An Evaluation of a Teaching Interaction Procedure Implemented in a Recess Setting

The teaching interaction procedure (TIP) is a strategy that has been demonstrated as effective in promoting social skill acquisition in school settings for young students with social communication deficits (Leaf et al., 2009; Leaf et al., 2010). However, a frequently cited criticism of social skills training is the lack of generalizability of target skills to novel contexts (Bellini et al., 2007). The purpose of the study was to evaluate a TIP-based social skills intervention conducted on the playground, intended to promote generalizability through training in naturalistic settings and to evaluate generalizability of skill acquisition to the classroom. Eight students 5-8 years old with an educational classification of autism or developmental delay participated in the study. The primary dependent variable was skill acquisition in the playground setting, and a secondary measure was generalized skill acquisition to the classroom setting. Target skills included appropriate body language, participation, and responding to initiations. A multi-probe design embedded within a multiple baseline design across target skills with concurrent replication across participants was used to evaluate the primary and secondary measures. Overall, results suggest that increases in skill acquisition were observed during implementation of the TIP across most participants and skills in both training and generalization phases. However, substantial variability was noted across participants related to maintaining skill acquisition during maintenance and follow-up phases in both the training and generalization settings. Limitations of these results are discussed as well as implications for school practitioners

Normalization and Generalization in Deep Learning

In this thesis, we discuss the importance of data normalization in deep learning and its relationship with generalization. Normalization is a staple of deep learning architectures and has been shown to improve the stability and generalizability of deep learning models, yet the reason why these normalization techniques work is still unknown and is an active area of research. Inspired by this uncertainty, we explore how different normalization techniques perform when employed in different deep learning architectures, while also exploring generalization and metrics associated with generalization in congruence with our investigation into normalization. The goal behind our experiments was to investigate if there exist any identifiable trends for the different normalization methods across an array of different training schemes with respect to the various metrics employed. We found that class similarity was seemingly the strongest predictor for train accuracy, test accuracy, and generalization ratio across all employed metrics. Overall, BatchNorm and EvoNormBO generally performed the best on measures of test and train accuracy, while InstanceNorm and Plain performed the worst

Early Recognition of Burn- and Trauma-Related Acute Kidney Injury: A Pilot Comparison of Machine Learning Techniques.

Severely burned and non-burned trauma patients are at risk for acute kidney injury (AKI). The study objective was to assess the theoretical performance of artificial intelligence (AI)/machine learning (ML) algorithms to augment AKI recognition using the novel biomarker, neutrophil gelatinase associated lipocalin (NGAL), combined with contemporary biomarkers such as N-terminal pro B-type natriuretic peptide (NT-proBNP), urine output (UOP), and plasma creatinine. Machine learning approaches including logistic regression (LR), k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), and deep neural networks (DNN) were used in this study. The AI/ML algorithm helped predict AKI 61.8 (32.5) hours faster than the Kidney Disease and Improving Global Disease Outcomes (KDIGO) criteria for burn and non-burned trauma patients. NGAL was analytically superior to traditional AKI biomarkers such as creatinine and UOP. With ML, the AKI predictive capability of NGAL was further enhanced when combined with NT-proBNP or creatinine. The use of AI/ML could be employed with NGAL to accelerate detection of AKI in at-risk burn and non-burned trauma patients

Robustness and Generalization

We derive generalization bounds for learning algorithms based on their robustness: the property that if a testing sample is "similar" to a training sample, then the testing error is close to the training error. This provides a novel approach, different from the complexity or stability arguments, to study generalization of learning algorithms. We further show that a weak notion of robustness is both sufficient and necessary for generalizability, which implies that robustness is a fundamental property for learning algorithms to work

Exploring the generalizability of visual search strategies

When searching our visual environment, we often have multiple strategies available. For example, when looking for apples on a supermarket shelf, you can look for red things, round things, or you can just search serially through all items. How do we choose a strategy? Recent research on this question has revealed substantial variation across individuals in attentional control strategies when approaching visual search tasks, and the strategies have been found to be reliable within subjects. However, strategies on one visual search task have failed to generalize across different paradigms that assess various components of strategy use (Clarke et al., 2018). Thus, evidence for whether strategies generalize beyond a single paradigm remains scarce. While previous tests of generalizability used paradigms that vary in many ways, we focused on a single strategy component that could be preserved across tasks, with several other changes. In two experiments, we assessed the correlation between individuals' strategies in the Standard Adaptive Choice Visual Search (Standard ACVS; Irons & Leber, 2018) and a modified novel visual search task, Spatial ACVS. In the Standard ACVS, participants seeking to perform optimally have to enumerate subsets of different colored squares and identify the smaller subset to choose a target from. Similarly, in the Spatial ACVS, participants seeking optimal performance have to enumerate spatially separate subsets of squares (one on the left and one on the right side of the display), choosing the target in the smaller subset. Participants finished both tasks in the same order in one experimental session. Results showed a positive correlation in optimal target choices between the two tasks (r = .38), indicating similar strategy usage. Future studies can focus on what strategy components tend more to be generalized across tasks and whether an individual's strategy can generalize to tasks with a combination of several strategy components. The ultimate goal is to fully understand how people choose their attentional control strategies in unconstrained, real-life environments.NSF BCS-1632296No embargoAcademic Major: Psycholog

Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel

Explaining generalizations and preventing over-confident predictions are central goals of studies on the loss landscape of neural networks. Flatness, defined as loss invariability on perturbations of a pre-trained solution, is widely accepted as a predictor of generalization in this context. However, the problem that flatness and generalization bounds can be changed arbitrarily according to the scale of a parameter was pointed out, and previous studies partially solved the problem with restrictions: Counter-intuitively, their generalization bounds were still variant for the function-preserving parameter scaling transformation or limited only to an impractical network structure. As a more fundamental solution, we propose new prior and posterior distributions invariant to scaling transformations by \textit{decomposing} the scale and connectivity of parameters, thereby allowing the resulting generalization bound to describe the generalizability of a broad class of networks with the more practical class of transformations such as weight decay with batch normalization. We also show that the above issue adversely affects the uncertainty calibration of Laplace approximation and propose a solution using our invariant posterior. We empirically demonstrate our posterior provides effective flatness and calibration measures with low complexity in such a practical parameter transformation case, supporting its practical effectiveness in line with our rationale