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Found 7 papers, 2 papers with code
Label Space Partition Selection for Multi-Object Tracking Using Two-Layer Partitioning
To address such problems, a divide-and-conquer manner has been employed with parallel computation.
Is Cross-modal Information Retrieval Possible without Training?
Embeddings for a particular modality of data occupy a high-dimensional space of its own, but it can be semantically aligned to another by a simple mapping without training a deep neural net.
Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization
We build and fine-tune an abstractive dialogue summarization model on a shared encoder-decoder architecture using the enhanced BERT.
Abstractive Dialogue Summarization
Abstractive Text Summarization
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Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks
In zero-shot cross-lingual transfer, a supervised NLP task trained on a corpus in one language is directly applicable to another language without any additional training.
Evaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Tasks
The pre-trained BERT and A Lite BERT (ALBERT) models can be fine-tuned to give state-ofthe-art results in sentence-pair regressions such as semantic textual similarity (STS) and natural language inference (NLI).
DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets
We propose DefogGAN, a generative approach to the problem of inferring state information hidden in the fog of war for real-time strategy (RTS) games.
Clear the Fog: Combat Value Assessment in Incomplete Information Games with Convolutional Encoder-Decoders
StarCraft, one of the most popular real-time strategy games, is a compelling environment for artificial intelligence research for both micro-level unit control and macro-level strategic decision making.
