Huafeng Liu

Most works of interpretable neural networks strive for learning the semantics concepts merely from single modal information such as images. However, humans usually learn semantic concepts from multiple modalities and the semantics is encoded by the brain from fused multi-modal information. Inspired by cognitive science and vision-language learning, we propose a Prototype-Concept Alignment Network (ProCoNet) for learning visual prototypes under the guidance of textual concepts. In the ProCoNet, we have designed a visual encoder to decompose the input image into regional features of prototypes, while also developing a prompt generation strategy that incorporates in-context learning to prompt large language models to generate textual concepts. To align visual prototypes with textual concepts, we leverage the multimodal space provided by the pre-trained CLIP as a bridge. Specifically, the regional features from the vision space and the cropped regions of prototypes encoded by CLIP reside on different but semantically highly correlated manifolds, i.e. follow a multi-manifold distribution. We transform the multi-manifold distribution alignment problem into optimizing the projection matrix by Cayley transform on the Stiefel manifold. Through the learned projection matrix, visual prototypes can be projected into the multimodal space to align with semantically similar textual concept features encoded by CLIP. We conducted two case studies on the CUB-200-2011 and Oxford Flower dataset. Our experiments show that the ProCoNet provides higher accuracy and better interpretability compared to the single-modality interpretable model. Furthermore, ProCoNet offers a level of interpretability not previously available in other interpretable methods.

User preferences behind users’ decision-making processes are highly diverse and may range from lower-level concepts with more specific intentions and higher-level concepts with more general intentions. In this case, user preferences tend to be expressed hierarchically. However, learning such intentions with different levels from user behaviors is challenging, and remains largely neglected by the existing literature. Meanwhile, user behavior data tends to be sparse because of the limited user response and the vast combinations of users and items, which results in cold-start problems with unclear user intentions. In this paper, we propose a mixture intention neural stochastic process (MINSP), a new view of the stochastic processes family using a general meta-learning mechanism and mixture strategy for robust recommendation with hierarchical preferences modeling. By considering the recommendation process for each user as a stochastic process, MINSP defines distributions over functions and is capable of rapid adaptation to different users. To capture the user's intention on different levels, an iterative additive algorithm is proposed that minimizes the approximation error by backfitting the residuals of previous approximations. In this case, the induced tree intention hierarchies serve as an aggregated structured representation of the whole preference, summarizing the gist for convenient navigation and better generalization. Furthermore, we theoretically analyze the generalization error bound of the proposed MINSP to guarantee the model performance. Empirical results show that our approach can achieve substantial improvement over the state-of-the-art baselines in terms of recommendation performance, and obtain an interpretable hierarchical intention structure.

=User cold-start recommendation is a well-known challenge in current recommender systems. The cause is that the number of user interactions is too few to accurately estimate user preferences. Furthermore, the uncertainty of user interactions intensifies along with the number of user interactions decreasing. Although existing meta-learning based models with globally sharing knowledge show good performance in most cold-start scenarios, the ability of handling challenges on intention importance and prediction uncertainty is missing: (1) Intra-user uncertainty. When estimating user preferences (reflected in the user's latent representation), each of user interactions is independently considered in the form of user-item pair, which cannot capture the correlation between user interactions, as well as considering the global intent under user interactions. (2) Inter-user importance. During the model training, all users are treated as equally important, which cannot distinguish the contribution of users in the model training process. Assigning the same weight to all users may lead to users with high uncertainty incorrectly guiding the model learning in the early stage of training. To tackle the above challenges, in this paper, we focus on modeling user preference as a weighted distribution over functions (WDoF) for user cold-start recommendation, which not only models the intra-user uncertainty through neural processes with Multinomial likelihood but also considers the importance of different users with curriculum learning during the model training process. Furthermore, we provide a theoretical explanation that why the proposed model performs better than regular neural processes based recommendation methods. Experiments on four real-world datasets demonstrate the effectiveness of the proposed model over several state-of-the-art cold-start recommendation methods.

As an important branch of unsupervised learning methods, clustering makes a wide contribution in the area of data mining. It is well known that capturing the group-discriminative properties of each sample for clustering is crucial. Among them, deep clustering delivers promising results due to the strong representational power of neural networks. However, most of them adopt sample-level learning strategies, and the standalone data point barely captures its holistic cluster’s context and may undergo sub-optimal cluster assignment. To tackle this issue, we propose a Structure-driven Representation Learning (SRL) method by introducing latent structure information into the representation learning process at both the local and global levels. Specifically, a local-structure-driven sample representation strategy is proposed to approximate the estimation of data distribution, which models the neighborhood distribution of samples with potential structure information and exploits statistical dependencies between them to improve cluster consistency. A global-structure-driven cluster representation strategy is designed, where the context of each cluster is sufficiently encoded according to its samples (exemplar-theory) and corresponding prototype (prototype-theory). In this case, each cluster can only be related to its most similar samples, and different clusters are separated as much as possible. These two models are seamlessly combined into a joint optimization problem, which can be efficiently solved. Experiments on six widely-used datasets demonstrate the superiority of SRL over state-of-the-art clustering methods.

When humans explain their reasoning, such as their classification decisions, they often break down an image into parts and highlight the evidence from those parts to support the concepts they have in mind. Drawing inspiration from this cognitive process, several self-explaining models have been proposed to explain predictions by part-level concepts. However, these models can be limited by their structure and difficulty in determining the effect of individual parts on the output category. To address these challenges, we introduce a self-explaining architecture that uses a plug-in transparent embedding space (TesNet) to connect high-level input patches (e.g. feature maps or tokens) with output categories. The transparent embedding space is spanned by basis concepts and constructed on the Grassmann manifold. The basis concepts are enforced to be category-aware, and within-category concepts are orthogonal to each other, ensuring the embedding space is disentangled. To reduce concept redundancy and restore the concept space structure, we introduce two concept pruning methods and a new re-training strategy to build a slimming transparent embedding space. We verify the scalability of TesNet through experiments on deep networks such as VGG, ResNet, DenseNet, and Vision Transformer. Additionally, we design several metrics for self-explaining models to quantify interpretability and compare them with state-of-the-art self-explaining methods. Our experiments demonstrate that TesNet is much more effective for classification tasks, providing better interpretability on predictions and improving final accuracy.

Text classification, is one of the key tasks for representing the semantic information of documents, multi-label text classification (MLTC) is an important branch of it. MLTC aims to tag the most relevant labels for the given document. Compared to the standard multi-class case where each document has only one label, it is considerably more difficulty to annotate new coming documents for multi-label text classification. Furthermore, it also suffers from the challenge of highly skewed long-tailed label distribution. i.e., a few labels are associated with a large number of documents (a.k.a. head labels), while a large fraction of labels are associated with a small number of documents (a.k.a. tail labels). Due to the relative infrequency of tail labels, this leads to an imbalance that biases towards predicting more head labels. As challenging as this task is, it is an essential task to tackle since it represents many real-world cases, such as text retrieval of news. To address the challenge, we propose a Triple Alliance Prototype Orthotist Network (TAPON) to build a generic meta-mapping from few-shot prototypes to many-shot classifier parameters, which aims to promote the generalizability of tail classifiers. To be specific, TAPON is a two-stage method. At the first stage focusing on head labels, TAPON obtains the meta-knowledge between many-shot classifier parameters and few-shot prototype of head labels. Head label classifiers are trained by many-shot documents. Meanwhile, the triple alliance prototype is obtained by adopting an Attentive Prototype with the aid of few-shot documents, label semantic information and label correlation. Additionally, a Prototype Orthotist module is especially designed to capture the meta-knowledge between the many-shot classifier and few-shot prototype. At the second stage of transferring, TAPON aims to transfer the generic meta-mapping from head labels to tail labels. It first uses Attentive Prototype to obtain triple alliance prototype for tail labels, and then uses the meta-knowledge obtained from the first stage to get many-shot classifiers for tail labels. By conducting extensive experiments on four benchmark datasets, we show that the proposed TAPON significantly outperforms other state-of-the-art methods for long-tailed multi-label text classification.

Discovering hidden pattern from imbalanced data is a critical issue in various real-world applications. Existing classification methods usually suffer from the limitation of data especially for minority classes, and result in unstable prediction and low performance. In this paper, a deep generative classifier is proposed to mitigate this issue via both model perturbation and data perturbation. Specially, the proposed generative classifier is derived from a deep latent variable model where two variables are involved. One variable is to capture the essential information of the original data, denoted as latent codes, which are represented by a probability distribution rather than a single fixed value. The learnt distribution aims to enforce the uncertainty of model and implement model perturbation, thus, lead to stable predictions. The other variable is a prior to latent codes so that the codes are restricted to lie on components in Gaussian Mixture Model. As a confounder affecting generative processes of data (feature/label), the latent variables are supposed to capture the discriminative latent distribution and implement data perturbation. Extensive experiments have been conducted on widely-used real imbalanced image datasets. Experimental results demonstrate the superiority of our proposed model by comparing with popular imbalanced classification baselines on imbalance classification task.