Variance Reduced Stochastic Gradient Descent with Neighbors

Stochastic Gradient Descent (SGD) is a workhorse in machine learning, yet its slow convergence can be a computational bottleneck. Variance reduction techniques such as SAG, SVRG and SAGA have been proposed to overcome this weakness, achieving linear convergence. However, these methods are either based on computations of full gradients at pivot points, or on keeping per data point corrections in memory. Therefore speed-ups relative to SGD may need a minimal number of epochs in order to materialize. This paper investigates algorithms that can exploit neighborhood structure in the training data to share and re-use information about past stochastic gradients across data points, which offers advantages in the transient optimization phase. As a side-product we provide a unified convergence analysis for a family of variance reduction algorithms, which we call memorization algorithms. We provide experimental results supporting our theory.Comment: Appears in: Advances in Neural Information Processing Systems 28 (NIPS 2015). 13 page

Barrier Frank-Wolfe for Marginal Inference

We introduce a globally-convergent algorithm for optimizing the tree-reweighted (TRW) variational objective over the marginal polytope. The algorithm is based on the conditional gradient method (Frank-Wolfe) and moves pseudomarginals within the marginal polytope through repeated maximum a posteriori (MAP) calls. This modular structure enables us to leverage black-box MAP solvers (both exact and approximate) for variational inference, and obtains more accurate results than tree-reweighted algorithms that optimize over the local consistency relaxation. Theoretically, we bound the sub-optimality for the proposed algorithm despite the TRW objective having unbounded gradients at the boundary of the marginal polytope. Empirically, we demonstrate the increased quality of results found by tightening the relaxation over the marginal polytope as well as the spanning tree polytope on synthetic and real-world instances.Comment: 25 pages, 12 figures, To appear in Neural Information Processing Systems (NIPS) 2015, Corrected reference and cleaned up bibliograph

A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines

Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neural network weights. Conventional processing architectures are not well suited for simulating neural networks, often requiring large amounts of energy and time. Additionally, synapses in biological neural networks are not binary connections, but exhibit a nonlinear response function as neurotransmitters are emitted and diffuse between neurons. Inspired by neuroscience principles, we present a digital neuromorphic architecture, the Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex synaptic response functions without requiring additional hardware components. We consider the paradigm of spiking neurons with temporally coded information as opposed to non-spiking rate coded neurons used in most neural networks. In this paradigm we examine liquid state machines applied to speech recognition and show how a liquid state machine with temporal dynamics maps onto the STPU-demonstrating the flexibility and efficiency of the STPU for instantiating neural algorithms.Comment: 8 pages, 4 Figures, Preprint of 2017 IJCN

What the brain 'Likes': neural correlates of providing feedback on social media.

Evidence increasingly suggests that neural structures that respond to primary and secondary rewards are also implicated in the processing of social rewards. The 'Like'-a popular feature on social media-shares features with both monetary and social rewards as a means of feedback that shapes reinforcement learning. Despite the ubiquity of the Like, little is known about the neural correlates of providing this feedback to others. In this study, we mapped the neural correlates of providing Likes to others on social media. Fifty-eight adolescents and young adults completed a task in the MRI scanner designed to mimic the social photo-sharing app Instagram. We examined neural responses when participants provided positive feedback to others. The experience of providing Likes to others on social media related to activation in brain circuity implicated in reward, including the striatum and ventral tegmental area, regions also implicated in the experience of receiving Likes from others. Providing Likes was also associated with activation in brain regions involved in salience processing and executive function. We discuss the implications of these findings for our understanding of the neural processing of social rewards, as well as the neural processes underlying social media use

Mindreading in individuals with an empathizing versus systemizing cognitive style An fMRI study

Our fMRI study compares the neural correlates of face-based mindreading in healthy individuals with an empathizing (n=12) versus systemizing cognitive style (n=12). The empathizing group consists of individuals that score high on empathizing and low on systemizing, while the systemizing group consists of individuals with an opposite cognitive pattern. We hypothesize that the empathizing group will show stronger simulation-type neural activity (e.g., in mirror neuron areas, medial prefrontal cortex, anterior cingulate cortex) or simulation-related neural activity (e.g., in areas involved in perspective taking and experiential processing) compared to the systemizing group. As hypothesized, our study reveals that the empathizing group shows significantly stronger activity in mirror neuron areas of the brain, such as the left inferior frontal gyrus and inferior parietal lobe, and in temporal areas involved in perspective taking and autobiographical memory. Moreover, the empathizing group, but not the systemizing group, shows activity in the medial prefrontal cortex and anterior cingulate cortex which have been related to simulation-type neural activity in the brain and are central to mindreading. Also, the systemizing group shows significantly stronger activity in the left parahippocampal gyrus. In conclusion, both the empathizing and systemizing individuals show simulation-type and simulation-related neural activity during face-based mindreading. However, more neural activity indicative of simulation-based processing is seen in the empathizing individuals, while more neural activity indicative of non-simulation-based processing is seen in the systemizing individuals

Infants' neural processing of facial attractiveness

textThe relationship between infants’ neural processing of and visual preferences for attractive and unattractive faces was investigated through the integration of event-related potential and preferential looking methods. Six-month-olds viewed color images of female faces previously rated by adults for attractiveness. The faces were presented in contrasting pairs of attractiveness (attractive/unattractive) for 1.5-second durations. The results showed that compared to attractive faces, unattractive faces elicited larger N290 amplitudes at left hemisphere electrode sites (PO9) and smaller P400 amplitudes at electrode sites across both hemispheres (PO9 and PO10). There were no significant differences between infants’ overall looking times based on attractiveness, however, a significant relationship was found between amplitude and trial looking time; larger N290 amplitudes were associated with longer trial looking times. The results suggest that compared to attractive faces, unattractive faces require greater cognitive resources and longer initial attention for visual processing.Psycholog