title: Semi-supervised learning using Siamese networks

authors: Sahito, Attaullah

subjects: computer science

abstract: Neural networks have been successfully used as classification models yielding state-of-the-art results when trained on a large number of labeled samples. These models, however, are more difficult to train successfully for semi-supervised problems where small amounts of labeled instances are available along with a large number of unlabeled instances. This work explores a new training method for semi-supervised learning that is based on similarity function learning using a Siamese network to obtain a suitable embedding. The learned representations are discriminative in Euclidean space, and hence can be used for labeling unlabeled instances using a nearest-neighbor classifier. Confident predictions of unlabeled instances are used as true labels for retraining the Siamese network on the expanded training set. This process is applied iteratively. We perform an empirical study of this iterative self-training algorithm. For improving unlabeled predictions, local learning with global consistency [22] is also evaluated.

text: Semi-supervised learning using Siamese networks Neural networks have been successfully used as classification models yielding state-of-the-art results when trained on a large number of labeled samples. These models, however, are more difficult to train successfully for semi-supervised problems where small amounts of labeled instances are available along with a large number of unlabeled instances. This work explores a new training method for semi-supervised learning that is based on similarity function learning using a Siamese network to obtain a suitable embedding. The learned representations are discriminative in Euclidean space, and hence can be used for labeling unlabeled instances using a nearest-neighbor classifier. Confident predictions of unlabeled instances are used as true labels for retraining the Siamese network on the expanded training set. This process is applied iteratively. We perform an empirical study of this iterative self-training algorithm. For improving unlabeled predictions, local learning with global consistency [22] is also evaluated.

year: 2019

title: Food miles or carbon emissions? Exploring labelling preference for food transport footprint with a stated choice study

authors: Caputo, Vincenzina

abstract: The ecological footprint of food transport can be communicated using carbon dioxide emissions (CO₂ label) or by providing information about both the length of time and the mileage travelled (food miles label). We use stated choice data to estimate conventional unobserved taste heterogeneity models and extend them to a specification that also addresses attribute nonattendance. The implied posterior distributions of the marginal willingness to pay values are compared graphically and are used in validation regressions. We find strong bimodality of taste distribution as the emerging feature, with different groups of subjects having low and high valuations for these labels. The best fitting model shows that CO₂ and food miles valuations are much correlated. CO₂ valuations can be high even for those respondents expressing low valuations for food miles. However, the reverse is not true. Taken together, the results suggest that consumers tend to value the CO₂ label at least as much and sometimes more than the food miles label.

text: Food miles or carbon emissions? Exploring labelling preference for food transport footprint with a stated choice study The ecological footprint of food transport can be communicated using carbon dioxide emissions (CO₂ label) or by providing information about both the length of time and the mileage travelled (food miles label). We use stated choice data to estimate conventional unobserved taste heterogeneity models and extend them to a specification that also addresses attribute nonattendance. The implied posterior distributions of the marginal willingness to pay values are compared graphically and are used in validation regressions. We find strong bimodality of taste distribution as the emerging feature, with different groups of subjects having low and high valuations for these labels. The best fitting model shows that CO₂ and food miles valuations are much correlated. CO₂ valuations can be high even for those respondents expressing low valuations for food miles. However, the reverse is not true. Taken together, the results suggest that consumers tend to value the CO₂ label at least as much and sometimes more than the food miles label.

year: 2013

title: Chief’s Mana in action; a study investigating the team’s perspective.

authors: Howarth, Joelene

subjects: Sports psychology

abstract: Team culture, and its relevance to performance behaviour and attitudes, has recently gained increased focus in sport psychology research and practice (Schroeder, 2010; Cruickshank & Collins 2012; Morgan, Fletcher & Sarkar, 2014). Cruickshank and Collins (2012) state that“high performing cultures prevail when the shared perception and action of the elite team environment members; (a) supports sustained optimal performance; (b) persists across time in the face of variable results(i.e. wins, losses, ties); and most importantly, (c) leads to consistently high performance” (p.340). Spink et al. (2013) found preliminary evidence that a positive psychological climate, as defined by the significance and meaning of the work environment to the individual, is also linked to increased athlete effort. Various researchers suggests that it is a difficult task to change, cultivate and maintain a team culture, that is the social and psychological environment, which promotes best performance (Cruickshank & Collins 2012; Schroeder, 2010).

text: Chief’s Mana in action; a study investigating the team’s perspective. Team culture, and its relevance to performance behaviour and attitudes, has recently gained increased focus in sport psychology research and practice (Schroeder, 2010; Cruickshank & Collins 2012; Morgan, Fletcher & Sarkar, 2014). Cruickshank and Collins (2012) state that“high performing cultures prevail when the shared perception and action of the elite team environment members; (a) supports sustained optimal performance; (b) persists across time in the face of variable results(i.e. wins, losses, ties); and most importantly, (c) leads to consistently high performance” (p.340). Spink et al. (2013) found preliminary evidence that a positive psychological climate, as defined by the significance and meaning of the work environment to the individual, is also linked to increased athlete effort. Various researchers suggests that it is a difficult task to change, cultivate and maintain a team culture, that is the social and psychological environment, which promotes best performance (Cruickshank & Collins 2012; Schroeder, 2010).

year: 2016