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Cold-start problem is one of the most challenging problems for recommender systems. One promising solution to this problem is cross-domain recommendation (CDR) which leverages rich information from an auxiliary source domain to improve the performance of recommender system in the target domain. In particular, the family of embedding and mapping methods for CDR is very effective, which explicitly learn a mapping function from source embeddings to target embeddings to transfer user’s preferences. Recent works usually transfer an overall source embedding by modeling a common or personalized preference bridge for all users. However, a unified user embedding cannot reflect the user’s multiple interests in auxiliary source domain. In this paper, we propose a novel framework called reinforced multi-interest transfer for CDR (REMIT). Specifically, we first construct a heterogeneous information network and employ different meta-path based aggregations to get user’s multiple interests in source domain, then transform different interest embeddings with different meta-generated personalized bridge functions for each user. To better coordinate the transformed user interest embeddings and the item embedding in target domain, we systematically develop a reinforced method to dynamically assign weights to transformed interests for different training instances and optimize the performance of target model. In addition, the REMIT is a general framework that can be applied upon various base models in target domain. Our extensive experimental results on large real-world datasets demonstrate the superior performance and compatibility of REMIT.

AAAI 2023

REMIT: Reinforced Multi-Interest Transfer for Cross-domain Recommendation

ml: transfer

ml: reinforcement learning algorithms

ml: graph-based machine learning

domain adaptation

multi-task learning

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.

Reconstructing a High Dynamic Range (HDR) image from several multi-exposed Low Dynamic Range images is a challenging task, especially in the presence of camera motion and object motion. Though existing models using convolutional neural networks (CNNs) have made much progress, challenges still exist, e.g., ghosting effects. Transformers, originating from the natural language processing field, have shown success in computer vision tasks, thanks to their ability to address a large receptive field even within a single layer. In this paper, we propose a Transformer model for HDR imaging. Our pipeline includes three steps: alignment, reconstruction, and enhancement. The key component is the HDR transformer module. Through experiments and ablation studies, we demonstrate that our model outperforms the state-of-the-art by large margins on several popular public datasets.

Improving Dynamic HDR Imaging with Fusion Transformer

The Extract-generate framework has been a classic approach for text summarization. As pretrained language models struggling with long-input summarization for their high memory cost, extract-generate framework regains researchers' interests. However, the cost of its effectiveness in dealing with long-input summarization is the loss of context information. In this paper, we present a context-aware extract-generate framework (CAEG) for long-input text summarization. It focuses on preserving both local and global context information in an extract-generate framework with little cost, and can be applied to most of existing extract-generate summarization models. CAEG generates a set of context-related text spans called context prompts for each text snippet and use them to transfer the context information from the extractor and generator. To find such context prompts, we propose to capture the context information based on the interpretation of the extractor, where the text spans having the highest contribution to the extraction decision are considered as containing the richest context information. We evaluate our approach on both long-document and long-dialogue summarization datasets: arXiv and QMSum. The experiment results show that CAEG achieves the-state-of-art result on QMSum and outperforms other extract-generate based models in arXiv. 

Preserve Context Information for Extract-Generate Long-Input Summarization Framework

Despite the remarkable progress of image captioning, existing captioners typically lack the controllable capability to generate desired image captions, e.g., describing the image in a rough or detailed manner, in a factual or emotional view, etc. In this paper, we show that a unified model is qualified to perform well in diverse domains and freely switch among multiple styles. Such a controllable capability is achieved by embedding the prompt learning into the image captioning framework. To be specific, we design a set of prompts to fine-tune the pre-trained image captioner. These prompts allow the model to absorb stylized data from different domains for joint training, without performance degradation in each domain. Furthermore, we optimize the prompts with learnable vectors in the continuous word embedding space, avoiding the heuristic prompt engineering and meanwhile exhibiting superior performance. In the inference stage, our model is able to generate desired stylized captions by choosing the corresponding prompts. Extensive experiments verify the controllable capability of the proposed method. Notably, we achieve outstanding performance on two diverse image captioning benchmarks including COCO Karpathy split and TextCaps using a unified model.

Controllable Image Captioning via Prompting

To capture global non-consecutive and long-distance semantic information, graph convolutional network (GCN) has been widely used for text classification. While GCN-based methods have achieved great success in offline evaluations, they usually construct fixed document-token graphs and cannot perform inference on new documents. It is still a challenge to apply GCNs in online systems which need to infer continual text data. In this work, we present a Continual GCN model, short as ContGCN, to generalize inferences from observed documents to unobserved documents. Concretely, we propose a novel global-token-local-document paradigm to dynamically update the document-token graph in every batch for any online system during both training and testing phases. Moreover, we design an occurrence memory module and a self-supervised contrastive learning objective to update the proposed ContGCN in any online system in a label-free manner. Extensive offline experiments conducted on five public datasets demonstrate that our proposed ContGCN can significantly improve inference quality. A 3-month A/B test on our internal online system shows ContGCN achieves 8.86% performance gain compared with state-of-the-art methods.

Continual Graph Convolutional Network for Text Classification

Cross-domain crowd counting has shown progressively improved performance. However, most methods fail to explicitly consider the transferability of different features between source and target domains. In this paper, we propose an innovative explicit Invariant Feature induced Cross-domain Knowledge Transformation framework to address the inconsistent domain-invariant features of different domains. The main idea is to explicitly extract domain-invariant features from both source and target domains, which builds a bridge to transfer more rich knowledge between two domains. The framework consists of three parts, global feature decoupling (GFD), relation exploration and alignment (REA), and graph-guided knowledge enhancement (GKE). In the GFD module, domain-invariant features are efficiently decoupled from domain-specific ones in two domains, which allows the model to distinguish crowds features from backgrounds in the complex scenes. In the REA module both inter-domain relation graph (Inter-RG) and intra-domain relation graph (Intra-RG) are built. Specifically, Inter-RG aggregates multi-scale domain-invariant features between two domains and further aligns local-level invariant features. Intra-RG preserves taskrelated specific information to assist the domain alignment. Furthermore, GKE strategy models the confidence of pseudolabels to further enhance the adaptability of the target domain. Various experiments show our method achieves state-of-theart performance on the standard benchmarks. Code is available at https://github.com/caiyiqing/IF-CKT.

Explicit Invariant Feature Induced Cross-Domain Crowd Counting

Logic-based machine learning has the crucial advantage of transparency. However, despite significant recent progress, further research is needed to close the accuracy gap between logic-based architectures and deep neural network ones. This paper introduces a novel variant of the Tsetlin machine (TM) that randomly drops clauses, the logical learning element of TMs. In effect, TM with Drop Clause ignores a random selection of the clauses in each epoch, selected according to a predefined probability. In this way, the TM learning phase becomes more diverse. To explore the effects that Drop Clause has on accuracy, training time and robustness, we conduct extensive experiments on nine benchmark datasets in natural language processing (IMDb, R8, R52, MR, and TREC) and image classification (MNIST, Fashion MNIST, CIFAR-10, and CIFAR-100). Our proposed model outperforms baseline machine learning algorithms by a wide margin and achieves competitive performance compared with recent deep learning models, such as BERT-Large and AlexNet-DFA. In brief, we observe up to +10% increase in accuracy and 2x to 4x faster learning than for the standard TM. We visualize the patterns learnt by Drop Clause TM in the form of heatmaps and show evidence of the ability of drop clause to learn more unique and discriminative patterns. We finally evaluate how Drop Clause affects learning robustness by introducing corruptions and alterations in the image/language test data, which exposes increased learning robustness.

Drop Clause: Enhancing Performance, Robustness and Pattern Recognition Capabilities of the Tsetlin Machine

Large-scale cross-modal pre-training paradigms have recently shown ubiquitous success on a wide range of downstream tasks, e.g., zero-shot classification, retrieval and image captioning. However, their successes highly rely on the scale and quality of web-crawled data that naturally contain much incomplete and noisy information (e.g., wrong or irrelevant contents). Existing works either design manual rules to clean data or generate pseudo-targets as auxiliary signals for reducing noise impact, which do not explicitly tackle both the incorrect and incomplete challenges at the same time. In this paper, to automatically mitigate the impact of noise by solely mining over existing data, we propose a principled Noise-robust Language-Image Pre-training framework (NLIP) to stabilize pre-training via two schemes: noise-harmonization and noise-completion. First, in noise-harmonization scheme, NLIP estimates the noise probability of each pair according to the memorization effect of cross-modal transformers, then adopts noise-adaptive regularization to harmonize the cross-modal alignments with varying degrees. Second, in noise-completion scheme, to enrich the missing object information of text, NLIP injects a concept-conditioned cross-modal decoder to obtain semantic-consistent synthetic captions to complete noisy ones, which uses the retrieved visual concepts (i.e., objects’ names) for the corresponding image to guide captioning generation. By collaboratively optimizing noise-harmonization and noise-completion schemes, our NLIP can alleviate the common noise effects during image-text pre-training in a more efficient way. Extensive experiments show the significant performance improvements of our NLIP using only 26M data over existing pre-trained models (e.g., CLIP, FILIP and BLIP) on 12 zero-shot classification datasets (e.g., +8.6% over CLIP on average accuracy), MSCOCO image captioning (e.g., +1.9 over BLIP trained with 129M data on CIDEr) and zero-shot image-text retrieval tasks.

NLIP: Noise-robust Language-Image Pre-training

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 the self-attention mechanism was originally proposed to model language sequences whose tokens (i.e., words) are discrete and highly semantic. However, unlike language sequences, most time series are sequential and continuous numeric points. Time steps with temporal redundancy are weakly semantic, and only leveraging time-domain tokens is hard to depict the overall properties of time series (e.g., the overall trend and periodic variations). To address these problems, we propose a novel Transformer-based forecasting model named InParformer with an Interactive Parallel Attention (InPar Attention) mechanism. The InPar Attention is proposed to learn long-range dependencies comprehensively in both frequency and time domains. To improve its learning capacity and efficiency, we further design several mechanisms, including query selection, key-value pair compression, and recombination. Moreover, InParformer is constructed with evolutionary seasonal-trend decomposition modules to enhance intricate temporal pattern extraction. Extensive experiments on six real-world benchmarks show that InParformer outperforms the state-of-the-art forecasting Transformers.

InParformer: Evolutionary Decomposition Transformers with Interactive Parallel Attention for Long-Term Time Series Forecasting

The scribble-supervised semantic segmentation is an important yet challenging task in the field of computer vision.
To deal with the pixel-wise sparse annotation problem, we propose a Progressive Bayesian Inference (PBI) framework to boost the performance of the scribble-supervised semantic segmentation, which can effectively infer the semantic distribution of these unlabeled pixels to guide the optimization of segmentation network. 
The PBI dynamically improves the model learning from two aspects: Bayesian inference module (i.e., semantic distribution learning) and the pixel-wise segmenter (i.e., model updating). 
Specifically, we effectively infer the semantic probability distribution of these unlabeled pixels with our designed Bayesian inference module, where its guidance is estimated through the Bayesian expectation maximization under the situation of partially observed data. 
The segmenter can be progressively improved under the joint guidance of the original scribble information and the learned semantic distribution. 
The segmenter optimization and semantic distribution promotion are encapsulated into a unified architecture where they could improve each other with mutual evolution in a progressive fashion. 
Comprehensive evaluations on several benchmark datasets demonstrate the effectiveness and superiority of our proposed PBI when compared with other state-of-the-art methods applied to the scribble-supervised semantic segmentation task.

Progressive Bayesian Inference for Scribble-supervised Semantic Segmentation

Federated learning (FL) is known to be susceptible to model poisoning attacks in which malicious clients hamper the accuracy of the global model by sending manipulated model updates to the central server during the FL training process.  Existing defenses mainly focus on Byzantine-robust FL aggregations, and largely ignore the impact of the underlying deep neural network (DNN) that is used to FL training.  Inspired by recent findings on critical learning periods (CLP) in DNNs, where small gradient errors have irrecoverable impact on the final model accuracy, we propose a new defense, called \textit{a CLP-aware defense against poisoning of FL} (DeFL).  The key idea of DeFL is to measure fine-grained differences between DNN model updates via an easy-to-compute federated gradient norm vector (FGNV) metric.  Using FGNV, DeFL simultaneously detects malicious clients and identifies CLP, which in turn is leveraged to guide the adaptive removal of detected malicious clients from aggregation.  As a result, DeFL not only mitigates model poisoning attacks on the global model but also is robust to detection errors.  Our extensive experiments on three benchmark datasets demonstrate that DeFL produces significant performance gain over conventional defenses against state-of-the-art model poisoning attacks.

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