Search Results for author: Sang-Bum Kim

Found 4 papers, 0 papers with code

Interpretable Word Embedding Contextualization

no code implementations WS 2018 Kyoung-Rok Jang, Sung-Hyon Myaeng, Sang-Bum Kim

In this paper, we propose a method of calibrating a word embedding, so that the semantic it conveys becomes more relevant to the context.

Word Embeddings

Training a Probabilistic Graphical Model with Resistive Switching Electronic Synapses

no code implementations27 Sep 2016 S. Burc Eryilmaz, Emre Neftci, Siddharth Joshi, Sang-Bum Kim, Matthew BrightSky, Hsiang-Lan Lung, Chung Lam, Gert Cauwenberghs, H. -S. Philip Wong

Current large scale implementations of deep learning and data mining require thousands of processors, massive amounts of off-chip memory, and consume gigajoules of energy.

Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array

no code implementations19 Jun 2014 Sukru Burc Eryilmaz, Duygu Kuzum, Rakesh Jeyasingh, Sang-Bum Kim, Matthew BrightSky, Chung Lam, H. -S. Philip Wong

Recent advances in neuroscience together with nanoscale electronic device technology have resulted in huge interests in realizing brain-like computing hardwares using emerging nanoscale memory devices as synaptic elements.

Experimental Demonstration of Array-level Learning with Phase Change Synaptic Devices

no code implementations29 May 2014 S. Burc Eryilmaz, Duygu Kuzum, Rakesh G. D. Jeyasingh, Sang-Bum Kim, Matthew BrightSky, Chung Lam, H. -S. Philip Wong

We demonstrate, in hardware, that 2-D crossbar arrays of phase change synaptic devices can achieve associative learning and perform pattern recognition.

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