Results 11 to 20 of about 111,243 (144)
Closed-Form Factorization of Latent Semantics in GANs [PDF]
Computer Vision and Pattern Recognition, 2020 A rich set of interpretable dimensions has been shown to emerge in the latent space of the Generative Adversarial Networks (GANs) trained for synthesizing images.
Yujun Shen, Bolei Zhou
semanticscholar +1 more source
Dataset Distillation via Factorization [PDF]
Neural Information Processing Systems, 2022 In this paper, we study \xw{dataset distillation (DD)}, from a novel perspective and introduce a \emph{dataset factorization} approach, termed \emph{HaBa}, which is a plug-and-play strategy portable to any existing DD baseline.
Songhua Liu +4 more
semanticscholar +1 more source
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction [PDF]
International Joint Conference on Artificial Intelligence, 2017 Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise ...
Huifeng Guo +4 more
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Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer [PDF]
SIAM Review, 1995 A digital computer is generally believed to be an efficient universal computing device; that is, it is believed to be able to simulate any physical computing device with an increase in computation time by at most a polynomial factor. This may not be true
P. Shor
semanticscholar +1 more source
Neural Collaborative Filtering vs. Matrix Factorization Revisited [PDF]
ACM Conference on Recommender Systems, 2020 Embedding based models have been the state of the art in collaborative filtering for over a decade. Traditionally, the dot product or higher order equivalents have been used to combine two or more embeddings, e.g., most notably in matrix factorization ...
Steffen Rendle +3 more
semanticscholar +1 more source
Language model compression with weighted low-rank factorization [PDF]
International Conference on Learning Representations, 2022 Factorizing a large matrix into small matrices is a popular strategy for model compression. Singular value decomposition (SVD) plays a vital role in this compression strategy, approximating a learned matrix with fewer parameters.
Yen-Chang Hsu +5 more
semanticscholar +1 more source
Neural Factorization Machines for Sparse Predictive Analytics [PDF]
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017 Many predictive tasks of web applications need to model categorical variables, such as user IDs and demographics like genders and occupations. To apply standard machine learning techniques, these categorical predictors are always converted to a set of ...
Xiangnan He, Tat-Seng Chua
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TuckER: Tensor Factorization for Knowledge Graph Completion [PDF]
Conference on Empirical Methods in Natural Language Processing, 2019 Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing facts based on existing ones.
Ivana Balazevic +2 more
semanticscholar +1 more source
Secure Federated Matrix Factorization [PDF]
IEEE Intelligent Systems, 2019 To protect user privacy and meet law regulations, federated (machine) learning is obtaining vast interests in recent years. The key principle of federated learning is training a machine learning model without needing to know each user’s personal raw ...
Di Chai, Leye Wang, Kai Chen, Qiang Yang
semanticscholar +1 more source
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks [PDF]
International Joint Conference on Artificial Intelligence, 2017 Factorization Machines (FMs) are a supervised learning approach that enhances the linear regression model by incorporating the second-order feature interactions.
Jun Xiao +5 more
semanticscholar +1 more source