view/26160 | https://ojs.aaai.org/index.php/AAAI/article/download/26160/25932 | [
"Sepehr Sameni",
"Simon Jenni",
"Paolo Favaro"
] | In this paper, we introduce a novel self-supervised learning (SSL) loss for image representation learning. There is a growing belief that generalization in deep neural networks is linked to their ability to discriminate object shapes. Since object shape is related to the location of its parts, we propose to detect thos... | main | null | 10.1609/aaai.v37i8.26160 | 37 | 8 | 9704-9713 | official | 2204.04788 | title_snapshot |
10.1609/aaai.v37i11.26611 | Continual Graph Convolutional Network for Text Classification | https://ojs.aaai.org/index.php/AAAI/article/view/26611 | https://ojs.aaai.org/index.php/AAAI/article/download/26611/26383 | [
"Tiandeng Wu",
"Qijiong Liu",
"Yi Cao",
"Yao Huang",
"Xiao-Ming Wu",
"Jiandong Ding"
] | Graph convolutional network (GCN) has been successfully applied to capture global non-consecutive and long-distance semantic information for text classification. However, while GCN-based methods have shown promising results in offline evaluations, they commonly follow a seen-token-seen-document paradigm by constructing... | main | null | 10.1609/aaai.v37i11.26611 | 37 | 11 | 13754-13762 | official | 2304.04152 | title_snapshot |
10.1609/aaai.v37i1.25175 | Boosting Point Clouds Rendering via Radiance Mapping | https://ojs.aaai.org/index.php/AAAI/article/view/25175 | https://ojs.aaai.org/index.php/AAAI/article/download/25175/24947 | [
"Xiaoyang Huang",
"Yi Zhang",
"Bingbing Ni",
"Teng Li",
"Kai Chen",
"Wenjun Zhang"
] | Recent years we have witnessed rapid development in NeRF-based image rendering due to its high quality. However, point clouds rendering is somehow less explored. Compared to NeRF-based rendering which suffers from dense spatial sampling, point clouds rendering is naturally less computation intensive, which enables its ... | main | null | 10.1609/aaai.v37i1.25175 | 37 | 1 | 953-961 | official | 2210.15107 | title_snapshot |
10.1609/aaai.v37i7.25987 | Identifying Selection Bias from Observational Data | https://ojs.aaai.org/index.php/AAAI/article/view/25987 | https://ojs.aaai.org/index.php/AAAI/article/download/25987/25759 | [
"David Kaltenpoth",
"Jilles Vreeken"
] | Access to a representative sample from the population is an assumption that underpins all of machine learning. Selection effects can cause observations to instead come from a subpopulation, by which our inferences may be subject to bias. It is therefore important to know whether or not a sample is affected by selection... | main | null | 10.1609/aaai.v37i7.25987 | 37 | 7 | 8177-8185 | official | null | null |
10.1609/aaai.v37i4.25619 | Causal Conditional Hidden Markov Model for Multimodal Traffic Prediction | https://ojs.aaai.org/index.php/AAAI/article/view/25619 | https://ojs.aaai.org/index.php/AAAI/article/download/25619/25391 | [
"Yu Zhao",
"Pan Deng",
"Junting Liu",
"Xiaofeng Jia",
"Mulan Wang"
] | Multimodal traffic flow can reflect the health of the transportation system, and its prediction is crucial to urban traffic management. Recent works overemphasize spatio-temporal correlations of traffic flow, ignoring the physical concepts that lead to the generation of observations and their causal relationship. Spati... | main | null | 10.1609/aaai.v37i4.25619 | 37 | 4 | 4929-4936 | official | 2301.08249 | title_snapshot |
10.1609/aaai.v37i7.25988 | PIXEL: Physics-Informed Cell Representations for Fast and Accurate PDE Solvers | https://ojs.aaai.org/index.php/AAAI/article/view/25988 | https://ojs.aaai.org/index.php/AAAI/article/download/25988/25760 | [
"Namgyu Kang",
"Byeonghyeon Lee",
"Youngjoon Hong",
"Seok-Bae Yun",
"Eunbyung Park"
] | With the increases in computational power and advances in machine learning, data-driven learning-based methods have gained significant attention in solving PDEs. Physics-informed neural networks (PINNs) have recently emerged and succeeded in various forward and inverse PDE problems thanks to their excellent properties,... | main | null | 10.1609/aaai.v37i7.25988 | 37 | 7 | 8186-8194 | official | 2207.12800 | title_snapshot |
10.1609/aaai.v37i9.26238 | MetaZSCIL: A Meta-Learning Approach for Generalized Zero-Shot Class Incremental Learning | https://ojs.aaai.org/index.php/AAAI/article/view/26238 | https://ojs.aaai.org/index.php/AAAI/article/download/26238/26010 | [
"Yanan Wu",
"Tengfei Liang",
"Songhe Feng",
"Yi Jin",
"Gengyu Lyu",
"Haojun Fei",
"Yang Wang"
] | Generalized zero-shot learning (GZSL) aims to recognize samples whose categories may not have been seen at training. Standard GZSL cannot handle dynamic addition of new seen and unseen classes. In order to address this limitation, some recent attempts have been made to develop continual GZSL methods. However, these met... | main | null | 10.1609/aaai.v37i9.26238 | 37 | 9 | 10408-10416 | official | null | null |
10.1609/aaai.v37i1.25201 | InstanceFormer: An Online Video Instance Segmentation Framework | https://ojs.aaai.org/index.php/AAAI/article/view/25201 | https://ojs.aaai.org/index.php/AAAI/article/download/25201/24973 | [
"Rajat Koner",
"Tanveer Hannan",
"Suprosanna Shit",
"Sahand Sharifzadeh",
"Matthias Schubert",
"Thomas Seidl",
"Volker Tresp"
] | Recent transformer-based offline video instance segmentation (VIS) approaches achieve encouraging results and significantly outperform online approaches. However, their reliance on the whole video and the immense computational complexity caused by full Spatio-temporal attention limit them in real-life applications such... | main | null | 10.1609/aaai.v37i1.25201 | 37 | 1 | 1188-1195 | official | 2208.10547 | title_snapshot |
10.1609/aaai.v37i2.25317 | Adaptive Dynamic Filtering Network for Image Denoising | https://ojs.aaai.org/index.php/AAAI/article/view/25317 | https://ojs.aaai.org/index.php/AAAI/article/download/25317/25089 | [
"Hao Shen",
"Zhong-Qiu Zhao",
"Wandi Zhang"
] | In image denoising networks, feature scaling is widely used to enlarge the receptive field size and reduce computational costs. This practice, however, also leads to the loss of high-frequency information and fails to consider within-scale characteristics. Recently, dynamic convolution has exhibited powerful capabiliti... | main | null | 10.1609/aaai.v37i2.25317 | 37 | 2 | 2227-2235 | official | 2211.12051 | title_snapshot |
10.1609/aaai.v37i11.26498 | Domain-Adapted Dependency Parsing for Cross-Domain Named Entity Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/26498 | https://ojs.aaai.org/index.php/AAAI/article/download/26498/26270 | [
"Chenxiao Dou",
"Xianghui Sun",
"Yaoshu Wang",
"Yunjie Ji",
"Baochang Ma",
"Xiangang Li"
] | In recent years, many researchers have leveraged structural information from dependency trees to improve Named Entity Recognition (NER). Most of their methods take dependency-tree labels as input features for NER model training. However, such dependency information is not inherently provided in most NER corpora, making... | main | null | 10.1609/aaai.v37i11.26498 | 37 | 11 | 12737-12744 | official | null | null |
10.1609/aaai.v37i3.25435 | Unbiased Heterogeneous Scene Graph Generation with Relation-Aware Message Passing Neural Network | https://ojs.aaai.org/index.php/AAAI/article/view/25435 | https://ojs.aaai.org/index.php/AAAI/article/download/25435/25207 | [
"Kanghoon Yoon",
"Kibum Kim",
"Jinyoung Moon",
"Chanyoung Park"
] | Recent scene graph generation (SGG) frameworks have focused on learning complex relationships among multiple objects in an image. Thanks to the nature of the message passing neural network (MPNN) that models high-order interactions between objects and their neighboring objects, they are dominant representation learning... | main | null | 10.1609/aaai.v37i3.25435 | 37 | 3 | 3285-3294 | official | 2212.00443 | title_snapshot |
10.1609/aaai.v37i3.25496 | Debiased Fine-Tuning for Vision-Language Models by Prompt Regularization | https://ojs.aaai.org/index.php/AAAI/article/view/25496 | https://ojs.aaai.org/index.php/AAAI/article/download/25496/25268 | [
"Beier Zhu",
"Yulei Niu",
"Saeil Lee",
"Minhoe Hur",
"Hanwang Zhang"
] | We present a new paradigm for fine-tuning large-scale vision-language pre-trained models on downstream task, dubbed Prompt Regularization (ProReg). Different from traditional fine-tuning which easily overfits to the downstream task data, ProReg uses the prediction by prompting the pretrained model to regularize the fin... | main | null | 10.1609/aaai.v37i3.25496 | 37 | 3 | 3834-3842 | official | 2301.12429 | title_snapshot |
10.1609/aaai.v37i4.25520 | Self-Supervised Primal-Dual Learning for Constrained Optimization | https://ojs.aaai.org/index.php/AAAI/article/view/25520 | https://ojs.aaai.org/index.php/AAAI/article/download/25520/25292 | [
"Seonho Park",
"Pascal Van Hentenryck"
] | This paper studies how to train machine-learning models that directly approximate the optimal solutions of constrained optimization problems. This is an empirical risk minimization under constraints, which is challenging as training must balance optimality and feasibility conditions. Supervised learning methods often a... | main | null | 10.1609/aaai.v37i4.25520 | 37 | 4 | 4052-4060 | official | 2208.09046 | title_snapshot |
10.1609/aaai.v37i11.26515 | A Simple Yet Effective Subsequence-Enhanced Approach for Cross-Domain NER | https://ojs.aaai.org/index.php/AAAI/article/view/26515 | https://ojs.aaai.org/index.php/AAAI/article/download/26515/26287 | [
"Jinpeng Hu",
"DanDan Guo",
"Yang Liu",
"Zhuo Li",
"Zhihong Chen",
"Xiang Wan",
"Tsung-Hui Chang"
] | Cross-domain named entity recognition (NER), aiming to address the limitation of labeled resources in the target domain, is a challenging yet important task. Most existing studies alleviate the data discrepancy across different domains at the coarse level via combing NER with language modelings or introducing domain-ad... | main | null | 10.1609/aaai.v37i11.26515 | 37 | 11 | 12890-12898 | official | null | null |
10.1609/aaai.v37i3.25443 | Cyclically Disentangled Feature Translation for Face Anti-spoofing | https://ojs.aaai.org/index.php/AAAI/article/view/25443 | https://ojs.aaai.org/index.php/AAAI/article/download/25443/25215 | [
"Haixiao Yue",
"Keyao Wang",
"Guosheng Zhang",
"Haocheng Feng",
"Junyu Han",
"Errui Ding",
"Jingdong Wang"
] | Current domain adaptation methods for face anti-spoofing leverage labeled source domain data and unlabeled target domain data to obtain a promising generalizable decision boundary. However, it is usually difficult for these methods to achieve a perfect domain-invariant liveness feature disentanglement, which may degrad... | main | null | 10.1609/aaai.v37i3.25443 | 37 | 3 | 3358-3366 | official | 2212.03651 | title_snapshot |
10.1609/aaai.v37i11.26475 | Generalized Category Discovery with Decoupled Prototypical Network | https://ojs.aaai.org/index.php/AAAI/article/view/26475 | https://ojs.aaai.org/index.php/AAAI/article/download/26475/26247 | [
"Wenbin An",
"Feng Tian",
"Qinghua Zheng",
"Wei Ding",
"Qianying Wang",
"Ping Chen"
] | Generalized Category Discovery (GCD) aims to recognize both known and novel categories from a set of unlabeled data, based on another dataset labeled with only known categories. Without considering differences between known and novel categories, current methods learn about them in a coupled manner, which can hurt model... | main | null | 10.1609/aaai.v37i11.26475 | 37 | 11 | 12527-12535 | official | 2211.15115 | title_snapshot |
10.1609/aaai.v37i1.25170 | Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning | https://ojs.aaai.org/index.php/AAAI/article/view/25170 | https://ojs.aaai.org/index.php/AAAI/article/download/25170/24942 | [
"Bingchen Huang",
"Zhineng Chen",
"Peng Zhou",
"Jiayin Chen",
"Zuxuan Wu"
] | The dynamic expansion architecture is becoming popular in class incremental learning, mainly due to its advantages in alleviating catastrophic forgetting. However, task confu- sion is not well assessed within this framework, e.g., the discrepancy between classes of different tasks is not well learned (i.e., inter-task ... | main | null | 10.1609/aaai.v37i1.25170 | 37 | 1 | 908-916 | official | 2212.14284 | title_snapshot |
10.1609/aaai.v37i4.25662 | Molformer: Motif-Based Transformer on 3D Heterogeneous Molecular Graphs | https://ojs.aaai.org/index.php/AAAI/article/view/25662 | https://ojs.aaai.org/index.php/AAAI/article/download/25662/25434 | [
"Fang Wu",
"Dragomir Radev",
"Stan Z. Li"
] | Procuring expressive molecular representations underpins AI-driven molecule design and scientific discovery. The research mainly focuses on atom-level homogeneous molecular graphs, ignoring the rich information in subgraphs or motifs. However, it has been widely accepted that substructures play a dominant role in ident... | main | null | 10.1609/aaai.v37i4.25662 | 37 | 4 | 5312-5320 | official | 2110.01191 | title_snapshot |
10.1609/aaai.v37i9.26309 | Joint Multimodal Entity-Relation Extraction Based on Edge-Enhanced Graph Alignment Network and Word-Pair Relation Tagging | https://ojs.aaai.org/index.php/AAAI/article/view/26309 | https://ojs.aaai.org/index.php/AAAI/article/download/26309/26081 | [
"Li Yuan",
"Yi Cai",
"Jin Wang",
"Qing Li"
] | Multimodal named entity recognition (MNER) and multimodal relation extraction (MRE) are two fundamental subtasks in the multimodal knowledge graph construction task. However, the existing methods usually handle two tasks independently, which ignores the bidirectional interaction between them. This paper is the first to... | main | null | 10.1609/aaai.v37i9.26309 | 37 | 9 | 11051-11059 | official | 2211.15028 | title_snapshot |
10.1609/aaai.v37i10.26401 | XRand: Differentially Private Defense against Explanation-Guided Attacks | https://ojs.aaai.org/index.php/AAAI/article/view/26401 | https://ojs.aaai.org/index.php/AAAI/article/download/26401/26173 | [
"Truc Nguyen",
"Phung Lai",
"Hai Phan",
"My T. Thai"
] | Recent development in the field of explainable artificial intelligence (XAI) has helped improve trust in Machine-Learning-as-a-Service (MLaaS) systems, in which an explanation is provided together with the model prediction in response to each query. However, XAI also opens a door for adversaries to gain insights into t... | main | null | 10.1609/aaai.v37i10.26401 | 37 | 10 | 11873-11881 | official | 2212.04454 | title_snapshot |
10.1609/aaai.v37i5.25743 | SWL-Adapt: An Unsupervised Domain Adaptation Model with Sample Weight Learning for Cross-User Wearable Human Activity Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/25743 | https://ojs.aaai.org/index.php/AAAI/article/download/25743/25515 | [
"Rong Hu",
"Ling Chen",
"Shenghuan Miao",
"Xing Tang"
] | In practice, Wearable Human Activity Recognition (WHAR) models usually face performance degradation on the new user due to user variance. Unsupervised domain adaptation (UDA) becomes the natural solution to cross-user WHAR under annotation scarcity. Existing UDA models usually align samples across domains without diffe... | main | null | 10.1609/aaai.v37i5.25743 | 37 | 5 | 6012-6020 | official | 2212.00724 | title_snapshot |
10.1609/aaai.v37i4.25582 | Scaling Law for Recommendation Models: Towards General-Purpose User Representations | https://ojs.aaai.org/index.php/AAAI/article/view/25582 | https://ojs.aaai.org/index.php/AAAI/article/download/25582/25354 | [
"Kyuyong Shin",
"Hanock Kwak",
"Su Young Kim",
"Max Nihlén Ramström",
"Jisu Jeong",
"Jung-Woo Ha",
"Kyung-Min Kim"
] | Recent advancement of large-scale pretrained models such as BERT, GPT-3, CLIP, and Gopher, has shown astonishing achievements across various task domains. Unlike vision recognition and language models, studies on general-purpose user representation at scale still remain underexplored. Here we explore the possibility of... | main | null | 10.1609/aaai.v37i4.25582 | 37 | 4 | 4596-4604 | official | 2111.11294 | title_snapshot |
10.1609/aaai.v37i7.26069 | EASAL: Entity-Aware Subsequence-Based Active Learning for Named Entity Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/26069 | https://ojs.aaai.org/index.php/AAAI/article/download/26069/25841 | [
"Yang Liu",
"Jinpeng Hu",
"Zhihong Chen",
"Xiang Wan",
"Tsung-Hui Chang"
] | Active learning is a critical technique for reducing labelling load by selecting the most informative data. Most previous works applied active learning on Named Entity Recognition (token-level task) similar to the text classification (sentence-level task). They failed to consider the heterogeneity of uncertainty within... | main | null | 10.1609/aaai.v37i7.26069 | 37 | 7 | 8897-8905 | official | null | null |
10.1609/aaai.v37i1.25132 | Few-Shot Defect Image Generation via Defect-Aware Feature Manipulation | https://ojs.aaai.org/index.php/AAAI/article/view/25132 | https://ojs.aaai.org/index.php/AAAI/article/download/25132/24904 | [
"Yuxuan Duan",
"Yan Hong",
"Li Niu",
"Liqing Zhang"
] | The performances of defect inspection have been severely hindered by insufficient defect images in industries, which can be alleviated by generating more samples as data augmentation. We propose the first defect image generation method in the challenging few-shot cases. Given just a handful of defect images and relativ... | main | null | 10.1609/aaai.v37i1.25132 | 37 | 1 | 571-578 | official | 2303.02389 | title_snapshot |
10.1609/aaai.v37i6.25861 | Context-Aware Safe Medication Recommendations with Molecular Graph and DDI Graph Embedding | https://ojs.aaai.org/index.php/AAAI/article/view/25861 | https://ojs.aaai.org/index.php/AAAI/article/download/25861/25633 | [
"Qianyu Chen",
"Xin Li",
"Kunnan Geng",
"Mingzhong Wang"
] | Molecular structures and Drug-Drug Interactions (DDI) are recognized as important knowledge to guide medication recommendation (MR) tasks, and medical concept embedding has been applied to boost their performance. Though promising performance has been achieved by leveraging Graph Neural Network (GNN) models to encode t... | main | null | 10.1609/aaai.v37i6.25861 | 37 | 6 | 7053-7060 | official | null | null |
10.1609/aaai.v37i6.25845 | InParformer: Evolutionary Decomposition Transformers with Interactive Parallel Attention for Long-Term Time Series Forecasting | https://ojs.aaai.org/index.php/AAAI/article/view/25845 | https://ojs.aaai.org/index.php/AAAI/article/download/25845/25617 | [
"Haizhou Cao",
"Zhenhao Huang",
"Tiechui Yao",
"Jue Wang",
"Hui He",
"Yangang Wang"
] | Long-term time series forecasting (LTSF) provides substantial benefits for numerous real-world applications, whereas places essential demands on the model capacity to capture long-range dependencies. Recent Transformer-based models have significantly improved LTSF performance. It is worth noting that Transformer with t... | main | null | 10.1609/aaai.v37i6.25845 | 37 | 6 | 6906-6915 | official | null | null |
10.1609/aaai.v37i1.25215 | Real-World Deep Local Motion Deblurring | https://ojs.aaai.org/index.php/AAAI/article/view/25215 | https://ojs.aaai.org/index.php/AAAI/article/download/25215/24987 | [
"Haoying Li",
"Ziran Zhang",
"Tingting Jiang",
"Peng Luo",
"Huajun Feng",
"Zhihai Xu"
] | Most existing deblurring methods focus on removing global blur caused by camera shake, while they cannot well handle local blur caused by object movements. To fill the vacancy of local deblurring in real scenes, we establish the first real local motion blur dataset (ReLoBlur), which is captured by a synchronized beam-s... | main | null | 10.1609/aaai.v37i1.25215 | 37 | 1 | 1314-1322 | official | 2204.08179 | title_snapshot |
10.1609/aaai.v37i1.25156 | Weakly-Supervised Camouflaged Object Detection with Scribble Annotations | https://ojs.aaai.org/index.php/AAAI/article/view/25156 | https://ojs.aaai.org/index.php/AAAI/article/download/25156/24928 | [
"Ruozhen He",
"Qihua Dong",
"Jiaying Lin",
"Rynson W.H. Lau"
] | Existing camouflaged object detection (COD) methods rely heavily on large-scale datasets with pixel-wise annotations. However, due to the ambiguous boundary, annotating camouflage objects pixel-wisely is very time-consuming and labor-intensive, taking ~60mins to label one image. In this paper, we propose the first weak... | main | null | 10.1609/aaai.v37i1.25156 | 37 | 1 | 781-789 | official | 2207.14083 | title_snapshot |
10.1609/aaai.v37i3.25430 | DPText-DETR: Towards Better Scene Text Detection with Dynamic Points in Transformer | https://ojs.aaai.org/index.php/AAAI/article/view/25430 | https://ojs.aaai.org/index.php/AAAI/article/download/25430/25202 | [
"Maoyuan Ye",
"Jing Zhang",
"Shanshan Zhao",
"Juhua Liu",
"Bo Du",
"Dacheng Tao"
] | Recently, Transformer-based methods, which predict polygon points or Bezier curve control points for localizing texts, are popular in scene text detection. However, these methods built upon detection transformer framework might achieve sub-optimal training efficiency and performance due to coarse positional query model... | main | null | 10.1609/aaai.v37i3.25430 | 37 | 3 | 3241-3249 | official | 2207.04491 | title_snapshot |
10.1609/aaai.v37i4.25670 | Bootstrapping Multi-View Representations for Fake News Detection | https://ojs.aaai.org/index.php/AAAI/article/view/25670 | https://ojs.aaai.org/index.php/AAAI/article/download/25670/25442 | [
"Qichao Ying",
"Xiaoxiao Hu",
"Yangming Zhou",
"Zhenxing Qian",
"Dan Zeng",
"Shiming Ge"
] | Previous researches on multimedia fake news detection include a series of complex feature extraction and fusion networks to gather useful information from the news. However, how cross-modal consistency relates to the fidelity of news and how features from different modalities affect the decision-making are still open q... | main | null | 10.1609/aaai.v37i4.25670 | 37 | 4 | 5384-5392 | official | 2206.05741 | title_snapshot |
10.1609/aaai.v37i6.25854 | NHITS: Neural Hierarchical Interpolation for Time Series Forecasting | https://ojs.aaai.org/index.php/AAAI/article/view/25854 | https://ojs.aaai.org/index.php/AAAI/article/download/25854/25626 | [
"Cristian Challu",
"Kin G. Olivares",
"Boris N. Oreshkin",
"Federico Garza Ramirez",
"Max Mergenthaler Canseco",
"Artur Dubrawski"
] | Recent progress in neural forecasting accelerated improvements in the performance of large-scale forecasting systems. Yet, long-horizon forecasting remains a very difficult task. Two common challenges afflicting the task are the volatility of the predictions and their computational complexity. We introduce NHITS, a mod... | main | null | 10.1609/aaai.v37i6.25854 | 37 | 6 | 6989-6997 | official | 2201.12886 | title_judge |
10.1609/aaai.v37i7.26020 | Bespoke: A Block-Level Neural Network Optimization Framework for Low-Cost Deployment | https://ojs.aaai.org/index.php/AAAI/article/view/26020 | https://ojs.aaai.org/index.php/AAAI/article/download/26020/25792 | [
"Jong-Ryul Lee",
"Yong-Hyuk Moon"
] | As deep learning models become popular, there is a lot of need for deploying them to diverse device environments. Because it is costly to develop and optimize a neural network for every single environment, there is a line of research to search neural networks for multiple target environments efficiently. However, exist... | main | null | 10.1609/aaai.v37i7.26020 | 37 | 7 | 8465-8472 | official | 2303.01913 | title_snapshot |
10.1609/aaai.v37i5.25777 | A Structural Complexity Analysis of Synchronous Dynamical Systems | https://ojs.aaai.org/index.php/AAAI/article/view/25777 | https://ojs.aaai.org/index.php/AAAI/article/download/25777/25549 | [
"Eduard Eiben",
"Robert Ganian",
"Thekla Hamm",
"Viktoriia Korchemna"
] | Synchronous dynamical systems are well-established models that have been used to capture a range of phenomena in networks, including opinion diffusion, spread of disease and product adoption. We study the three most notable problems in synchronous dynamical systems: whether the system will transition to a target config... | main | null | 10.1609/aaai.v37i5.25777 | 37 | 5 | 6313-6321 | official | 2312.08385 | title_snapshot |
10.1609/aaai.v37i5.25773 | Epistemic Disjunctive Datalog for Querying Knowledge Bases | https://ojs.aaai.org/index.php/AAAI/article/view/25773 | https://ojs.aaai.org/index.php/AAAI/article/download/25773/25545 | [
"Gianluca Cima",
"Marco Console",
"Maurizio Lenzerini",
"Antonella Poggi"
] | The Datalog query language can express several powerful recursive properties, often crucial in real-world scenarios. While answering such queries is feasible over relational databases, the picture changes dramatically when data is enriched with intensional knowledge. It is indeed well-known that answering Datalog queri... | main | null | 10.1609/aaai.v37i5.25773 | 37 | 5 | 6280-6288 | official | null | null |
10.1609/aaai.v37i8.26128 | Scaling Marginalized Importance Sampling to High-Dimensional State-Spaces via State Abstraction | https://ojs.aaai.org/index.php/AAAI/article/view/26128 | https://ojs.aaai.org/index.php/AAAI/article/download/26128/25900 | [
"Brahma S. Pavse",
"Josiah P. Hanna"
] | We consider the problem of off-policy evaluation (OPE) in reinforcement learning (RL), where the goal is to estimate the performance of an evaluation policy, pie, using a fixed dataset, D, collected by one or more policies that may be different from pie. Current OPE algorithms may produce poor OPE estimates under polic... | main | null | 10.1609/aaai.v37i8.26128 | 37 | 8 | 9417-9425 | official | 2212.07486 | title_snapshot |
10.1609/aaai.v37i8.26176 | Adaptive Mixing of Auxiliary Losses in Supervised Learning | https://ojs.aaai.org/index.php/AAAI/article/view/26176 | https://ojs.aaai.org/index.php/AAAI/article/download/26176/25948 | [
"Durga Sivasubramanian",
"Ayush Maheshwari",
"Prathosh AP",
"Pradeep Shenoy",
"Ganesh Ramakrishnan"
] | In many supervised learning scenarios, auxiliary losses are used in order to introduce additional information or constraints into the supervised learning objective. For instance, knowledge distillation aims to mimic outputs of a powerful teacher model; similarly, in rule-based approaches, weak labeling information is p... | main | null | 10.1609/aaai.v37i8.26176 | 37 | 8 | 9855-9863 | official | 2202.03250 | title_snapshot |
10.1609/aaai.v37i8.26147 | Automata Cascades: Expressivity and Sample Complexity | https://ojs.aaai.org/index.php/AAAI/article/view/26147 | https://ojs.aaai.org/index.php/AAAI/article/download/26147/25919 | [
"Alessandro Ronca",
"Nadezda Alexandrovna Knorozova",
"Giuseppe De Giacomo"
] | Every automaton can be decomposed into a cascade of basic prime automata. This is the Prime Decomposition Theorem by Krohn and Rhodes. Guided by this theory, we propose automata cascades as a structured, modular, way to describe automata as complex systems made of many components, each implementing a specific functiona... | main | null | 10.1609/aaai.v37i8.26147 | 37 | 8 | 9588-9595 | official | 2211.14028 | title_snapshot |
10.1609/aaai.v37i5.25690 | Game Implementation: What Are the Obstructions? | https://ojs.aaai.org/index.php/AAAI/article/view/25690 | https://ojs.aaai.org/index.php/AAAI/article/download/25690/25462 | [
"Jiehua Chen",
"Seyedeh Negar Layegh Khavidaki",
"Sebastian Vincent Haydn",
"Sofia Simola",
"Manuel Sorge"
] | In many applications, we want to influence the decisions of independent agents by designing incentives for their actions. We revisit a fundamental problem in this area, called GAME IMPLEMENTATION: Given a game in standard form and a set of desired strategies, can we design a set of payment promises such that if the pla... | main | null | 10.1609/aaai.v37i5.25690 | 37 | 5 | 5557-5564 | official | 2212.00699 | title_snapshot |
10.1609/aaai.v37i11.26490 | Preference-Controlled Multi-Objective Reinforcement Learning for Conditional Text Generation | https://ojs.aaai.org/index.php/AAAI/article/view/26490 | https://ojs.aaai.org/index.php/AAAI/article/download/26490/26262 | [
"Wenqing Chen",
"Jidong Tian",
"Caoyun Fan",
"Yitian Li",
"Hao He",
"Yaohui Jin"
] | Conditional text generation is to generate text sequences conditioning on linguistic or non-linguistic data. The main line of existing work proposed deterministic models to improve the fidelity of the generated text but often ignored the diversity. Another line relied on conditional variational auto-encoders (CVAEs), w... | main | null | 10.1609/aaai.v37i11.26490 | 37 | 11 | 12662-12672 | official | null | null |
10.1609/aaai.v37i5.25791 | Common Knowledge of Abstract Groups | https://ojs.aaai.org/index.php/AAAI/article/view/25791 | https://ojs.aaai.org/index.php/AAAI/article/download/25791/25563 | [
"Merlin Humml",
"Lutz Schröder"
] | Epistemic logics typically talk about knowledge of individual agents or groups of explicitly listed agents. Often, however, one wishes to express knowledge of groups of agents specified by a given property, as in ‘it is common knowledge among economists’. We introduce such a logic of common knowledge, which we term abs... | main | null | 10.1609/aaai.v37i5.25791 | 37 | 5 | 6434-6441 | official | 2211.16284 | title_snapshot |
10.1609/aaai.v37i8.26219 | USER: Unsupervised Structural Entropy-Based Robust Graph Neural Network | https://ojs.aaai.org/index.php/AAAI/article/view/26219 | https://ojs.aaai.org/index.php/AAAI/article/download/26219/25991 | [
"Yifei Wang",
"Yupan Wang",
"Zeyu Zhang",
"Song Yang",
"Kaiqi Zhao",
"Jiamou Liu"
] | Unsupervised/self-supervised graph neural networks (GNN) are susceptible to the inherent randomness in the input graph data, which adversely affects the model's performance in downstream tasks. In this paper, we propose USER, an unsupervised and robust version of GNN based on structural entropy, to alleviate the interf... | main | null | 10.1609/aaai.v37i8.26219 | 37 | 8 | 10235-10243 | official | 2302.05889 | title_snapshot |
10.1609/aaai.v37i6.25908 | Diffeomorphic Information Neural Estimation | https://ojs.aaai.org/index.php/AAAI/article/view/25908 | https://ojs.aaai.org/index.php/AAAI/article/download/25908/25680 | [
"Bao Duong",
"Thin Nguyen"
] | Mutual Information (MI) and Conditional Mutual Information (CMI) are multi-purpose tools from information theory that are able to naturally measure the statistical dependencies between random variables, thus they are usually of central interest in several statistical and machine learning tasks, such as conditional inde... | main | null | 10.1609/aaai.v37i6.25908 | 37 | 6 | 7468-7475 | official | 2211.10856 | title_snapshot |
10.1609/aaai.v37i2.25304 | Exposing the Self-Supervised Space-Time Correspondence Learning via Graph Kernels | https://ojs.aaai.org/index.php/AAAI/article/view/25304 | https://ojs.aaai.org/index.php/AAAI/article/download/25304/25076 | [
"Zheyun Qin",
"Xiankai Lu",
"Xiushan Nie",
"Yilong Yin",
"Jianbing Shen"
] | Self-supervised space-time correspondence learning is emerging as a promising way of leveraging unlabeled video. Currently, most methods adapt contrastive learning with mining negative samples or reconstruction adapted from the image domain, which requires dense affinity across multiple frames or optical flow constrain... | main | null | 10.1609/aaai.v37i2.25304 | 37 | 2 | 2110-2118 | official | null | null |
10.1609/aaai.v37i6.25870 | Wiener Graph Deconvolutional Network Improves Graph Self-Supervised Learning | https://ojs.aaai.org/index.php/AAAI/article/view/25870 | https://ojs.aaai.org/index.php/AAAI/article/download/25870/25642 | [
"Jiashun Cheng",
"Man Li",
"Jia Li",
"Fugee Tsung"
] | Graph self-supervised learning (SSL) has been vastly employed to learn representations from unlabeled graphs. Existing methods can be roughly divided into predictive learning and contrastive learning, where the latter one attracts more research attention with better empirical performance. We argue that, however, predic... | main | null | 10.1609/aaai.v37i6.25870 | 37 | 6 | 7131-7139 | official | 2206.12933 | title_snapshot |
10.1609/aaai.v37i9.26347 | CowClip: Reducing CTR Prediction Model Training Time from 12 Hours to 10 Minutes on 1 GPU | https://ojs.aaai.org/index.php/AAAI/article/view/26347 | https://ojs.aaai.org/index.php/AAAI/article/download/26347/26119 | [
"Zangwei Zheng",
"Pengtai Xu",
"Xuan Zou",
"Da Tang",
"Zhen Li",
"Chenguang Xi",
"Peng Wu",
"Leqi Zou",
"Yijie Zhu",
"Ming Chen",
"Xiangzhuo Ding",
"Fuzhao Xue",
"Ziheng Qin",
"Youlong Cheng",
"Yang You"
] | The click-through rate (CTR) prediction task is to predict whether a user will click on the recommended item. As mind-boggling amounts of data are produced online daily, accelerating CTR prediction model training is critical to ensuring an up-to-date model and reducing the training cost. One approach to increase the tr... | main | null | 10.1609/aaai.v37i9.26347 | 37 | 9 | 11390-11398 | official | 2204.06240 | title_snapshot |
10.1609/aaai.v37i3.25498 | SRoUDA: Meta Self-Training for Robust Unsupervised Domain Adaptation | https://ojs.aaai.org/index.php/AAAI/article/view/25498 | https://ojs.aaai.org/index.php/AAAI/article/download/25498/25270 | [
"Wanqing Zhu",
"Jia-Li Yin",
"Bo-Hao Chen",
"Ximeng Liu"
] | As acquiring manual labels on data could be costly, unsupervised domain adaptation (UDA), which transfers knowledge learned from a rich-label dataset to the unlabeled target dataset, is gaining increasingly more popularity. While extensive studies have been devoted to improving the model accuracy on target domain, an i... | main | null | 10.1609/aaai.v37i3.25498 | 37 | 3 | 3852-3860 | official | 2212.05917 | title_snapshot |
10.1609/aaai.v37i4.25541 | Rule Induction in Knowledge Graphs Using Linear Programming | https://ojs.aaai.org/index.php/AAAI/article/view/25541 | https://ojs.aaai.org/index.php/AAAI/article/download/25541/25313 | [
"Sanjeeb Dash",
"Joao Goncalves"
] | We present a simple linear programming (LP) based method to learn compact and interpretable sets of rules encoding the facts in a knowledge graph (KG) and use these rules to solve the KG completion problem. Our LP model chooses a set of rules of bounded complexity from a list of candidate first-order logic rules and as... | main | null | 10.1609/aaai.v37i4.25541 | 37 | 4 | 4233-4241 | official | 2110.08245 | title_snapshot |
10.1609/aaai.v37i2.25276 | Robust One-Shot Segmentation of Brain Tissues via Image-Aligned Style Transformation | https://ojs.aaai.org/index.php/AAAI/article/view/25276 | https://ojs.aaai.org/index.php/AAAI/article/download/25276/25048 | [
"Jinxin Lv",
"Xiaoyu Zeng",
"Sheng Wang",
"Ran Duan",
"Zhiwei Wang",
"Qiang Li"
] | One-shot segmentation of brain tissues is typically a dual-model iterative learning: a registration model (reg-model) warps a carefully-labeled atlas onto unlabeled images to initialize their pseudo masks for training a segmentation model (seg-model); the seg-model revises the pseudo masks to enhance the reg-model for ... | main | null | 10.1609/aaai.v37i2.25276 | 37 | 2 | 1861-1869 | official | 2211.14521 | title_snapshot |
10.1609/aaai.v37i7.26024 | Goal-Conditioned Q-learning as Knowledge Distillation | https://ojs.aaai.org/index.php/AAAI/article/view/26024 | https://ojs.aaai.org/index.php/AAAI/article/download/26024/25796 | [
"Alexander Levine",
"Soheil Feizi"
] | Many applications of reinforcement learning can be formalized as goal-conditioned environments, where, in each episode, there is a "goal" that affects the rewards obtained during that episode but does not affect the dynamics. Various techniques have been proposed to improve performance in goal-conditioned environments,... | main | null | 10.1609/aaai.v37i7.26024 | 37 | 7 | 8500-8509 | official | 2208.13298 | title_snapshot |
10.1609/aaai.v37i7.25997 | Inverse-Reference Priors for Fisher Regularization of Bayesian Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/25997 | https://ojs.aaai.org/index.php/AAAI/article/download/25997/25769 | [
"Keunseo Kim",
"Eun-Yeol Ma",
"Jeongman Choi",
"Heeyoung Kim"
] | Recent studies have shown that the generalization ability of deep neural networks (DNNs) is closely related to the Fisher information matrix (FIM) calculated during the early training phase. Several methods have been proposed to regularize the FIM for increased generalization of DNNs. However, they cannot be used direc... | main | null | 10.1609/aaai.v37i7.25997 | 37 | 7 | 8264-8272 | official | null | null |
10.1609/aaai.v37i4.25538 | Learning Representations of Bi-level Knowledge Graphs for Reasoning beyond Link Prediction | https://ojs.aaai.org/index.php/AAAI/article/view/25538 | https://ojs.aaai.org/index.php/AAAI/article/download/25538/25310 | [
"Chanyoung Chung",
"Joyce Jiyoung Whang"
] | Knowledge graphs represent known facts using triplets. While existing knowledge graph embedding methods only consider the connections between entities, we propose considering the relationships between triplets. For example, let us consider two triplets T1 and T2 where T1 is (Academy_Awards, Nominates, Avatar) and T2 is... | main | null | 10.1609/aaai.v37i4.25538 | 37 | 4 | 4208-4216 | official | 2302.02601 | title_snapshot |
10.1609/aaai.v37i3.25400 | Cross-Modal Contrastive Learning for Domain Adaptation in 3D Semantic Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/25400 | https://ojs.aaai.org/index.php/AAAI/article/download/25400/25172 | [
"Bowei Xing",
"Xianghua Ying",
"Ruibin Wang",
"Jinfa Yang",
"Taiyan Chen"
] | Domain adaptation for 3D point cloud has attracted a lot of interest since it can avoid the time-consuming labeling process of 3D data to some extent. A recent work named xMUDA leveraged multi-modal data to domain adaptation task of 3D semantic segmentation by mimicking the predictions between 2D and 3D modalities, and... | main | null | 10.1609/aaai.v37i3.25400 | 37 | 3 | 2974-2982 | official | null | null |
10.1609/aaai.v37i10.26422 | AlphaRoute: Large-Scale Coordinated Route Planning via Monte Carlo Tree Search | https://ojs.aaai.org/index.php/AAAI/article/view/26422 | https://ojs.aaai.org/index.php/AAAI/article/download/26422/26194 | [
"Guiyang Luo",
"Yantao Wang",
"Hui Zhang",
"Quan Yuan",
"Jinglin Li"
] | This paper proposes AlphaRoute, an AlphaGo inspired algorithm for coordinating large-scale routes, built upon graph attention reinforcement learning and Monte Carlo Tree Search (MCTS). We first partition the road network into regions and model large-scale coordinated route planning as a Markov game, where each partitio... | main | null | 10.1609/aaai.v37i10.26422 | 37 | 10 | 12058-12067 | official | null | null |
10.1609/aaai.v37i11.26630 | On the Calibration and Uncertainty with Pólya-Gamma Augmentation for Dialog Retrieval Models | https://ojs.aaai.org/index.php/AAAI/article/view/26630 | https://ojs.aaai.org/index.php/AAAI/article/download/26630/26402 | [
"Tong Ye",
"Shijing Si",
"Jianzong Wang",
"Ning Cheng",
"Zhitao Li",
"Jing Xiao"
] | Deep neural retrieval models have amply demonstrated their power but estimating the reliability of their predictions remains challenging. Most dialog response retrieval models output a single score for a response on how relevant it is to a given question. However, the bad calibration of deep neural network results in v... | main | null | 10.1609/aaai.v37i11.26630 | 37 | 11 | 13923-13931 | official | 2303.08606 | title_snapshot |
10.1609/aaai.v37i3.25375 | Truncate-Split-Contrast: A Framework for Learning from Mislabeled Videos | https://ojs.aaai.org/index.php/AAAI/article/view/25375 | https://ojs.aaai.org/index.php/AAAI/article/download/25375/25147 | [
"Zixiao Wang",
"Junwu Weng",
"Chun Yuan",
"Jue Wang"
] | Learning with noisy label is a classic problem that has been extensively studied for image tasks, but much less for video in the literature. A straightforward migration from images to videos without considering temporal semantics and computational cost is not a sound choice. In this paper, we propose two new strategies... | main | null | 10.1609/aaai.v37i3.25375 | 37 | 3 | 2751-2758 | official | 2212.13495 | title_snapshot |
10.1609/aaai.v37i11.26645 | A Generative Approach for Script Event Prediction via Contrastive Fine-Tuning | https://ojs.aaai.org/index.php/AAAI/article/view/26645 | https://ojs.aaai.org/index.php/AAAI/article/download/26645/26417 | [
"Fangqi Zhu",
"Jun Gao",
"Changlong Yu",
"Wei Wang",
"Chen Xu",
"Xin Mu",
"Min Yang",
"Ruifeng Xu"
] | Script event prediction aims to predict the subsequent event given the context. This requires the capability to infer the correlations between events. Recent works have attempted to improve event correlation reasoning by using pretrained language models and incorporating external knowledge (e.g., discourse relations). ... | main | null | 10.1609/aaai.v37i11.26645 | 37 | 11 | 14056-14064 | official | 2212.03496 | title_snapshot |
10.1609/aaai.v37i4.25588 | Efficient Embeddings of Logical Variables for Query Answering over Incomplete Knowledge Graphs | https://ojs.aaai.org/index.php/AAAI/article/view/25588 | https://ojs.aaai.org/index.php/AAAI/article/download/25588/25360 | [
"Dingmin Wang",
"Yeyuan Chen",
"Bernardo Cuenca Grau"
] | The problem of answering complex First-order Logic queries over incomplete knowledge graphs is receiving growing attention in the literature. A promising recent approach to this problem has been to exploit neural link predictors, which can be effective in identifying individual missing triples in the incomplete graph, ... | main | null | 10.1609/aaai.v37i4.25588 | 37 | 4 | 4652-4659 | official | null | null |
10.1609/aaai.v37i6.25900 | Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints | https://ojs.aaai.org/index.php/AAAI/article/view/25900 | https://ojs.aaai.org/index.php/AAAI/article/download/25900/25672 | [
"Yuhao Ding",
"Javad Lavaei"
] | We consider primal-dual-based reinforcement learning (RL) in episodic constrained Markov decision processes (CMDPs) with non-stationary objectives and constraints, which plays a central role in ensuring the safety of RL in time-varying environments. In this problem, the reward/utility functions and the state transition... | main | null | 10.1609/aaai.v37i6.25900 | 37 | 6 | 7396-7404 | official | 2201.11965 | title_snapshot |
10.1609/aaai.v37i3.25376 | Active Token Mixer | https://ojs.aaai.org/index.php/AAAI/article/view/25376 | https://ojs.aaai.org/index.php/AAAI/article/download/25376/25148 | [
"Guoqiang Wei",
"Zhizheng Zhang",
"Cuiling Lan",
"Yan Lu",
"Zhibo Chen"
] | The three existing dominant network families, i.e., CNNs, Transformers and MLPs, differ from each other mainly in the ways of fusing spatial contextual information, leaving designing more effective token-mixing mechanisms at the core of backbone architecture development. In this work, we propose an innovative token-mix... | main | null | 10.1609/aaai.v37i3.25376 | 37 | 3 | 2759-2767 | official | 2203.06108 | title_snapshot |
10.1609/aaai.v37i5.25740 | The Effect of Modeling Human Rationality Level on Learning Rewards from Multiple Feedback Types | https://ojs.aaai.org/index.php/AAAI/article/view/25740 | https://ojs.aaai.org/index.php/AAAI/article/download/25740/25512 | [
"Gaurav R. Ghosal",
"Matthew Zurek",
"Daniel S. Brown",
"Anca D. Dragan"
] | When inferring reward functions from human behavior (be it demonstrations, comparisons, physical corrections, or e-stops), it has proven useful to model the human as making noisy-rational choices, with a "rationality coefficient" capturing how much noise or entropy we expect to see in the human behavior. Prior work typ... | main | null | 10.1609/aaai.v37i5.25740 | 37 | 5 | 5983-5992 | official | 2208.10687 | title_snapshot |
10.1609/aaai.v37i11.26511 | RenewNAT: Renewing Potential Translation for Non-autoregressive Transformer | https://ojs.aaai.org/index.php/AAAI/article/view/26511 | https://ojs.aaai.org/index.php/AAAI/article/download/26511/26283 | [
"Pei Guo",
"Yisheng Xiao",
"Juntao Li",
"Min Zhang"
] | Non-autoregressive neural machine translation (NAT) models are proposed to accelerate the inference process while maintaining relatively high performance. However, existing NAT models are difficult to achieve the desired efficiency-quality trade-off. For one thing, fully NAT models with efficient inference perform infe... | main | null | 10.1609/aaai.v37i11.26511 | 37 | 11 | 12854-12862 | official | 2303.07665 | title_snapshot |
10.1609/aaai.v37i4.25612 | Practical Cross-System Shilling Attacks with Limited Access to Data | https://ojs.aaai.org/index.php/AAAI/article/view/25612 | https://ojs.aaai.org/index.php/AAAI/article/download/25612/25384 | [
"Meifang Zeng",
"Ke Li",
"Bingchuan Jiang",
"Liujuan Cao",
"Hui Li"
] | In shilling attacks, an adversarial party injects a few fake user profiles into a Recommender System (RS) so that the target item can be promoted or demoted. Although much effort has been devoted to developing shilling attack methods, we find that existing approaches are still far from practical. In this paper, we anal... | main | null | 10.1609/aaai.v37i4.25612 | 37 | 4 | 4864-4874 | official | 2302.07145 | title_snapshot |
10.1609/aaai.v37i6.25857 | Scalable and Globally Optimal Generalized L₁ K-center Clustering via Constraint Generation in Mixed Integer Linear Programming | https://ojs.aaai.org/index.php/AAAI/article/view/25857 | https://ojs.aaai.org/index.php/AAAI/article/download/25857/25629 | [
"Aravinth Chembu",
"Scott Sanner",
"Hassan Khurram",
"Akshat Kumar"
] | The k-center clustering algorithm, introduced over 35 years ago, is known to be robust to class imbalance prevalent in many clustering problems and has various applications such as data summarization, document clustering, and facility location determination. Unfortunately, existing k-center algorithms provide highly su... | main | null | 10.1609/aaai.v37i6.25857 | 37 | 6 | 7015-7023 | official | null | null |
10.1609/aaai.v37i7.25972 | Learning from Training Dynamics: Identifying Mislabeled Data beyond Manually Designed Features | https://ojs.aaai.org/index.php/AAAI/article/view/25972 | https://ojs.aaai.org/index.php/AAAI/article/download/25972/25744 | [
"Qingrui Jia",
"Xuhong Li",
"Lei Yu",
"Jiang Bian",
"Penghao Zhao",
"Shupeng Li",
"Haoyi Xiong",
"Dejing Dou"
] | While mislabeled or ambiguously-labeled samples in the training set could negatively affect the performance of deep models, diagnosing the dataset and identifying mislabeled samples helps to improve the generalization power. Training dynamics, i.e., the traces left by iterations of optimization algorithms, have recentl... | main | null | 10.1609/aaai.v37i7.25972 | 37 | 7 | 8041-8049 | official | 2212.09321 | title_snapshot |
10.1609/aaai.v37i5.25765 | RobustLoc: Robust Camera Pose Regression in Challenging Driving Environments | https://ojs.aaai.org/index.php/AAAI/article/view/25765 | https://ojs.aaai.org/index.php/AAAI/article/download/25765/25537 | [
"Sijie Wang",
"Qiyu Kang",
"Rui She",
"Wee Peng Tay",
"Andreas Hartmannsgruber",
"Diego Navarro Navarro"
] | Camera relocalization has various applications in autonomous driving. Previous camera pose regression models consider only ideal scenarios where there is little environmental perturbation. To deal with challenging driving environments that may have changing seasons, weather, illumination, and the presence of unstable o... | main | null | 10.1609/aaai.v37i5.25765 | 37 | 5 | 6209-6216 | official | 2211.11238 | title_snapshot |
10.1609/aaai.v37i4.25594 | Augmenting Affective Dependency Graph via Iterative Incongruity Graph Learning for Sarcasm Detection | https://ojs.aaai.org/index.php/AAAI/article/view/25594 | https://ojs.aaai.org/index.php/AAAI/article/download/25594/25366 | [
"Xiaobao Wang",
"Yiqi Dong",
"Di Jin",
"Yawen Li",
"Longbiao Wang",
"Jianwu Dang"
] | Recently, progress has been made towards improving automatic sarcasm detection in computer science. Among existing models, manually constructing static graphs for texts and then using graph neural networks (GNNs) is one of the most effective approaches for drawing long-range incongruity patterns. However, the manually ... | main | null | 10.1609/aaai.v37i4.25594 | 37 | 4 | 4702-4710 | official | null | null |
10.1609/aaai.v37i7.26057 | Dual Label-Guided Graph Refinement for Multi-View Graph Clustering | https://ojs.aaai.org/index.php/AAAI/article/view/26057 | https://ojs.aaai.org/index.php/AAAI/article/download/26057/25829 | [
"Yawen Ling",
"Jianpeng Chen",
"Yazhou Ren",
"Xiaorong Pu",
"Jie Xu",
"Xiaofeng Zhu",
"Lifang He"
] | With the increase of multi-view graph data, multi-view graph clustering (MVGC) that can discover the hidden clusters without label supervision has attracted growing attention from researchers. Existing MVGC methods are often sensitive to the given graphs, especially influenced by the low quality graphs, i.e., they tend... | main | null | 10.1609/aaai.v37i7.26057 | 37 | 7 | 8791-8798 | official | null | null |
10.1609/aaai.v37i2.25239 | CDTA: A Cross-Domain Transfer-Based Attack with Contrastive Learning | https://ojs.aaai.org/index.php/AAAI/article/view/25239 | https://ojs.aaai.org/index.php/AAAI/article/download/25239/25011 | [
"Zihan Li",
"Weibin Wu",
"Yuxin Su",
"Zibin Zheng",
"Michael R. Lyu"
] | Despite the excellent performance, deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples. Besides, these examples are often transferable among different models. In other words, the same adversarial example can fool multiple models with different architectures at the same time. Based on th... | main | null | 10.1609/aaai.v37i2.25239 | 37 | 2 | 1530-1538 | official | null | null |
10.1609/aaai.v37i3.25415 | DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completion | https://ojs.aaai.org/index.php/AAAI/article/view/25415 | https://ojs.aaai.org/index.php/AAAI/article/download/25415/25187 | [
"Zhiqiang Yan",
"Kun Wang",
"Xiang Li",
"Zhenyu Zhang",
"Jun Li",
"Jian Yang"
] | Unsupervised depth completion aims to recover dense depth from the sparse one without using the ground-truth annotation. Although depth measurement obtained from LiDAR is usually sparse, it contains valid and real distance information, i.e., scale-consistent absolute depth values. Meanwhile, scale-agnostic counterparts... | main | null | 10.1609/aaai.v37i3.25415 | 37 | 3 | 3109-3117 | official | 2211.10994 | title_snapshot |
10.1609/aaai.v37i1.25087 | Progress and Limitations of Deep Networks to Recognize Objects in Unusual Poses | https://ojs.aaai.org/index.php/AAAI/article/view/25087 | https://ojs.aaai.org/index.php/AAAI/article/download/25087/24859 | [
"Amro Abbas",
"Stéphane Deny"
] | Deep networks should be robust to rare events if they are to be successfully deployed in high-stakes real-world applications. Here we study the capability of deep networks to recognize objects in unusual poses. We create a synthetic dataset of images of objects in unusual orientations, and evaluate the robustness of a ... | main | null | 10.1609/aaai.v37i1.25087 | 37 | 1 | 160-168 | official | 2207.08034 | title_snapshot |
10.1609/aaai.v37i1.25159 | Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfer | https://ojs.aaai.org/index.php/AAAI/article/view/25159 | https://ojs.aaai.org/index.php/AAAI/article/download/25159/24931 | [
"Sunan He",
"Taian Guo",
"Tao Dai",
"Ruizhi Qiao",
"Xiujun Shu",
"Bo Ren",
"Shu-Tao Xia"
] | Real-world recognition system often encounters the challenge of unseen labels. To identify such unseen labels, multi-label zero-shot learning (ML-ZSL) focuses on transferring knowledge by a pre-trained textual label embedding (e.g., GloVe). However, such methods only exploit single-modal knowledge from a language model... | main | null | 10.1609/aaai.v37i1.25159 | 37 | 1 | 808-816 | official | 2207.01887 | title_snapshot |
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