While previous studies show that modeling the minimum meaning-bearing units (characters or morphemes) benefits learning vector representations of words, they ignore the semantic dependencies across these units when deriving word vectors. In this work, we propose to improve the learning ...
Once trained, the vectorial representations that emerge from pretext tasks capture the important features of the images, and can be used for comparison and categorization. Here, we present the development, validation and use of cytoself, a deep learning-based approach for fully self-supervised ...
Distributed representations of words and phrases and their compositionality. In Proc. 26th International Conference on Neural Information Processing Systems Vol. 2 (eds Burges, C. J. et al.) 3111–3119 (Curran Associates, 2013). Grover, A. & Leskovec, J. Node2vec: scalable feature learning ...
we want to set the hidden unit states and weights such that when we show the RBM an input record and ask the RBM to reconstruct the record, it generates something pretty close to the original input vector. Hinton talks about this effect in terms of how machines “dream about data.” ...
72 In this section, we will introduce the commonly used representations of proteins in DL models (Figure 4): sequence-based, structure-based, and one special form of representation relevant to computational modeling of proteins: coarse-grained models. Amino Acid Sequence as Representation As the ...
which can be considered as corpus’ sentences. Then, based on the corpus, vector representations are generated using neural language models. Ristoski et al. [10] propose RDF2Vec that uses language modeling approaches for unsupervised feature extraction from sequences of words and adapts them to ...
We also include results on few-shot learning and transfer learning to better evaluate the quality of the representations. More results and ablation studies can be found in the Ap- pendix. 4.1. Pre-training Setup We set the input image resolution as 256x256 t...
Implementing random sampling in the proposed encoding space is triv- ial: to sample uniformly from the entire search space, we simply draw encoding from the uniform Dirichlet distribu- tion p˜i ∼ Dir(1, ..., 1). More importantly, by virtue of the unique ...
(SRC) was used to classify the facial expressions. Bartlett et al.17used a combination of Adaboost and Support vector Machine (SVM) to recognize facial expressions to be used in the human-robot interaction assessment. With the advent of deep learning architecture, the need for manually ...
By sharing representations between the main task and auxiliary task, the multi-task model improves the performance on the main task. For MTL BioNER models, the number of successful examples is growing. Crichton et al. [23] uses convolution layer as the shared part and fully connected layer as...