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Found 5 papers, 3 papers with code
Pixel Embedding: Fully Quantized Convolutional Neural Network with Differentiable Lookup Table
By quantizing network weights and activations to low bitwidth, we can obtain hardware-friendly and energy-efficient networks.
MLF-SC: Incorporating multi-layer features to sparse coding for anomaly detection
The proposed method, multi-layer feature sparse coding (MLF-SC), employs a neural network for feature extraction, and feature maps from intermediate layers of the network are given to sparse coding, whereas the standard sparse-coding-based anomaly detection method directly works on given images.
Node Centralities and Classification Performance for Characterizing Node Embedding Algorithms
Embedding graph nodes into a vector space can allow the use of machine learning to e. g. predict node classes, but the study of node embedding algorithms is immature compared to the natural language processing field because of a diverse nature of graphs.
Alpha-divergence bridges maximum likelihood and reinforcement learning in neural sequence generation
Neural sequence generation is commonly approached by using maximum- likelihood (ML) estimation or reinforcement learning (RL).
Neural Sequence Model Training via $α$-divergence Minimization
We propose a new neural sequence model training method in which the objective function is defined by $\alpha$-divergence.
