6 citations · 11 across the 4 of their papers we have counts for
5 papers
Pixel Embedding: Fully Quantized Convolutional Neural Network with Differentiable Lookup Table
Hiroyuki Tokunaga, Joel Nicholls, Daria Vazhenina +1
By quantizing network weights and activations to low bitwidth, we can obtain hardware-friendly and energy-efficient networks. However, existing quantization techniques utilizing th…
Visual explanations of machine learning model estimating charge states in quantum dots
Yui Muto, Takumi Nakaso, Motoya Shinozaki +13
Charge state recognition in quantum dot devices is important in the preparation of quantum bits for quantum information processing. Toward auto-tuning of larger-scale quantum devic…
MLF-SC: Incorporating multi-layer features to sparse coding for anomaly detection
Ryuji Imamura, Kohei Azuma, Atsushi Hanamoto +1
Anomalies in images occur in various scales from a small hole on a carpet to a large stain. However, anomaly detection based on sparse coding, one of the widely used anomaly detect…
Node Centralities and Classification Performance for Characterizing Node Embedding Algorithms
Kento Nozawa, Masanari Kimura, Atsunori Kanemura
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…
Neural Sequence Model Training via -divergence Minimization
Sotetsu Koyamada, Yuta Kikuchi, Atsunori Kanemura +2
We propose a new neural sequence model training method in which the objective function is defined by -divergence. We demonstrate that the objective function generalizes the maxi…