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- Scaling Distributed Machine Learning with the Parameter Server979
View on new challenges identified are shared, and some of the application scenarios such as micro-blog data analysis and data processing in building next generation search engines are covered.
- Shift-Net: Image Inpainting via Deep Feature Rearrangement511
A special shift-connection layer to the U-Net architecture, namely Shift-Net, is introduced for filling in missing regions of any shape with sharp structures and fine-detailed textures and an end-to-end learning algorithm is further developed to train the Shift- net.
- Learning Convolutional Networks for Content-Weighted Image Compression421
The bit rate of the different parts of the image is adapted to local content, and the content-aware bit rate is allocated under the guidance of a content-weighted importance map so that the sum of the importance map can serve as a continuous alternative of discrete entropy estimation to control compression rate.
- Earthformer: Exploring Space-Time Transformers for Earth System Forecasting416
The idea is to decompose the data into cuboids and apply cuboid-level self-attention in parallel, which achieves state-of-the-art performance on real-world benchmarks about precipitation nowcasting and El Nino/Southern Oscillation forecasting.
- AIM: Adapting Image Models for Efficient Video Action Recognition273
This work proposes a novel method to Adapt pre-trained Image Models (AIM) for efficient video understanding, and shows that the proposed AIM can achieve competitive or even better performance than prior arts with substantially fewer tunable parameters on four video action recognition benchmarks.
- Hierarchical Recurrent Neural Network for Document Modeling170
A novel hierarchical recurrent neural network language model (HRNNLM) for document modeling that integrates it as the sentence history information into the word level RNN to predict the word sequence with cross-sentence contextual information.
- A Recursive Recurrent Neural Network for Statistical Machine Translation166
A novel recursive recurrent neural network (R 2 NN) is proposed to model the end-to-end decoding process for statistical machine translation and can outperform the state of theart baseline by about 1.5 points in BLEU.
- Exploring distributional similarity based models for query spelling correction143
Novel methods for use of distributional similarity estimated from query logs in learning improved query spelling correction models are described, which can significantly outperform their baseline systems in the web query spelling Correction task.
- MixGen: A New Multi-Modal Data Augmentation136
MixGen is presented: a joint data augmentation for vision-language representation learning to further improve data efficiency and generates new image-text pairs with semantic relationships preserved by interpolating images and concatenating text.
- Iterative Row Sampling127
This work shows that alternating between computing a short matrix estimate and finding more accurate approximate leverage scores leads to a series of geometrically smaller instances that gives an algorithm whose runtime is input sparsity plus an overhead comparable to the cost of solving a regression problem on the smaller approximation.
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- Effective Tag Set Selection in Chinese Word Segmentation via Conditional Random Field Modeling122
It is shown that there is a significant performance difference as different tag sets are selected, and the proposed method gives the state-of-the-art performance.
- Bilingually-constrained Phrase Embeddings for Machine Translation120
This work proposes Bilingually-constrained Recursive Auto-encoders (BRAE) to learn semantic phrase embeddings (compact vector representations for phrases), which can distinguish the phrases with different semantic meanings.
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- Sequence-to-Dependency Neural Machine Translation107
A novel Sequence-to-Dependency Neural Machine Translation (SD-NMT) method, in which the target word sequence and its corresponding dependency structure are jointly constructed and modeled, and this structure is used as context to facilitate word generations.
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- LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation98
This paper proposes LayoutDiffuse, a novel neural adaptor that adapts a foundational diffusion model pretrained on large-scale image or text-image datasets for layout-to-image generation that significantly outperforms other 10 generative models based on GANs, VQ-VAE, and diffusion models.
- Learning Content-Weighted Deep Image Compression95
An upper-triangular masked convolutional network (triuMCN) for large context modeling is proposed, where the channel-wise multi-valued quantization is deployed for the discretization of the encoder features, and an importance map subnet is introduced to generate the importance masks for spatially varying code pruning.
- A Probabilistic Approach to Syntax-based Reordering for Statistical Machine Translation92
A novel, probabilistic approach to reordering which combines the merits of syntax and phrase-based SMT is proposed, which leads to BLEU improvement of 1.56% for the NIST MT-05 task of Chinese-toEnglish translation.
- CrossNorm and SelfNorm for Generalization under Distribution Shifts75
CrossNorm exchanges channel-wise mean and variance between feature maps to enlarge training distribution, while SelfNorm uses attention to recalibrate the statistics to bridge gaps between training and test distributions.
- Video Contrastive Learning with Global Context70
This paper proposes a new video-level contrastive learning method based on segments to formulate positive pairs that is able to capture the global context in a video, thus robust to temporal content change and can outperform previous state-of-the-arts efforts.
- Knowledge-Based Semantic Embedding for Machine Translation67
This paper builds and formulate a semantic space to connect the source and target languages, and applies it to the sequence-to-sequence framework to pro-pose a Knowledge-Based Semantic Em-bedding (KBSE) method.
- Removing Batch Normalization Boosts Adversarial Training53
This work proposes to completely eliminate this bottleneck by removing all BN layers in AT and achieves adversarial robustness with only a minor sacrifice on clean sample accuracy, and observes better model smoothness and larger decision margins from NoFrost, which make the models less sensitive to input perturbations and thus more robust.
- Modular Blind Video Quality Assessment50
A modular BVQA model that comprises a base quality predictor, a spatial rectifier, and a temporal rectifier, responding to the visual content and distortion, spatial resolution, and frame rate changes on video quality, respectively is proposed.
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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 2026-10-14. 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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