United States

AAAI 2023

High-Throughput, High-Performance Deep Learning-driven Light Guide Plate Surface Visual Quality Inspection Tailored for Real-world Manufacturing Environments

deep learning and neural networks

tiny ml

embedded systems

manufacturing

pattern recognition

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

AAAI 2025

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



poster

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Deep generative models are effective in style transfer. 
Previous methods learn one or several specific artist-style from a collection of artworks.
These methods not only homogenize the artist-style of different artworks of the same artist but also lack generalization for the unseen artists.
To solve these challenges, we propose a double-style transferring module (DSTM).
It extracts different artist-style and artwork-style from different artworks (even untrained) and preserves the intrinsic diversity between different artworks of the same artist.
DSTM swaps the two styles in the adversarial training and encourages realistic image generation given arbitrary style combinations.
However, learning style from single artwork can often cause over-adaption to it, resulting in the introduction of structural features of style image.
We further propose an edge enhancing module (EEM) which derives edge information from multi-scale and multi-level features to enhance structural consistency.
We broadly evaluate our method across six large-scale benchmark datasets.
Empirical results show that our method achieves arbitrary artist-style and artwork-style extraction from a single artwork, and effectively avoids introducing the style image’s structural features.
Our method improves the state-of-the-art deception rate from 58.9% to 67.2% and the average FID from 48.74 to 42.83.

Preserving Structural Consistency in Arbitrary Artist and Artwork Style Transfer

The decomposition-based multi-objective evolutionary algorithm (MOEA/D) transforms a multi-objective optimization problem (MOP) into a set of single-objective subproblems for collaborative optimization. Mismatches between subproblems and solutions can lead to severe performance degradation of MOEA/D. Most existing mismatch coping strategies only work when the L∞ scalarization is used. A mismatch coping strategy that can use any Lp scalarization, even when facing MOPs with non-convex Pareto fronts, is of great significance for MOEA/D. This paper uses the global replacement (GR) as the backbone. We analyze how GR can no longer avoid mismatches when L∞ is replaced by another Lp with p ∈ [1, ∞), and find that the Lp-based (1 ≤ p < ∞) subproblems having inconsistently large preference regions. When p is set to a small value, some middle subproblems have very small preference regions so that their direction vectors cannot pass through their corresponding preference regions. Therefore, we propose a generalized Lp (GLp) scalarization to ensure that the subproblem’s direction vector passes through its preference region. Our theoretical analysis shows that GR can always avoid mismatches when using the GLp scalarization for any p ≥ 1. The experimental studies on various MOPs conform to the theoretical analysis.

A Generalized Scalarization Method for Evolutionary Multi-objective Optimization

It is possible for agents operating in a shared environment to interfere with one another. One mechanism of coordination is called Social Law. Enacting such a law in a multi-agent setting restricts agents' behaviors. Robustness, in this case, ensures that the agents do not harmfully interfere with each other and that each agent achieves its goals regardless of what other agents do. Previous work on social law verification examined only the case of boolean state variables. However, many real-world problems require reasoning with numeric variables. Moreover, numeric fluents allow a more compact representation of multiple planning problems.

In this paper, we develop a method to verify whether a given social law is robust via compilation to numeric planning. A solution to this compilation constitutes a counterexample to the robustness of the problem, i.e., evidence of cross-agent conflict. Thus, the social law is robust if and only if the proposed compilation is unsolvable. We empirically verify robustness in multiple domains using state-of-the-art numeric planners. Additionally, this compilation raises a challenge by generating a set of non-trivial numeric domains where unsolvability should be either proved or disproved.

Automated Verification of Social Laws in Numeric Settings

Semi-supervised learning (SSL) has been actively studied due to its ability to alleviate the reliance of deep learning models on labeled data. Although existing SSL methods based on pseudo-labeling strategies have made great progress, they rarely consider time-series data's intrinsic properties (e.g., temporal dependence). Learning representations by mining the inherent properties of time series has recently gained much attention. Nonetheless, how to utilize feature representations to design SSL paradigms for time series has not been explored. To this end, we propose a Time Series SSL framework via Temporal-Frequency Co-training (TS-TFC), leveraging the complementary information from two distinct views for unlabeled data learning. In particular, TS-TFC employs time-domain and frequency-domain views to train two deep neural networks simultaneously, and each view's pseudo-labels generated by label propagation in the representation space are adopted to guide the training of the other view's classifier. To enhance the discriminative of representations between categories, we propose a temporal-frequency supervised contrastive learning module, which integrates the learning difficulty of categories to improve the quality of pseudo-labels. Through co-training the pseudo-labels obtained from temporal-frequency representations, the complementary information in the two distinct views is exploited to enable the model to better learn the distribution of categories. Extensive experiments on 106 UCR datasets show that TS-TFC outperforms state-of-the-art methods, demonstrating the effectiveness and robustness of our proposed model.

Temporal-Frequency Co-Training for Time Series Semi-Supervised Learning

Given a sequence of sets, where each set contains an arbitrary number of elements, temporal sets prediction aims to predict which elements will appear in the subsequent set. Existing methods for temporal sets prediction are developed on sophisticated components (e.g., recurrent neural networks, attention or gating mechanisms and graph neural networks), which inevitably increase the model complexity due to more trainable parameters and higher computational costs. Moreover, the involved nonlinear activation may contribute little or even degrade the performance. In this paper, we present a succinct architecture that is solely built on the Simplified Fully Connected Networks (SFCNs) for temporal sets prediction to bring both effectiveness and efficiency together. In particular, given a user's sequence of sets, we employ SFCNs to derive representations of the user by learning inter-set temporal dependencies, intra-set element relationships and intra-embedding channel correlations. Two families of general functions are introduced to preserve the permutation-invariant property of each set and the permutation-equivariant property of elements in each set. Moreover, we design a user representations adaptive fusing module to aggregate user representations according to each element for improving the prediction performance. Experiments on four benchmarks show the superiority of our approach over the state-of-the-arts under both transductive and inductive settings. We also theoretically and empirically demonstrate that our model has lower space and time complexity than baselines. Codes and datasets are available at https://github.com/yule-BUAA/SFCNTSP.

Predicting Temporal Sets with Simplified Fully Connected Networks

Over the years, learning-based multi-view stereo methods have achieved great success based on their coarse-to-fine depth estimation frameworks.  However, 3D CNN-based cost volume regularization inevitably leads to over-smoothing problems at object boundaries due to its smooth properties. Moreover, discrete and sparse depth hypothesis sampling exacerbates the difficulty in recovering the depth of thin structures and object boundaries. To this end, we present an Efficient edge-Preserving multi-view stereo Network (EPNet) for practical depth estimation. To keep delicate estimation at details, a Hierarchical Edge-Preserving Residual learning (HEPR) module is proposed to progressively rectify the upsampling errors and help refine multi-scale depth estimation. After that, a Cross-view Photometric Consistency (CPC) is proposed to enhance the gradient flow for detailed structures, which further boosts the estimation accuracy. Last, we design a lightweight cascade framework and inject the above two strategies into it to achieve better efficiency and performance trade-offs. Extensive experiments show that our method achieves state-of-the-art performance with fast inference speed and low memory usage. Notably, our method tops the first place on challenging Tanks and Temples advanced dataset and ETH3D high-res benchmark among all published learning-based methods. Code will be available at https://github.com/susuwj/EPNet.

Efficient Edge-Preserving Multi-view Stereo Network for Depth Estimation

We devise a new regularization for learning denoising in a self-supervised manner. This regularization is formulated using a deep image prior learned by the network, rather than a traditional predefined prior. Specifically, we treat the output of the network as a ``prior'' that we denoise again after ``re-noising''. The network is updated to minimize the discrepancy between the again denoised image and its prior. Based on the assumption of zero mean and spatially independent noise, we demonstrate that the network can learn to denoise even if it has not seen any clean images. The denoising ability of our method is derived from the fact that CNNs naturally tend to capture low-level image statistics. Since our method utilizes the image prior implicitly captured by the deep denoising CNN to guide denoising, we refer to this training strategy as Implicit Deep Denoiser Prior (IDDP). IDDP can be seen as a mixture of learning-based methods and traditional model-based denoising methods, in which regularization is adaptively formulated using the output of the network. We show the application of IDDP to various denoising tasks using only observed corrupted data. It can achieve the denoising performance close to supervised CNNs.

Self-Supervised Image Denoising using Implicit Deep Denoiser Prior

Bayesian optimal experimental design is a sub-field of statistics focused on developing methods to make efficient use of experimental resources. Any potential design is evaluated in terms of a utility function, such as the (theoretically well-justified) expected information gain (EIG); unfortunately however, under most circumstances the EIG is intractable to evaluate. In this work we build off of successful variational approaches, which optimize a parameterized variational model with respect to bounds on the EIG. Past work focused on learning a new variational model from scratch for each new design considered. Here we present a novel neural architecture that allows experimenters to optimize a single variational model that can estimate the EIG for potentially infinitely many designs. To further improve computational efficiency, we also propose to train the variational model on a significantly cheaper-to-evaluate lower bound, and show empirically that the resulting model provides an excellent guide for more accurate, but expensive to evaluate  bounds on the EIG. We demonstrate the effectiveness of our technique on generalized linear models, a class of statistical models that is widely used in the analysis of controlled experiments. Experiments show that our method is able to greatly improve accuracy over existing approximation strategies, and achieve these results with far better sample efficiency.

Design Amortization for Bayesian Optimal Experimental Design

Learning representations for graph-structured data is essential for graph analytical tasks.
While remarkable progress has been made on static graphs, researches on temporal graphs are still in its beginning stage.
The bottleneck of the temporal graph representation learning approach is the neighborhood aggregation strategy, based on which graph attributes share and gather information explicitly.
Existing neighborhood aggregation strategies fail to capture either the short-term features or the long-term features of temporal graph attributes, leading to unsatisfactory model performance and even poor robustness and domain generality of the representation learning method.
To address this problem, we propose a Frame-level Timeline Modeling (FTM) method that helps to capture both short-term and long-term features, and thus learns more informative representations on temporal graphs.
In particular, we present a novel link-based framing technique to preserve the short-term features
and then incorporate a timeline aggregator module to capture the intrinsic dynamics of graph evolution as long-term features.
Our method can be easily assembled with most temporal GNNs. 
Extensive experiments on common datasets show that our method brings great improvements to the capability, the robustness, and the domain generality of backbone methods in downstream tasks. We also achieve new state-of-the-art performance on two benchmarks.

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