Consider the sequential optimization of a continuous, possibly non-convex, and expensive to evaluate objective function $f$. The problem can be cast as a Gaussian Process (GP) bandit where $f$ lives in a reproducing kernel Hilbert space (RKHS). The state of the art analysis of several learning algorithms shows a significant gap between the lower and upper bounds on the simple regret performance. When $N$ is the number of exploration trials and $\gamma_N$ is the maximal information gain, we prove an $\tilde{\mathcal{O}}(\sqrt{\gamma_N/N})$ bound on the simple regret performance of a pure exploration algorithm that is significantly tighter than the existing bounds. We show that this bound is order optimal up to logarithmic factors for the cases where a lower bound on regret is known. To establish these results, we prove novel and sharp confidence intervals for GP models applicable to RKHS elements which may be of broader interest.

Natural conversations are filled with disfluencies.This study investigates if and how BERT understands disfluency with three experiments: (1) a behavioural study using a downstream task, (2) an analysis of sentence embeddings and (3) an analysis of the attention mechanism on disfluency.The behavioural study shows that without fine-tuning on disfluent data, BERT does not suffer significant performance loss when presented disfluent compared to fluent inputs (exp1).Analysis on sentence embeddings of disfluent and fluent sentence pairs reveals that the deeper the layer, the more similar their representation (exp2).This indicates that deep layers of BERT become relatively invariant to disfluency.We pinpoint attention as a potential mechanism that could explain this phenomenon (exp3).Overall, the study suggests that BERT has knowledge of disfluency structure.We emphasise the potential of using BERT to understand natural utterances without disfluency removal.

In this paper we propose a new biologically inspired model which simulates the visual pathways in the human brain used for classification of matching optical and sonar derived images. Marine mammals, such as dolphins, that live in waters with poor optical clarity and low light levels such as littoral zones, use a combination of optical vision and biosonar to navigate and hunt for prey. Given that dolphins have evolved a synergistic combination of optical visual input and acoustic/sonar input, the primary focus of this paper is on reaching a similar level of synergy for a diver or Autonomous Underwater Vehicle (AUV) platform equipped with a system to extend the range and resolution of vision in poor ambient visibility. We propose a biologically inspired model that combines and processes visual images acquired via optical and acoustic pathways and show that the combined model enhances the accuracy of automatic classification of target objects in underwater images.

In many coastal environments, particularly in tropical zones, coral reef ecosystems have exceptional biodiversity, contribute to coastal defense, provide unique and important habitats and valuable commercial resources. Assessment of environmental impacts on biodiversity in such areas are increasingly important to mitigate potential adverse effects on specific ecosystems. Visual classification of marine organisms is necessary for population estimates of individual species of corals or other benthic organisms. In this paper, we introduce a new image dataset of benthic organisms that are of different colors, shapes, scales, visibility and are taken from different viewpoints. We evaluate several different classification approaches on this dataset, and show that CQ-HMAX, our new biologically inspired approach to utilizing color information for object and scene recognition, that is inspired by the characteristics of color- and object-selective neurons in the high-level inferotemporal (IT) cortex of the primate visual system, results in better classification results in comparison with existing computational models such as support vectors machines, SIFT based approaches and the HMAX biologically inspired approach. We show that concatenating our model which encodes color information with the HMAX model which encodes grayscale shape information results in the highest classification accuracy.

In this paper, we explore the use of spatial and frequency information of features in the biologically inspired model of HMAX. We discuss and refine previous models which use a similar framework and build specialized features which are better tuned to image structures by using unsupervised methods of clustering and picking the most frequent features using the statistics of the occurrence of the features in different spatial zones. Our classification results on the Caltech 101 dataset show significant improvements of up to 6% compared to previous improvements of the biologically inspired model of HMAX.