Intent Detection is a crucial component of Dialogue Systems wherein the objective is to classify a user utterance into one of multiple pre-defined intents. A pre-requisite for developing an effective intent identifier is a training dataset labeled …
Identifying all possible user intents for a dialog system at design time is challenging even for skilled domain experts. For practical applications, novel intents may have to be inferred incrementally on the fly. This typically entails repeated …
Machine-learning models are widely used for performance prediction due to its applications in the advancements of hardware-software co-development. Several researchers have focused on predicting the performance of an unknown target platform (or system) from the known performance of a particular platform (or system); we call this as the cross-platform prediction. Transfer learning is used to reuse previously gained knowledge on a similar task. In this paper, we use transfer learning for solving two problems cross-platform prediction and cross-systems prediction. Our result shows the prediction error of 15% in case of cross-systems (Simulated to Physical) prediction whereas in case of the cross-platform prediction error of 17% for simulation-based X86 to ARM prediction and 23% for physical Intel Core to Intel-Xeon system using best performing tree-based machine-learning model. We have also experimented with dimensionality reduction using PCA and selection of best hyper-parameters using grid search techniques.
Performance prediction is an active area of research due to its applicability in the advancements of hardware-software co-development. Several empirical machine-learning models, such as linear models, non-linear models, probabilistic models, tree-based models and, neural networks, are used for performance prediction. Furthermore, the prediction model's accuracy may vary depending on performance data gathered for different software types (compute-bound, memory-bound) and different hardware (simulation-based or physical systems). We have examined fourteen machine-learning models on simulation-based hardware and physical systems by executing several benchmark programs with different computation and data access patterns. Our results show that the tree-based machine-learning models outperform all other models with median absolute percentage error (MedAPE) of less than 5% followed by bagging and boosting models that help to improve weak learners. We have also observed that prediction accuracy is higher on simulation-based hardware due to its deterministic nature as compared to physical systems. Moreover, in physical systems, the prediction accuracy of memory-bound algorithms is higher as compared to compute-bound algorithms due to manufacturer variability in processors.
In this paper, we have have described two key improvements to NILMTK; a rewritten model interface to simplify authoring of new disaggregation algorithms, and a new experiment API through which algorithmic comparisons can be specified with relatively little model knowledge. In addition, we have introduced NILMTKcontrib, a new repository containing 3 benchmarks and 9 modern disaggregation algorithms. In addition, such algorithms will be continuously evaluated in a range of pre-defined scenarios to produce an ongoing NILM competition
Monument recognition is a challenging problem in the domain of image classification due to huge variations in the architecture of different monuments. Different orientations of the structure play an important role in the recognition of the monuments in their images. The paper proposes an approach for classification of various monuments based on the features of the monument images. The state-of-the-art Deep Convolutional Neural Networks (DCNN) is used for extracting representations. The model is trained on representations of different Indian monuments, obtained from cropped images, which exhibit geographic and cultural diversity. Experiments have been carried out on the manually acquired dataset that is composed of images of different monuments where each monument has images from different angular views. The experiments show the performance of the model when it is trained on representations of cropped images of the various monuments. The overall accuracy achieved is 92.7%, using DCNN, for a total of 100 different monuments that have been considered in the dataset for classification.