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- Alex J. SmolaSuggested from co-authorship
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Works186 from public data
- Image-Based Recommendations on Styles and Substitutes3,040
The approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within.
- A Survey on Deep Neural Network Pruning: Taxonomy, Comparison, Analysis, and Recommendations599
This survey provides a comprehensive review of existing research works on deep neural network pruning in a taxonomy of 1) universal/specific speedup, 2) when to prune, 3) how to prune, and 4) fusion of pruning and other compression techniques.
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- From Motion Blur to Motion Flow: A Deep Learning Solution for Removing Heterogeneous Motion Blur439
This work directly estimates the motion flow from the blurred image through a fully-convolutional deep neural network (FCN) and recovers the unblurred image from the estimated motion flow and is the first universal end-to-end mapping from the blur image to the dense motion flow.
- Fast Supervised Hashing with Decision Trees for High-Dimensional Data430
Experiments demonstrate that the proposed method significantly outperforms most state-of-the-art methods in retrieval precision and training time, and is orders of magnitude faster than many methods in terms of training time.
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- Attention-Guided Network for Ghost-Free High Dynamic Range Imaging313
The proposed AHDRNet is a non-flow-based method, which can also avoid the artifacts generated by optical-flow estimation error, and can achieve state-of-the-art quantitative and qualitative results.
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- Hash Kernels for Structured Data241
This work generalizes previous work using sampling and shows a principled way to compute the kernel matrix for data streams and sparse feature spaces and gives deviation bounds from the exact kernel matrix.
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- Seeing Deeply and Bidirectionally: A Deep Learning Approach for Single Image Reflection Removal191
This work argues that, to remove reflection truly well, it should estimate the reflection and utilize it to estimate the background image, and proposes a cascade deep neural network, which estimates both the Background image and the reflection.
- Human Action Segmentation and Recognition Using Discriminative Semi-Markov Models148
This paper proposes a discriminative semi-Markov model approach, and defines a set of features over boundary frames, segments, as well as neighboring segments that enable it to conveniently capture a combination of local and global features that best represent each specific action type.
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- Implicit Sample Extension for Unsupervised Person Re-Identification144
An Implicit Sample Extension method to generate support samples from actual samples and their neighbouring clusters in the embedding space through a progressive linear interpolation (PLI) strategy, which reduces the “sub and mixed” clustering errors, thus improving the Re-ID performance.
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- Active Learning by Feature Mixing135
This paper proposes a novel method for batch AL called ALFA-Mix, which identifies unlabelled instances with sufficiently-distinct features by seeking inconsistencies in predictions resulting from interventions on their representations, and derives an efficient implementation based on a closed-form solution to the optimal interpolation causing changes in predictions.
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- Wind turbine power output prediction using a new hybrid neuro-evolutionary method121
A novel composite deep learning-based evolutionary approach for accurate forecasting of the power output in wind-turbine farms, developed in three stages, supported the superiority of the proposed hybrid model in terms of accurate forecasting and computational runtime compared with earlier published hybrid models.
- Learning Deep Gradient Descent Optimization for Image Deconvolution120
A recurrent gradient descent network (RGDN) is proposed by systematically incorporating deep neural networks into a fully parameterized gradient descent scheme and learns an implicit image prior and a universal update rule through recursive supervision.
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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