The Annotated S4
An annotated implementation of a series of papers developing state-space models for very long-term sequence modeling. Covers "HiPPO: Recurrent Memory with Optimal Polynomial Projections" and ends with "Efficiently Modeling Long Sequences with Structured State Spaces".
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Tasks
State Space ModelsSimilar Papers 제목 키워드 기반
Crowd Counting With Partial Annotations in an Image
To fully leverage the data captured from different scenes with different view angles while reducing the annotation cost, this paper studies a novel crowd counting setting, i.e. only using partial annotations in each …
Active LearningCrowd CountingWeText: Scene Text Detection under Weak Supervision
The requiring of large amounts of annotated training data has become a common constraint on various deep learning systems. In this paper, we propose a weakly supervised scene text detection method (WeText) that trains ro…
Scene Text DetectionText DetectionWeakly-supervised LearningNamed Entity Recognition for Partially Annotated Datasets
The most common Named Entity Recognizers are usually sequence taggers trained on fully annotated corpora, i.e. the class of all words for all entities is known. Partially annotated corpora, i.e. some but not all entities…
Allnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images
We propose a framework for localization and classification of masses in breast ultrasound (BUS) images. We have experimentally found that training convolutional neural network based mass detectors with large, weakly anno…
General ClassificationProjecting named entity recognizers without annotated or parallel corpora
Named entity recognition (NER) is a well-researched task in the field of NLP, which typically requires large annotated corpora for training usable models. This is a problem for languages which lack large annotated corpor…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER