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Learning with dataset bias in latent subcategory models
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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In this paper, we present a model which jointly learns an LSM for each dataset as well as a compound LSM. ...
Latent subcategory models (LSMs) offer significant improvements over training linear support vector machines (SVMs). ...
Discussion and Conclusion We presented a method for learning latent subcategories in presence of multiple biased datasets. ...
doi:10.1109/cvpr.2015.7298988
dblp:conf/cvpr/StamosMNMMP15
fatcat:kkzmm6ehlnhjvhvuh7ygicb23m
Mid-level Representation for Visual Recognition
[article]
2015
arXiv
pre-print
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We, additionally, study the outcomes provided by employing the subcategory-based models for undoing dataset bias. ...
We specifically employ the discriminative patches in a subcategory-aware webly-supervised fashion. ...
with Dataset Bias in Latent Subcategory Models”, D. ...
arXiv:1512.07314v1
fatcat:knmhkwxqk5aczis7ce6g2sv2wm
Structural Analysis of User Choices for Mobile App Recommendation
[article]
2016
arXiv
pre-print
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Moreover, we design an efficient learning algorithm to estimate the parameters for the SUCM model. ...
First, app markets have organized apps in a hierarchical taxonomy. Second, apps with similar functionalities are competing with each other. ...
Thus, we associate each internal node z with a latent variable qz to represent the category/subcategory level properties, and we model the latent variable qz as a function of the latent variable of z's ...
arXiv:1605.07980v1
fatcat:ygar37scgrczlpg74wnut74pwu
How important are Deformable Parts in the Deformable Parts Model?
[article]
2012
arXiv
pre-print
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A secondary contribution is the latent discriminative learning. Tertiary is the use of multiple components. ...
The main stated contribution of the Deformable Parts Model (DPM) detector of Felzenszwalb et al. ...
In terms of model learning, the DPM detector has the subcategory, as well as the part deformation parameters (four) as latent variables (for each of the 48 parts), while the visual subcategory detector ...
arXiv:1206.3714v1
fatcat:hy7gm6zyube3hnlumtfzds7m44
Refining Image Categorization by Exploiting Web Images and General Corpus
[article]
2017
arXiv
pre-print
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Studies show that refining real-world categories into semantic subcategories contributes to better image modeling and classification. ...
The following two major challenges are well studied: 1) noise in the labels of subcategories derived from the general corpus; 2) noise in the labels of images retrieved from the web. ...
In [31] , both of positive and negative images are used to learn subcategories. Particularly, a new model by joint clustering and classification was proposed for discriminative sub-categorization. ...
arXiv:1703.05451v1
fatcat:gjpbnhlog5gq5j4n25iqvrvedy
Visually-Aware Personalized Recommendation using Interpretable Image Representations
[article]
2018
arXiv
pre-print
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By using interpretable image representations generated with a unique feature learning process, our model learns to explain users' prior feedback in terms of their affinity towards specific visual attributes ...
In this paper we propose a novel approach to personalized clothing recommendation that models the dynamics of individual users' visual preferences. ...
In our case, the preference predictor extends the basic latent factor model by learning set of visual factors (θ u , θ i ), where θ T u θ i encodes a separate visual-speci c compatibility. ...
arXiv:1806.09820v2
fatcat:fau363r73vg3rjyzpqofgyazsy
Ups and Downs
2016
Proceedings of the 25th International Conference on World Wide Web - WWW '16
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2016 pre-print arXiv:1602.01585v1 fatcat:ab4534vu4bba7amepiqn4z2twa
In certain domains, such as fashion, modeling such preferences can be incredibly difficult, due to the need to simultaneously model the visual appearance of products as well as their evolution over time ...
In this paper we build novel models for the One-Class Collaborative Filtering setting, where our goal is to estimate users' fashion-aware personalized ranking functions based on their past feedback. ...
Our choice is to replace the stationary item bias term βi in Eq. 7 with a temporal counterpart βi(t) [19] . Per-Subcategory Temporal Dynamics. ...
doi:10.1145/2872427.2883037
dblp:conf/www/HeM16
fatcat:7rfv2qfmkjcltiybs7l3xybu74
Learning Improved Representations by Transferring Incomplete Evidence Across Heterogeneous Tasks
[article]
2019
arXiv
pre-print
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However, they often introduce noise or uncertainty in the labelset, that leads to decreased performance of supervised deep learning methods. ...
In this paper we evaluate the effectiveness of a representation learning method that uses external categorical evidence called "Evidence Transfer", against low amount of corresponding evidence termed as ...
The autoencoder is trained as a generative model that learns latent representation which approximate the data generation distribution. ...
arXiv:1912.10490v1
fatcat:fa2zh3mwnngnzdxf3yiiuggzve
Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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In this paper, we exploit POI-specific geographical influence to improve POI recommendation. ...
Experimental results on two real-world datasets demonstrate that POI-specific geographical influence significantly improves the performance of POI recommendation. ...
Each POI in both datasets is associated with its longitude and latitude. Additionally, in the Foursquare dataset, each POI is marked by 8 categories and 240 subcategories. ...
doi:10.24963/ijcai.2018/539
dblp:conf/ijcai/WangSOC18
fatcat:b5li2wpxyrf7bkmp5aw5lae2li
Zero-Shot Recommender Systems
[article]
2021
arXiv
pre-print
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On the other hand, zero-shot learning promises some degree of generalization from an old dataset to an entirely new dataset. In this paper, we explore the possibility of zero-shot learning in RS. ...
In terms of users, ZESRec builds upon recent advances on sequential RS to represent users using their interactions with items, thereby generalizing to unseen users as well. ...
This reflects strong recency bias in news recommendation. In contrast, our zero-shot learning naturally comes with an inductive bias to only model the transition from user history to the next item. ...
arXiv:2105.08318v2
fatcat:tbpnyiaefrhnlnlwsjqj3qlmbu
Metadata Embeddings for User and Item Cold-start Recommendations
[article]
2015
arXiv
pre-print
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I present a hybrid matrix factorisation model representing users and items as linear combinations of their content features' latent factors. ...
The model outperforms both collaborative and content-based models in cold-start or sparse interaction data scenarios (using both user and item metadata), and performs at least as well as a pure collaborative ...
I train the LightFM models using stochastic gradient descent with an initial learning rate of 0.05. The latent dimensionality of the models is set to 64 for all models and experiments. ...
arXiv:1507.08439v1
fatcat:4thubzvzzbbbfn2t4fkkg2vp5y
Deep Belief Nets for Topic Modeling
[article]
2015
arXiv
pre-print
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This article also comes with a newly developed deep belief nets toolbox for topic modeling tailored towards performance evaluation of the DBN model and comparisons to the LDA model. ...
Efficient retrieval can be carried out in the latent representation. We work both on public benchmarks and digital media content provided by Issuu, an online publishing platform. ...
As mentioned, Issuu has applied labels to the dataset from the results of their LDA model with a 150-dimensional latent representation. ...
arXiv:1501.04325v1
fatcat:symffahs5ze4bf6qwmkdmyynhi
Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability
[article]
2021
arXiv
pre-print
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Stochastic reachability can be used to detect biases in the availability of content and diagnose limitations in the opportunities for discovery granted to users. ...
In this work, we consider how preference models in interactive recommendation systems determine the availability of content and users' opportunities for discovery. ...
Acknowledgements This research is generously supported in part by ONR awards N00014-20-1-2497 and N00014-18-1-2833, NSF CPS award 1931853, and the DARPA Assured Autonomy program (FA8750-18-C-0101). ...
arXiv:2107.00833v1
fatcat:2puclhtbonccvhxeijzrd45fu4
Aspect-driven User Preference and News Representation Learning for News Recommendation
[article]
2022
arXiv
pre-print
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Extensive experiments are done on the commonly used real-world dataset MIND, which demonstrate the superiority of our method compared with representative and state-of-the-art methods. ...
In ANRS, news aspect-level encoder and user aspect-level encoder are devised to learn the fine-grained aspect-level representations of user's preferences and news characteristics respectively, which are ...
Second, the single neural network, i.e., CNN only achieves similar performance with the latent factor models, which means that the basic neural model is unable to effectively capture the features in news ...
arXiv:2110.05792v2
fatcat:wkd45dtj45ge5dodb2cxnggcdu
Probabilistic Machine Learning for Healthcare
[article]
2020
arXiv
pre-print
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Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning. ...
Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. ...
This work was supported in part by a CIFAR AI Chair at the Vector Institute (MG) and Microsoft Research (MG).
LITERATURE CITED ...
arXiv:2009.11087v1
fatcat:htosfeqvhndvfmlmud2pvl3nsy
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