3siThis paper proposes and experimentally assesses a machine learning approach for supporting the effective and efficient generation of synthetic memory reference traces for a wide range of application scenarios. The proposed approach makes a nice use of extended hierarchical Markov modelsreservedmixedAlfredo Cuzzocrea ; Enzo Mumolo; Marwan HassaniCuzzocrea, Alfredo Massimiliano; Mumolo, Enzo; Hassani, Marwa
[EN] Machine learning has becoming a trending topic in the last years, being now one of the most de...
Abstract This paper presents work on using hierarchical long term memory to reduce the memory requir...
In this master thesis project, we have researched how a theoretical model of the neo-cortex can be i...
This paper proposes and experimentally assesses a machine learning approach for supporting the effec...
Driven by several real-life case studies and in-lab developments, synthetic memory reference generat...
4siDriven by several real-life case studies and in-lab developments, synthetic memory reference gen...
\u3cp\u3eIn this paper we introduce a technique for the synthetic generation of memory references wh...
Trace-driven simulation is a popular technique useful in many applications, as for example analysis ...
This thesis presents novel methods for creating and improving hierarchical hidden Markov models. Th...
Abstract. Structured Hidden Markov Model (S-HMM) is a variant of Hierarchical Hidden Markov Model th...
We present a fast algorithm for learning the parameters of the abstract hidden Markov model, a type ...
We consider problems of sequence processing and propose a solution based on a discrete state model i...
Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov co...
Machine learning has becoming a trending topic in the last years, being now one of the most demandin...
Inspired by the hierarchical hidden Markov models (HHMM), we present the hier-archical semi-Markov c...
[EN] Machine learning has becoming a trending topic in the last years, being now one of the most de...
Abstract This paper presents work on using hierarchical long term memory to reduce the memory requir...
In this master thesis project, we have researched how a theoretical model of the neo-cortex can be i...
This paper proposes and experimentally assesses a machine learning approach for supporting the effec...
Driven by several real-life case studies and in-lab developments, synthetic memory reference generat...
4siDriven by several real-life case studies and in-lab developments, synthetic memory reference gen...
\u3cp\u3eIn this paper we introduce a technique for the synthetic generation of memory references wh...
Trace-driven simulation is a popular technique useful in many applications, as for example analysis ...
This thesis presents novel methods for creating and improving hierarchical hidden Markov models. Th...
Abstract. Structured Hidden Markov Model (S-HMM) is a variant of Hierarchical Hidden Markov Model th...
We present a fast algorithm for learning the parameters of the abstract hidden Markov model, a type ...
We consider problems of sequence processing and propose a solution based on a discrete state model i...
Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov co...
Machine learning has becoming a trending topic in the last years, being now one of the most demandin...
Inspired by the hierarchical hidden Markov models (HHMM), we present the hier-archical semi-Markov c...
[EN] Machine learning has becoming a trending topic in the last years, being now one of the most de...
Abstract This paper presents work on using hierarchical long term memory to reduce the memory requir...
In this master thesis project, we have researched how a theoretical model of the neo-cortex can be i...