Single-step retrosynthesis is a crucial task in organic synthesis, where the objective is to identify the reactants needed to produce a given product. In recent years, a variety of machine learning methods have been developed to tackle retrosynthesis prediction. In our study, we introduce RetroMoE, a novel generative model designed for the single-step retrosynthesis task. We start with a non-symmetric variational autoencoder (VAE) that incorporates a graph encoder to map molecular graphs into a latent space, followed by a transformer decoder for precise prediction of molecular SMILES strings. Additionally, we implement a simple yet effective mixture-of-experts (MoE) network to translate the product latent embedding into the reactant latent embedding. To our knowledge, this is the first approach that frames single-step retrosynthesis as a latent translation problem. Extensive experiments on the USPTO-50K and USPTO-MIT datasets demonstrate the superiority of our method, which not only surpasses most semi-template-based and template-free methods but also delivers competitive results against template-based methods. Notably, under the class-known setting on the USPTO-50K, our method achieves top-1 exact match accuracy comparable to the state-of-the-art template method, RetroKNN.
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Most existing multi-view representation learning methods assume view-completeness and noise-free data. However, such assumptions are strong in real-world applications. Despite advances in methods tailored to view-missing or noise problems individually, a one-size-fits-all approach that concurrently addresses both remains unavailable. To this end, we propose a holistic method, called Dual-masked Variational Autoencoders (DualVAE), which aims at learning robust multi-view representation. The DualVAE exhibits an innovative amalgamation of dual-masked prediction, mixture-of-experts learning, representation disentangling, and a joint loss function in wrapping up all components. The key novelty lies in the dual-masked (view-mask and patch-mask) mechanism to mimic missing views and noisy data. Extensive experiments on four multi-view datasets show the effectiveness of the proposed method and its superior performance in comparison to baselines. The code is available at https://github.com/XLearning-SCU/2025-IJCAI-DualVAE.
Low-rank Adaptation (LoRA) has demonstrated remarkable capabilities for task specific fine-tuning. However, in scenarios that involve multiple tasks, training a separate LoRA model for each one results in considerable inefficiency in terms of storage and inference. Moreover, existing parameter generation methods fail to capture the correlations among these tasks, making multi-task LoRA parameter generation challenging. To address these limitations, we propose In-Context Meta LoRA (ICM-LoRA), a novel approach that efficiently achieves task-specific customization of large language models (LLMs). Specifically, we use training data from all tasks to train a tailored generator, Conditional Variational Autoencoder (CVAE). CVAE takes task descriptions as inputs and produces task-aware LoRA weights as outputs. These LoRA weights are then merged with LLMs to create task-specialized models without the need for additional fine-tuning. Furthermore, we utilize in-context meta-learning for knowledge enhancement and task mapping, to capture the relationship between tasks and parameter distributions. As a result, our method achieves more accurate LoRA parameter generation for diverse tasks using CVAE. ICM-LoRA enables more accurate LoRA parameter reconstruction than current parameter reconstruction methods and is useful for implementing task-specific enhancements of LoRA parameters. At the same time, our method occupies 283MB, only 1% storage compared with the original LoRA. The code is available at https://github.com/YihuaJerry/ICM-LoRA.
Causal feature selection leverages causal discovery techniques to identify critical features associated with a target variable using observational data. Traditional methodologies primarily rely on constraint-based or score-based techniques, which are fraught with limitations. For example, conditional independence tests often yield unreliable results in the presence of noise and complex data generation processes, while the computational complexity of learning directed acyclic graphs increases exponentially with the number of variables involved. In light of recent advancements in deep learning, gradient-based methods have shown promise for global causal discovery. However, significant challenges arise when focusing on the identification of local causal features, particularly in defining the local causal constraint space to achieve both minimality and completeness. To address these issues, we introduce a novel gradient-based causal feature selection method (GCFS) that leverages an AutoEncoder to simultaneously model the target variable alongside other variables, thereby capturing of causal associations within a divide-and-conquer framework. Additionally, our approach incorporates a mask pruning strategy that transforms the search process into the minimization of a non-cyclic local reconstruction loss objective function. This function is then effectively optimized using a gradient-based method to accurately identify the causal features related to the target variable. Experimental results substantiate that GCFS surpasses existing methodologies across both synthetic and real datasets.
CASA: CNN Autoencoder-based Score Attention for Efficient Multivariate Long-term Time-series Forecasting
PDF ↗Multivariate long-term time series forecasting is critical for applications such as weather prediction, and traffic analysis. In addition, the implementation of Transformer variants has improved prediction accuracy. Following these variants, different input data process approaches also enhanced the field, such as tokenization techniques including point-wise, channel-wise, and patch-wise tokenization. However, previous studies still have limitations in time complexity, computational resources, and cross-dimensional interactions. To address these limitations, we introduce a novel CNN Autoencoder-based Score Attention mechanism (CASA), which can be introduced in diverse Transformers model-agnosticically by reducing memory and leading to improvement in model performance. Experiments on eight real-world datasets validate that CASA decreases computational resources by up to 77.7%, accelerates inference by 44.0%, and achieves state-of-the-art performance, ranking first in 87.5% of evaluated metrics. Our code is available at https://github.com/lmh9507/CASA.
Federated learning is a machine learning paradigm that enables decentralized clients to collaboratively learn a shared model while keeping all the training data local. While considerable research has focused on federated image generation, particularly Generative Adversarial Networks, Variational Autoencoders have received less attention. In this paper, we address the challenges of non-IID (independently and identically distributed) data environments featuring multiple groups of images of different types. Non-IID data distributions can lead to difficulties in maintaining a consistent latent space and can also result in local generators with disparate texture features being blended during aggregation. We thereby introduce FissionVAE that decouples the latent space and constructs decoder branches tailored to individual client groups. This method allows for customized learning that aligns with the unique data distributions of each group. Additionally, we incorporate hierarchical VAEs and demonstrate the use of heterogeneous decoder architectures within FissionVAE. We also explore strategies for setting the latent prior distributions to enhance the decoupling process. To evaluate our approach, we assemble two composite datasets: the first combines MNIST and FashionMNIST; the second comprises RGB datasets of cartoon and human faces, wild animals, marine vessels, and remote sensing images. Our experiments demonstrate that FissionVAE greatly improves generation quality on these datasets compared to baseline federated VAE models.
Recently, numerous multi-view clustering (MVC) and multi-view graph clustering (MVGC) methods have been proposed. Despite significant progress, they still face two issues: I) MVC and MVGC are often developed independently for multi-view and multi-graph data. They have redundancy but lack a unified methodology to combine their strengths. II) Contrastive learning is usually adopted to explore the associations across multiple views. However, traditional contrastive losses ignore the neighbor relationship in multi-view scenarios and easily lead to false associations in sample pairs. To address these issues, we propose Graph Embedded Contrastive Learning for Multi-View Clustering. Concretely, we propose a process of view-specific pre-training with adaptive graph convolution to make our method compatible with both multi-view and multi-graph data, which aggregates the graph information into data and leverages autoencoders to learn view-specific representations. Furthermore, to explore the view-cross associations, we introduce the process of view-cross contrastive learning and clustering, where we propose the graph-guided contrastive learning that can generate global graph to mitigate the false association issue as well as the cluster-guided contrastive clustering for improving the model robustness. Finally, extensive experiments demonstrate that our method achieves superior performance on both MVC and MVGC tasks.
We propose VideoHumanMIB, a novel framework for Video Human Motion In-betweening that enables seamless transitions between different motion video clips, facilitating the generation of longer and more natural digital human videos. While existing video frame interpolation methods work well for similar motions in adjacent frames, they often struggle with complex human movements, resulting in artifacts and unrealistic transitions. To address these challenges, we introduce a two-stage approach: First, we design an Appearance Reconstruction AutoEncoder to decouple appearance and motion information, extracting robust appearance-invariant features. Second, we develop an enhanced diffusion pretrained network that leverages both motion optical flow and human pose as guidance conditions, enabling the model to learn comprehensive latent distributions of possible motions. Rather than operating directly in pixel space, our model works in a learned latent space, allowing it to better capture the underlying motion dynamics. The framework is optimized with a dual-frame constraint loss and a motion flow loss to ensure temporal consistency and natural movement transitions. Extensive experiments demonstrate that our approach generates highly realistic transition sequences that significantly outperform existing methods, particularly in challenging scenarios with large motion variations. The proposed VideoHumanMIB establishes a new baseline for human motion synthesis and enables more natural and controllable digital human animation.
The evolution of point cloud processing algorithms necessitates an accurate assessment for their quality. Previous works consistently regard point cloud quality assessment (PCQA) as a MOS regression problem and devise a deterministic mapping, ignoring the stochasticity in generating MOS from subjective tests. This work presents the first probabilistic architecture for no-reference PCQA, motivated by the labeling process of existing datasets. The proposed method can model the quality judging stochasticity of subjects through a tailored conditional variational autoencoder (CVAE) and produces multiple intermediate quality ratings. These intermediate ratings simulate the judgments from different subjects and are then integrated into an accurate quality prediction, mimicking the generation process of a ground truth MOS. Specifically, our method incorporates a Prior Module, a Posterior Module, and a Quality Rating Generator, where the former two modules are introduced to model the judging stochasticity in subjective tests, while the latter is developed to generate diverse quality ratings. Extensive experiments indicate that our approach outperforms previous cutting-edge methods by a large margin and exhibits gratifying crossdataset robustness. Codes are available at https://git.openi.org.cn/OpenPointCloud/nrpcqa.
Graph anomaly detection (GAD), with its ability to accurately identify anomalous patterns in graph data, plays a vital role in areas such as network security, social media platforms, and fraud detection. Graph autoencoder-based methods are widely used for GAD due to their efficiency and effectiveness in capturing complex patterns and learning meaningful representations. However, the above methods are constrained by hardware memory, hindering the detection for large-scale graph data. In this paper, we propose a Memory-Efficient framework for large-scale attributed Graph Anomaly Detection (MEGAD). Specifically, MEGAD first generates node embeddings and then refines them through a lightweight joint optimization model, ensuring minimal memory overhead. The optimized embeddings are subsequently fed into a detector to compute anomaly scores. Extensive experiments demonstrate that our framework achieves comparable accuracy to state-of-the-art methods across multiple datasets while significantly reducing memory consumption on large-scale graphs.
In the digital age, recommendation systems are crucial for enhancing user experiences, with bundle recommendations playing a key role by integrating complementary products. However, existing methods fail to accurately identify user preferences for specific items within bundles, making it difficult to design bundles containing more items of interest to users. Additionally, these methods do not leverage similar preferences among users of the same category, resulting in unstable and incomplete preference expressions. To address these issues, we propose Preference Identification by Interaction Overlap for Bundle Recommendation (PIIO). The data augmentation module analyzes the overlap between bundle-item inclusions and user-item interactions to calculate the interaction probability of non-interacted bundles, selecting the bundle with the highest probability as a positive sample to enrich user-bundle interactions and uncover user preferences for items within bundles. The preference aggregation module utilizes the overlap in user-item interactions to select similar users, aggregates preferences using an autoencoder, and constructs comprehensive preference profiles. The optimization module predicts user-bundle matching scores based on a user interest boundary loss function. The proposed PIIO model is applied to two bundle recommendation datasets, and experiments demonstrate the effectiveness of the PIIO model, surpassing state-of-the-art models.
It is often challenging to identify a valid instrumental variable (IV), although the IV methods have been regarded as effective tools of addressing the confounding bias introduced by latent variables. To deal with this issue, an Interaction-Data-guided Conditional IV (IDCIV) debiasing method is proposed for Recommender Systems, called IDCIV-RS. The IDCIV-RS automatically generates the representations of valid CIVs and their corresponding conditioning sets directly from interaction data, significantly reducing the complexity of IV selection while effectively mitigating the confounding bias caused by latent variables in recommender systems. Specifically, the IDCIV-RS leverages a variational autoencoder (VAE) to learn both the CIV representations and their conditioning sets from interaction data, followed by the application of least squares to derive causal representations for click prediction. Extensive experiments on two real-world datasets, Movielens-10M and Douban-Movie, demonstrate that IDCIV-RS successfully learns the representations of valid CIVs, effectively reduces bias, and consequently improves recommendation accuracy.
RPMIL: Rethinking Uncertainty-Aware Probabilistic Multiple Instance Learning for Whole Slide Pathology Diagnosis
PDF ↗Whole slide images (WSIs) are gigapixel digital scans of traditional pathology slides, offering substantial support for cancer diagnosis. Current multiple instance learning (MIL) methods for WSIs typically extract instance features and aggregate these into a single bag feature for prediction. We observe that these MIL methods rely on point estimation, where each bag is mapped to a deterministic embedding. Such MIL methods based on point estimation fail to capture the full spectrum of data variability due to the reliance on fixed embedding, especially when the number of trainable bags is limited. In this paper, we rethink probabilistic modeling in MIL and propose RPMIL, an uncertainty-aware probabilistic MIL method for whole slide pathology diagnosis. RPMIL learns a probabilistic aggregator to consolidate instance features into dynamic bag feature distributions instead of a deterministic bag feature. Specifically, we employ a variational autoencoder approach to compress multiple instance features into a low-dimension space with probabilistic representation and obtain the bag feature distribution formulated by the mean and variance. Furthermore, we drive the prediction by jointly leveraging the instance feature distribution and bag feature distribution. We evaluate the WSI classification performance on two public datasets: Camelyon16 and TCGA-NSCLC. Extensive experiments demonstrate that our method surpasses point estimation methods in MIL, achieving state-of-the-art levels.
Point cloud understanding aims to acquire robust and general feature representations from unlabeled data. Masked point modeling-based methods have recently shown significant performance across various downstream tasks. These pre-training methods rely on random masking strategies to establish the perception of point clouds by restoring corrupted point cloud inputs, which leads to the failure of capturing reasonable semantic relationships by the self-supervised models. To address this issue, we propose Semantic Masked Autoencoder, which comprises two main components: a prototype-based component semantic modeling module and a component semantic-enhanced masking strategy. Specifically, in the component semantic modeling module, we design a component semantic guidance mechanism to direct a set of learnable prototypes in capturing the semantics of different components from objects. Leveraging these prototypes, we develop a component semantic-enhanced masking strategy that addresses the limitations of random masking in effectively covering complete component structures. Furthermore, we introduce a component semantic-enhanced prompt-tuning strategy, which further leverages these prototypes to improve the performance of pre-trained models in downstream tasks. Extensive experiments conducted on datasets such as ScanObjectNN, ModelNet40, and ShapeNetPart demonstrate the effectiveness of our proposed modules.
ExpTalk: Diverse Emotional Expression via Adaptive Disentanglement and Refined Alignment for Speech-Driven 3D Facial Animation
PDF ↗Speech-driven 3D facial animation aims to create lifelike facial expressions that synchronize accurately with speech. Despite significant progress, many existing methods may focus on generating facial animation with a fixed emotional state, neglecting the diverse transformations of facial emotions under a given speech input. To solve this issue, we focus on exploring the refined alignment between speech representations and multiple domains in facial expression information. We aim to disentangle the spoken language and emotion facial priors from speech expressions, to guide the refinement of the facial vertices based on speech. To accomplish this objective, we propose ExpTalk, which first applies an Adaptive Disentanglement Variational Autoencoder (AD-VAE) to decouple facial expression aligned with spoken language and emotions of speech through contrastive learning. Then a Refined Alignment Diffusion (RAD) is employed to iteratively refine the decoupled facial expression priors through diffusion-based perturbations, producing facial animations that align with the emotional variations of the given speech. Extensive experiments prove the effectiveness of our ExpTalk by surpassing state-of-the-arts by a large margin.
Accounting for Spatial Variability with the Histogram of Oriented Gradients Based Masking Improves Performance of Masked Autoencoder over Hyperspectral Satellite Imagery (Student Abstract)
PDF ↗Masked autoencoders employ random masking to effectively reconstruct input images using self-supervised techniques, which allows for efficient training on large datasets. However, the random masking strategy does not adequately tap into information encapsulated within high-dimensional hyperspectral satellite imagery that is used in several domains. We propose a novel masking strategy, HOGMAE, based on the Histogram of Oriented Gradients that incorporates rich information inherent within satellite images during the mask creation step. Our experiments, over a hyperspectral satellite dataset, demonstrate the effectiveness of our methodology.
Black-box optimization (BBO) has become increasingly relevant for tackling complex decision-making problems, especially in public policy domains such as police redistricting. However, its broader application in public policymaking is hindered by the complexity of defining feasible regions and the high-dimensionality of decisions. This paper introduces a novel BBO framework, termed as the Conditional And Generative Black-box Optimization (CageBO). This approach leverages a conditional variational autoencoder to learn the distribution of feasible decisions, enabling a two-way mapping between the original decision space and a simplified, constraint-free latent space. The CageBO efficiently handles the implicit constraints often found in public policy applications, allowing for optimization in the latent space while evaluating objectives in the original space. We validate our method through a case study on large-scale police redistricting problems in Atlanta, Georgia. Our results reveal that our CageBO offers notable improvements in performance and efficiency compared to the baselines.
Significant efforts have been dedicated to integrating the powerful Large Language Models (LLMs) with diverse modalities, particularly focusing on the fusion of language, vision and audio data. However, the graph-structured data, which is inherently rich in structural and domain-specific knowledge, has not yet been gracefully adapted to LLMs. Existing methods either describe the graph with raw text, suffering the loss of graph structural information, or feed Graph Neural Network (GNN) embeddings into LLMs at the cost of losing explainable prompt semantics. To bridge this gap, we introduce an end-to-end modality-aligning framework for LLM-graph alignment: Dual-Residual Vector Quantized-Variational AutoEncoder, namely Dr.E. Our approach is purposefully designed to facilitate token-level alignment with LLMs, enabling an effective translation of the intrinsic `language' of graphs into comprehensible natural language. We also manage to enhance LLMs' more robust structural understanding of graphs by incorporating multiple views of the central nodes based on their surrounding nodes at various distances. Our experimental evaluations on standard graph tasks demonstrate competitive performance against other state-of-the-art (SOTA) approaches. Additionally, our framework ensures certain visual interpretability, efficiency, and robustness, marking the promising successful endeavor to achieve token-level alignment between LLMs and GNNs.
Speech watermarking techniques can proactively mitigate the potential harmful consequences of instant voice cloning techniques. These techniques involve the insertion of signals into speech that are imperceptible to humans but can be detected by algorithms. Previous approaches typically embed watermark messages into continuous space. However, intuitively, embedding watermark information into robust discrete latent space can significantly improve the robustness of watermarking systems. In this paper, we propose DiscreteWM, a novel speech watermarking framework that injects watermarks into the discrete intermediate representations of speech. Specifically, we map speech into discrete latent space with a vector-quantized autoencoder and inject watermarks by changing the modular arithmetic relation of discrete IDs. To ensure the imperceptibility of watermarks, we also propose a manipulator model to select the candidate tokens for watermark embedding. Experimental results demonstrate that our framework achieves state-of-the-art performance in robustness and imperceptibility, simultaneously. Moreover, our flexible frame-wise approach can serve as an efficient solution for both voice cloning detection and information hiding. Additionally, DiscreteWM can encode 1 to 150 bits of watermark information within a 1-second speech clip, indicating its encoding capacity.
Recent advancements in proactive dialogues have garnered significant attention, particularly for more complex objectives (e.g. emotion support and persuasion). Unlike traditional task-oriented dialogues, proactive dialogues demand advanced policy planning and adaptability, requiring rich scenarios and comprehensive policy repositories to develop such systems. However, existing approaches tend to rely on Large Language Models (LLMs) for user simulation and online learning, leading to biases that diverge from realistic scenarios and result in suboptimal efficiency. Moreover, these methods depend on manually defined, context-independent, coarse-grained policies, which not only incur high expert costs but also raise concerns regarding their completeness. In our work, we highlight the potential for automatically discovering policies directly from raw, real-world dialogue records. To this end, we introduce a novel dialogue policy planning framework, LDPP. It fully automates the process from mining policies in dialogue records to learning policy planning. Specifically, we employ a variant of the Variational Autoencoder to discover fine-grained policies represented as latent vectors. After automatically annotating the data with these latent policy labels, we propose an Offline Hierarchical Reinforcement Learning (RL) algorithm in the latent space to develop effective policy planning capabilities. Our experiments demonstrate that LDPP outperforms existing methods on two proactive scenarios, even surpassing ChatGPT with only a 1.8-billion-parameter LLM.