arXiv research

ResGCN detects anomalies in attributed networks by capturing sparsity and nonlinearity.

New algorithm learns PTFs with noisy data efficiently.

Emotional content is a crucial ingredient in user-generated videos. However, the sparsity of emotional expressions in the videos poses an obstacle to visual emotion analysis. In this paper, we propose a new neural approach, Bi-stream Emotion Attribution-Classification Network (BEAC-Net), to solve three related emotion …

Recent research has demonstrated that feature attribution methods for deep networks can themselves be incorporated into training; these attribution priors optimize for a model whose attributions have certain desirable properties -- most frequently, that particular features are important or unimportant. These attributio…

Relational data are usually highly incomplete in practice, which inspires us to leverage side information to improve the performance of community detection and link prediction. This paper presents a Bayesian probabilistic approach that incorporates various kinds of node attributes encoded in binary form in relational m…

SISR improves feature attribution in complex payoff schemes.

Improved conversion rate prediction in online advertising using self-supervised pre-training.

Ordinal regression predicts the objects' labels that exhibit a natural ordering, which is important to many managerial problems such as credit scoring and clinical diagnosis. In these problems, the ability to explain how the attributes affect the prediction is critical to users. However, most, if not all, existing ordi…

New algorithm tackles subgroup fairness in AI with multiple sensitive attributes.

TSInsight improves interpretability of deep time-series models.

Unified framework for linear attribution methods in deep learning.

Efficient method certifies robustness of discrete data models, especially graphs.

CoT improves transformer sample efficiency by reducing input token dependencies and attention sparsity.

Intersectionality is a framework that analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including race, gender, sexual orientation, class, and disability. Intersectionality theory therefore implies it is important that fairness in artificial intelligence systems b…

To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and design delicate algorithms to exploit these additional information. In general, the attributes are not isolated but connected with each other, w…

We present ProxiModel, a novel event mining framework for extracting high-quality structured event knowledge from large, redundant, and noisy news data sources. The proposed model differentiates itself from other approaches by modeling both the event correlation within each individual document as well as across the cor…

Exact Gaussian Processes for massive datasets using non-stationary sparsity-discovering kernels.

Pruning improves model generalization in over-parameterized models, contradicting traditional theories.

Active learning (AL) on attributed graphs has received increasing attention with the prevalence of graph-structured data. Although AL has been widely studied for alleviating label sparsity issues with the conventional non-related data, how to make it effective over attributed graphs remains an open research question. E…

Study identifies key metrics for small and large tick assets in LOBs.

This paper introduces a new data-driven methodology for estimating sparse covariance matrices of the random coefficients in logit mixture models. Researchers typically specify covariance matrices in logit mixture models under one of two extreme assumptions: either an unrestricted full covariance matrix (allowing correl…

Researchers improve transformer networks' optimization and understanding.

A new deep learning model for tabular data improves accuracy over GBDT.

This paper presents a Semantic Attribute Modulation (SAM) for language modeling and style variation. The semantic attribute modulation includes various document attributes, such as titles, authors, and document categories. We consider two types of attributes, (title attributes and category attributes), and a flexible a…

Proposes a Taylor framework to unify and analyze attribution methods.

Unified framework for analyzing machine learning model attributions.

Facial attribute editing aims to manipulate single or multiple attributes of a face image, i.e., to generate a new face with desired attributes while preserving other details. Recently, generative adversarial net (GAN) and encoder-decoder architecture are usually incorporated to handle this task with promising results.…

Wavelet Attribution Method (WAM) improves feature attribution for deep models.

Proposes VCLANC for attributed network clustering using node and attribute embeddings.

Investors seek to attribute performance to various features using Shapley value method.

Unified analysis of removal-based feature attributions robustness.

A new method for disentangled latent spaces in VAEs that can manipulate attributes.

Graph structured data provide two-fold information: graph structures and node attributes. Numerous graph-based algorithms rely on both information to achieve success in supervised tasks, such as node classification and link prediction. However, node attributes could be missing or incomplete, which significantly deterio…

We are interested in attribute-guided face generation: given a low-res face input image, an attribute vector that can be extracted from a high-res image (attribute image), our new method generates a high-res face image for the low-res input that satisfies the given attributes. To address this problem, we condition the …

In many graphs such as social networks, nodes have associated attributes representing their behavior. Predicting node attributes in such graphs is an important problem with applications in many domains like recommendation systems, privacy preservation, and targeted advertisement. Attributes values can be predicted by a…

New framework for attributing online marketing touchpoints.

Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network intrusion detection and social spammer detection. However, most existing methods neglect the complex cross-modality interactio…

Study improves stock movement prediction using multimodal data.

Adapts IG for better feature attributions and robustness.

Spike-and-Slab Deep Learning (SS-DL) is a fully Bayesian alternative to Dropout for improving generalizability of deep ReLU networks. This new type of regularization enables provable recovery of smooth input-output maps with unknown levels of smoothness. Indeed, we show that the posterior distribution concentrates at t…

New method separates graph structure from node attributes to recover lost signal.

Attributes, such as metadata and profile, carry useful information which in principle can help improve accuracy in recommender systems. However, existing approaches have difficulty in fully leveraging attribute information due to practical challenges such as heterogeneity and sparseness. These approaches also fail to c…

New method builds complex networks from attribute interactions without normalization.

We develop a real-time anomaly detection algorithm for directed activity on large, sparse networks. We model the propensity for future activity using a dynamic logistic model with interaction terms for sender- and receiver-specific latent factors in addition to sender- and receiver-specific popularity scores; deviation…

New layer sparsity concept improves neural networks.

RoSHAP stabilizes feature attribution in machine learning models.

Method generates time-series attribution maps with identifiability guarantees.

Framework learns dynamic graph attributes and links co-evolution.