Articles — Two-column

Paper presented at ICCV 2019. This paper targets the task with discrete and periodic class labels (e.g., pose/orientation estimation) in the context of deep learning. The commonly used cross-entropy or regression loss is not well matched to this problem as they ignore the periodic nature of the labels and the class similarity, or assume labels are continuous value. We propose to incorporate inter-class correlations in a Wasserstein training framework by pre-defining (i.e., using arc length of a circle) or adaptively learning the ground metric. We extend the ground metric as a linear, convex or concave increasing function w.r.t. arc length from an optimization perspective. We also propose to construct the conservative target labels which model the inlier and outlier noises using a wrapped unimodal-uniform mixture distribution. Unlike the one-hot setting, the conservative label makes the computation of Wasserstein distance more challenging. We systematically conclude the practical closed-form solution of Wasserstein distance for pose data with either one-hot or conservative target label. We evaluate our method on head, body, vehicle and 3D object pose benchmarks with exhaustive ablation studies. The Wasserstein loss obtaining superior performance over the current methods, especially using convex mapping function for ground metric, conservative label, and closed-form solution.

Encontrar las características, de los diferentes tipos de de agresión registrados desde el 1 de enero de 2015 hasta septiembre de 2018, el en la ciudad de Denver.

Classifying pulmonary nodule CT images as either benign or malignant, using a trained Residual Neural Network.

In this paper, we evaluate a baseline word embedding model for a set of clinical notes derived from patient records. For our baseline, we extract features for this embedding using the Word2Vec module from the gensim package. We also build two models, a word2vec skipgram model with negative sampling and a positive point-wise mutual information (PPMI) model by training on the processed clinical notes. Our evaluation shows that both the PPMI and the skipgram models show improved results for medically-related terms when compared with the baseline model. PPMI shows the best result out of all three models.

In this project we simulate NMOS and PMOS transistor circuit in cadence virtusso tool and the I/V characteristics of PMOS and NMOS are observed

The purpose of this paper is to examine how chronobiologically effective cabin lighting increases comfort and well-being for passengers on long-haul flights.

comparison of lex and a c scanner

Report of the laboratory 04 of compiler class

Laboratory where LEX is compared to C