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Works372 from public data
- Understanding bag-of-words model: a statistical framework1,392
A statistical framework which generalizes the bag-of-words representation, in which the visual words are generated by a statistical process rather than using a clustering algorithm, while the empirical performance is competitive to clustering-based method.
- 735
- 735
- SoftTriple Loss: Deep Metric Learning Without Triplet Sampling446
The SoftTriple loss is proposed to extend the SoftMax loss with multiple centers for each class, equivalent to a smoothed triplet loss where each class has a single center.
- 384
- Combining link and content for community detection380
A discriminative model for combining the link and content analysis for community detection from networked data, such as paper citation networks and Word Wide Web is proposed and introduced and hidden variables are introduced to explicitly model the popularity of nodes.
- An automatic weighting scheme for collaborative filtering371
An optimization algorithm to automatically compute the weights for different items based on their ratings from training users will create a clustered distribution for user vectors in the item space by bringing users of similar interests closer and separating users of different interests more distant.
- 362
- Extremely Low Bit Neural Network: Squeeze the Last Bit Out With ADMM307
This paper focuses on compressing and accelerating deep models with network weights represented by very small numbers of bits, referred to as extremely low bit neural network, and proposes to solve this problem using extragradient and iterative quantization algorithms that lead to considerably faster convergency compared to conventional optimization methods.
- 299
- 297
- Simple and Efficient Multiple Kernel Learning by Group Lasso295
This paper forms a closed-form solution for optimizing the kernel weights based on the equivalence between group-lasso and MKL that leads to an efficient algorithm for MKL, but also generalizes to the case for Lp-MKL.
- 282
- Online Feature Selection and Its Applications280
This article investigates the problem of online feature selection (OFS) in which an online learner is only allowed to maintain a classifier involved only a small and fixed number of features, and presents novel algorithms to solve each of the two problems.
- Large-scale text categorization by batch mode active learning277
A novel active learning algorithm that selects a batch of text documents for labeling manually in each iteration and uses the Fisher information matrix as the measurement of model uncertainty and chooses the set of documents to effectively maximize the Fisher Information matrix of a classification model.
- Multimedia Search with Pseudo-relevance Feedback263
The results are encouraging, indicating that pseudo-relevance feedback shows great promise for multimedia retrieval with very varied and errorful data.
- 249
- Online AUC Maximization241
This work develops online learning algorithm for maximizing Area Under the ROC curve (AUC), a metric that is widely used for measuring the classification performance for imbalanced data distributions, and presents two algorithms for online AUC maximization with theoretic performance guarantee.
- Learning to Rank by Optimizing NDCG Measure229
This work proposes a probabilistic framework that addresses the challenge of evaluating ranking algorithms using the expectation of NDCG over all the possible permutations of documents, and proposes an algorithm that outperforms state-of-the-art ranking algorithms on several benchmark data sets.
- 228
- Regularized Distance Metric Learning:Theory and Algorithm209
It is shown that with appropriate constraints, the generalization error of regularized distance metric learning could be independent from the dimensionality, making it suitable for handling high dimensional data.
- Approximate kernel k-means202
It is shown both analytically and empirically that the performance of approximate kernel k-means is similar to that of the kernel k -means algorithm, but with dramatically reduced run-time complexity and memory requirements.
- An Extended Level Method for Efficient Multiple Kernel Learning199
The extended level method is extended, which was originally designed for optimizing non-smooth objective functions, to convex-concave optimization, and applies it to multiple kernel learning, and overcomes the drawbacks of SILP and SD.
- One-pass AUC optimization198
A regression-based algorithm is developed that requires going through the training data only once without storing the entire training dataset, where conventional online learning algorithms cannot be applied directly because AUC is measured by a sum of losses defined over pairs of instances from different classes.
- Beyond Appearance: A Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks192
This paper aims to learn a general human representation from massive unlabeled human images which can benefit downstream human-centric tasks to the maximum extent using a Semantic cOntrollable seLf-supervIseD lEaRning framework.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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