Canada

Link prediction is a fundamental task in network analysis, with the objective of predicting missing or potential links. While existing studies have mainly concentrated on single networks, it is worth noting that numerous real-world networks exhibit interconnectedness. For example, individuals often register on various social media platforms to access diverse services, such as chatting, tweeting, blogging, and rating movies. These platforms share a subset of users and are termed multilayer networks. The interlayer links in such networks hold valuable information that provides more comprehensive insights into the network structure. To effectively exploit this complementary information and enhance link prediction in the target network, we propose a novel cross-network embedding method. This method aims to represent different networks in a shared latent space, preserving proximity within single networks as well as consistency across multilayer networks. Specifically, nodes can aggregate messages from aligned nodes in other layers. Extensive experiments conducted on real-world datasets demonstrate the superior performance of our proposed method for link prediction in multilayer networks.

AAAI 2024

Link Prediction in Multilayer Networks via Cross-Network Embedding | VIDEO

social network analysis & community

app: social networks

dmkm: graph mining

ml: graph-based machine learning

technical paper

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

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-24 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.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.<br><br><br><br>

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/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. 

In biochemical modeling, some foundational systems can exhibit sudden and profound behavioral shifts, such as the cellular signaling pathway models, in which the physiological responses promptly react to environmental changes, resulting in steep changes in their dynamic model trajectories. These steep changes are one of the major challenges in biochemical modeling governed by nonlinear differential equations. One promising way to tackle this challenge is converting the input data from the time domain to the frequency domain through Fourier Neural Operators, which enhances the ability to analyze data periodicity and regularity. However, the effectiveness of these Fourier based methods diminishes in scenarios with complex abrupt switches. To address this limitation, an innovative Multiscale Attention Wavelet Neural Operator (MAWNO) method is proposed in this paper, which comprehensively combines the attention mechanism with the versatile wavelet transforms to effectively capture these abrupt switches. Specifically, the wavelet transform scrutinizes data across multiple scales to extract the characteristics of abrupt signals into wavelet coefficients, while the self-attention mechanism is adeptly introduced to enhance the wavelet coefficients in high-frequency signals that can better characterize the abrupt switches. Experimental results substantiate MAWNO’s supremacy in terms of accuracy on three classical biochemical models featuring periodic and steep trajectories. https://github.com/SUDERS/MAWNO.

Multiscale Attention Wavelet Neural Operator for Capturing Steep Trajectories in Biochemical Systems

Numerous techniques excel in brain tumor segmentation using multi-modal magnetic resonance imaging (MRI) sequences, delivering exceptional results. However, the prevalent absence of modalities in clinical scenarios hampers performance. Current approaches frequently resort to zero maps as substitutes for missing modalities, inadvertently introducing feature bias and redundant computations. To address these issues, we present the Token Merging transFormer (TMFormer) for robust brain tumor segmentation with missing modalities. TMFormer tackles these challenges by extracting and merging accessible modalities into more compact token sequences. The architecture comprises two core components: the Uni-modal Token Merging Block (UMB) and the Multi-modal Token Merging Block (MMB). The UMB enhances individual modality representation by adaptively consolidating spatially redundant tokens within and outside tumor-related regions, thereby refining token sequences for augmented representational capacity. Meanwhile, the MMB mitigates multi-modal feature fusion bias, exclusively leveraging tokens from present modalities and merging them into a unified multi-modal representation to accommodate varying modality combinations. Extensive experimental results on the BraTS 2018 and 2020 datasets demonstrate the superiority and efficacy of TMFormer compared to state-of-the-art methods when dealing with missing modalities.

TMFormer: Token Merging Transformer for Brain Tumor Segmentation with Missing Modalities

Multi-modal image segmentation is one of the core issues in computer vision. The main challenge lies in integrating common information between modalities while retaining specific patterns for each modality. Existing methods typically perform full fine-tuning on RGB-based pre-trained parameters to inherit the powerful representation of the foundation model. Although effective, such paradigm is not optimal due to weak transferability and scarce downstream data. Inspired by the recent success of prompt learning in language models, we propose the Grouping Prompt Tuning Framework (GoPT), which introduces explicit semantic grouping to learn modal-related prompts, adapting the frozen pre-trained foundation model to various downstream multi-modal segmentation tasks. Specifically, a class-aware uni-modal prompter is designed to balance intra- and inter-modal semantic propagation by grouping modality-specific class tokens, thereby improving the adaptability of spatial information. Furthermore, an alignment-induced cross-modal prompter is introduced to aggregate class-aware representations and share prompt parameters among different modalities to assist in modeling common statistics. Extensive experiments show the superiority of our GoPT, which achieves SOTA performance on various downstream multi-modal image segmentation tasks by training only < 1% model parameters.

Prompting Multi-Modal Image Segmentation with Semantic Grouping

Recent advancements in deep learning have greatly improved the efficiency of auxiliary medical diagnostics. However, concerns over patient privacy and data annotation costs restrict the viability of centralized training models. In response, federated semi-supervised learning has garnered substantial attention from medical institutions. However, it faces challenges arising from knowledge discrepancies among local clients and class imbalance in non-independent and identically distributed data. Existing methods like class balance adaptation for addressing class imbalance often overlook low-confidence yet valuable rare samples in unlabeled data and may compromise client privacy. To address these issues, we propose a novel framework with class awareness balance and dual teacher distillation called FedCD. FedCD introduces a global-local framework to balance and purify global and local knowledge. Additionally, we introduce a novel class awareness balance module to effectively explore potential rare classes and encourage balanced learning in unlabeled clients. Importantly, our approach prioritizes privacy protection by only exchanging network parameters during communication. Experimental results on two medical datasets under various settings demonstrate the effectiveness of FedCD. The code is available at https://github.com/YunzZ-Liu/FedCD.

FedCD: Federated Semi-supervised Learning with Class Awareness Balance via Dual Teachers

Image Quality Assessment (IQA) has long been a research hotspot in the field of image processing, especially No-Reference Image Quality Assessment (NR-IQA). Due to the powerful feature extraction ability, existing Convolution Neural Network (CNN) and Transformers based NR-IQA methods have achieved considerable progress. However, they still exhibit limited capability when facing unknown authentic distortion database. To further improve NR-IQA performance, in this paper, a novel supervised contrastive learning (SCL) and Transformer-based NR-IQA model SaTQA is proposed. We first train a model on a large-scale synthetic dataset by SCL (no image subjective score is required) to extract degradation features of images with various distortion types and levels. To further extract distortion information from images, we propose a backbone network incorporating the Multi-Stream Block (MSB) by combining the CNN inductive bias and Transformer long-term dependence modeling capability. Finally, we propose the Patch Attention Block (PAB) to obtain the final distorted image quality score by fusing the degradation features learned from contrastive learning with the perceptual distortion information extracted by the backbone network. Experimental results on seven standard IQA datasets show that SaTQA outperforms the state-of-the-art methods for both synthetic and authentic datasets.

Transformer-Based No-Reference Image Quality Assessment via Supervised Contrastive Learning | VIDEO

In this paper, we present SasWOT, the first training-free Semantic segmentation Architecture Search (SAS) framework via an auto-discovery proxy. Semantic segmentation is widely used in many real-time applications. For fast inference and memory efficiency, Previous SAS seeks the optimal segmenter by differentiable or RL Search. However, the significant computational costs of these training-based SAS limit their practical usage. To improve the search efficiency, we explore the training-free route but empirically observe that the existing zero-cost proxies designed on the classification task are sub-optimal on the segmentation benchmark. To address this challenge, we develop a customized proxy search framework for SAS tasks to augment its predictive capabilities. Specifically, we design the proxy search space based on the some observations: (1) different inputs of segmenter statistics can be well combined; (2) some basic operators can effectively improve the correlation. Thus, we build computational graphs with multiple statistics as inputs and different advanced basis arithmetic as the primary operations to represent candidate proxies. Then, we employ an evolutionary algorithm to crossover and mutate the superior candidates in the population based on correlation evaluation. Finally, based on the searched proxy, we perform the segmenter search without candidate training. In this way, SasWOT not only enables automated proxy optimization for SAS tasks but also achieves significant search acceleration before the retrain stage. Extensive experiments on Cityscapes and CamVid datasets demonstrate that SasWOT achieves superior trade-off between accuracy and speed over several state-of-the-art techniques. More remarkably, on Cityscapes dataset, SasWOT achieves the performance of 71.3% mIoU with the speed of 162 FPS.

SasWOT: Real-Time Semantic Segmentation Architecture Search WithOut Training

Video question answering involves understanding video content to generate accurate answers to questions. Recent studies have successfully modeled video features and achieved diverse multimodal interaction, yielding impressive outcomes. However, they have overlooked the fact that the video contains richer instances and events beyond the scope of the stated question. Extremely imbalanced alignment of information from both sides leads to significant instability in reasoning. To address this concern, we propose the Video-Context Aligned Transformer (V-CAT), which leverages the context to achieve semantic and content alignment between video and question. Specifically, the video and text are encoded into a shared semantic space initially. We apply contrastive learning to global video token and context token to enhance the semantic alignment. Then, the pooled context feature is utilized to obtain corresponding visual content. Finally, the answer is decoded by integrating the refined video and question features. We evaluate the effectiveness of V-CAT on MSVD-QA and MSRVTT-QA dataset, both achieving state-of-the-art performance. Extended experiments further analyze and demonstrate the effectiveness of each proposed module.

Video-Language Aligned Transformer for Video Question Answering

Recent advancements in single-stage Panoptic Narrative Grounding (PNG) have demonstrated significant potential. These methods predict pixel-level masks by directly matching pixels and phrases. However, they often neglect the modeling of semantic and visual relationships between phrase-level instances, limiting their ability for complex multi-modal reasoning in PNG. To tackle this issue, we propose XPNG, a "differentiation-refinement-localization" reasoning paradigm for accurately locating instances or regions. In XPNG, we introduce a Semantic Context Convolution (SCC) module to leverage semantic priors for generating distinctive features. This well-crafted module employs a combination of dynamic channel-wise convolution and pixel-wise convolution to embed semantic information and establish inter-object relationships guided by semantics. Subsequently, we propose a Visual Context Verification (VCV) module to provide visual cues, eliminating potential biases introduced by SCC and further refining the visual features generated by the previous module. Extensive experiments on PNG benchmark datasets reveal that our approach achieves state-of-the-art performance, significantly outperforming existing methods by a considerable margin and yielding a 3.9-point improvement in overall metrics. The source code will be available in the supplementary materials and released to the public.

Improving Panoptic Narrative Grounding by Harnessing Semantic Relationships and Visual Confirmation

In this paper we study the computational complexity of several reasoning tasks centered around the bounded plan existence problem. We do this for standard classical planning and hierarchical task network (HTN) planning and each for a grounded and a lifted representation. Whereas bounded plan existence complexity is known for classical planning, it has not yet been studied for HTN planning. For plan verification, results were available for both formalisms except for the lifted HTN planning. We will present lower and upper bounds of the complexity of plan verification in lifted HTN planning and provide novel insights into its grounded counterpart, in which we show that verification is not just NP-complete in the general case, but already for a severely restricted special case. Finally, we show the complexity concerning verifying the optimality of a given plan and discuss its connection to the bounded plan existence problem.

On the Computational Complexity of Plan Verification, (Bounded) Plan-Optimality Verification, and Bounded Plan Existence

Model-based offline reinforcement learning (RL) algorithms have emerged as a promising paradigm for offline RL.
These algorithms usually learn a dynamics model from a static dataset of transitions, use the model to generate synthetic trajectories, and perform conservative policy optimization within these trajectories. 
However, our observations indicate that policy optimization methods used in these model-based offline RL algorithms are not effective at exploring the learned model and induce biased exploration, which ultimately impairs the performance of the algorithm.
To address this issue, we propose Offline Conservative ExplorAtioN (OCEAN),  a novel rollout approach to model-based offline RL.
In our method, we incorporate additional exploration techniques and introduce three conservative constraints based on uncertainty estimation to mitigate the potential impact of significant dynamic errors resulting from exploratory transitions. 
Our work is a plug-in method and can be combined with classical model-based RL algorithms, such as MOPO, COMBO, and RAMBO.
Experiment results of our method on the D4RL MuJoCo benchmark show that OCEAN significantly improves the performance of existing algorithms.

Link Prediction in Multilayer Networks via Cross-Network Embedding | VIDEO

Downloads

Next from AAAI 2024

Multiscale Attention Wavelet Neural Operator for Capturing Steep Trajectories in Biochemical Systems

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES