This paper presents a new approach for designing test sequences to be generated on–chip. The proposed technique is based on machine learning, and provides a way to generate effi...
Christophe Fagot, Patrick Girard, Christian Landra...
This paper reports developments on a best description template for teaching methods, whose descriptive elements will eventually be mapped to the elements of the IMS Learning Desig...
Abstract. Approximate Policy Iteration (API) is a reinforcement learning paradigm that is able to solve high-dimensional, continuous control problems. We propose to exploit API for...
This paper presents an unsupervised learning approach to building a non-English (Arabic) stemmer. The stemming model is based on statistical machine translation and it uses an Eng...
Gradient Boosted Regression Trees (GBRT) are the current state-of-the-art learning paradigm for machine learned websearch ranking — a domain notorious for very large data sets. ...
Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal...