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Works306 from public data
- Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering4,946
A combined bottom-up and top-down attention mechanism that enables attention to be calculated at the level of objects and other salient image regions is proposed, demonstrating the broad applicability of this approach to VQA.
- Hierarchical Attention Networks for Document Classification4,944
Experiments conducted on six large scale text classification tasks demonstrate that the proposed architecture outperform previous methods by a substantial margin.
- Embedding Entities and Relations for Learning and Inference in Knowledge Bases3,965
It is found that embeddings learned from the bilinear objective are particularly good at capturing relational semantics and that the composition of relations is characterized by matrix multiplication.
- Learning deep structured semantic models for web search using clickthrough data2,323
A series of new latent semantic models with a deep structure that project queries and documents into a common low-dimensional space where the relevance of a document given a query is readily computed as the distance between them are developed.
- MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition2,241
A benchmark task to recognize one million celebrities from their face images, by using all the possibly collected face images of this individual on the web as training data, which could lead to one of the largest classification problems in computer vision.
- Stacked Attention Networks for Image Question Answering2,028
A multiple-layer SAN is developed in which an image is queried multiple times to infer the answer progressively, and the progress that the SAN locates the relevant visual clues that lead to the answer of the question layer-by-layer.
- AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks2,008
An Attentional Generative Adversarial Network that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation and for the first time shows that the layered attentional GAN is able to automatically select the condition at the word level for generating different parts of the image.
- Stacked Cross Attention for Image-Text Matching1,425
Stacked Cross Attention to discover the full latent alignments using both image regions and words in sentence as context and infer the image-text similarity achieves the state-of-the-art results on the MS-COCO and Flickr30K datasets.
- From captions to visual concepts and back1,363
This paper uses multiple instance learning to train visual detectors for words that commonly occur in captions, including many different parts of speech such as nouns, verbs, and adjectives, and develops a maximum-entropy language model.
- A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories863
A new framework for evaluating story understanding and script learning: the 'Story Cloze Test', which requires a system to choose the correct ending to a four-sentence story, and a new corpus of ~50k five-sentence commonsense stories, ROCStories, to enable this evaluation.
- Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base847
This work proposes a novel semantic parsing framework for question answering using a knowledge base that leverages the knowledge base in an early stage to prune the search space and thus simplifies the semantic matching problem.
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- A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems743
This work proposes a content-based recommendation system to address both the recommendation quality and the system scalability, and proposes to use a rich feature set to represent users, according to their web browsing history and search queries, using a Deep Learning approach.
- From Eliza to XiaoIce: challenges and opportunities with social chatbots742
The success metric for social chatbots is defined as conversation-turns per session (CPS), and it is shown how XiaoIce can dynamically recognize emotion and engage the user throughout long conversations with appropriate interpersonal responses.
- Learning semantic representations using convolutional neural networks for web search730
This paper presents a series of new latent semantic models based on a convolutional neural network to learn low-dimensional semantic vectors for search queries and Web documents that significantly outperforms other se-mantic models in retrieval performance.
- A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval728
A new latent semantic model that incorporates a convolutional-pooling structure over word sequences to learn low-dimensional, semantic vector representations for search queries and Web documents is proposed.
- 591
- Domain Adaptation via Pseudo In-Domain Data Selection584
The results show that more training data is not always better, and that best results are attained via proper domain-relevant data selection, as well as combining in- and general-domain systems during decoding.
- Visual Storytelling551
Modelling concrete description as well as figurative and social language, as provided in this dataset and the storytelling task, has the potential to move artificial intelligence from basic understandings of typical visual scenes towards more and more human-like understanding of grounded event structure and subjective expression.
- Deep Learning with Low Precision by Half-Wave Gaussian Quantization537
An half-wave Gaussian quantizer (HWGQ) is proposed for forward approximation and shown to have efficient implementation, by exploiting the statistics of of network activations and batch normalization operations, and to achieve much closer performance to full precision networks than previously available low-precision networks.
- CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise523
CleanNet, a joint neural embedding network, which only requires a fraction of the classes being manually verified to provide the knowledge of label noise that can be transferred to other classes is introduced, which can reduce label noise detection error rate on held-out classes where no human supervision available.
- Multimodal Intelligence: Representation Learning, Information Fusion, and Applications504
A technical review of available models and learning methods for multimodal intelligence, focusing on the combination of vision and natural language modalities, which has become an important topic in both the computer vision andnatural language processing research communities.
- Semantic Compositional Networks for Visual Captioning453
Experimental results show that the proposed method significantly outperforms prior state-of-the-art approaches, across multiple evaluation metrics.
- Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding445
The results show that on this task, both types of recurrent networks outperform the CRF baseline substantially, and a bi-directional Jordantype network that takes into account both past and future dependencies among slots works best, outperforming a CRFbased baseline by 14% in relative error reduction.
- Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval443
This work develops a multi-task DNN for learning representations across multiple tasks, not only leveraging large amounts of cross-task data, but also benefiting from a regularization effect that leads to more general representations to help tasks in new domains.
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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