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representational learning

Adam F Osth, Anna Jansson, Simon Dennis, Andrew Heathcote
A robust finding in recognition memory is that performance declines monotonically across test trials. Despite the prevalence of this decline, there is a lack of consensus on the mechanism responsible. Three hypotheses have been put forward: (1) interference is caused by learning of test items (2) the test items cause a shift in the context representation used to cue memory and (3) participants change their speed-accuracy thresholds through the course of testing. We implemented all three possibilities in a combined model of recognition memory and decision making, which inherits the memory retrieval elements of the Osth and Dennis (2015) model and uses the diffusion decision model (DDM: Ratcliff, 1978) to generate choice and response times...
May 17, 2018: Cognitive Psychology
Danny Spampinato, Pablo Celnik
Acquiring complex motor skills involves learning a number of distinct motor components. Two fundamental elements that constitute a skill are the internal representation (i.e., the calibration of a sensorimotor map) and the sequence of movements needed to execute the task. Learning each of these likely rely on different neural substrates such as the cerebellum and primary motor cortex (M1), and physiological mechanisms. However, the specific neurophysiological processes underlying the acquisition of these components remains poorly understood...
March 27, 2018: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
Rachel Ryskin, Zhenghan Qi, Natalie V Covington, Melissa Duff, Sarah Brown-Schmidt
Verb bias-the co-occurrence frequencies between a verb and the syntactic structures it may appear with-is a critical and reliable linguistic cue for online sentence processing. In particular, listeners use this information to disambiguate sentences with multiple potential syntactic parses (e.g., Feel the frog with the feather.). Further, listeners dynamically update their representations of specific verbs in the face of new evidence about verb-structure co-occurrence. Yet, little is known about the biological memory systems that support the use and dynamic updating of verb bias...
May 15, 2018: Brain and Language
David Hallac, Sagar Vare, Stephen Boyd, Jure Leskovec
Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters . For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions ( i.e. , walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series...
August 2017: KDD: Proceedings
Yingxiang Huang, Junghye Lee, Shuang Wang, Jimeng Sun, Hongfang Liu, Xiaoqian Jiang
BACKGROUND: Data sharing has been a big challenge in biomedical informatics because of privacy concerns. Contextual embedding models have demonstrated a very strong representative capability to describe medical concepts (and their context), and they have shown promise as an alternative way to support deep-learning applications without the need to disclose original data. However, contextual embedding models acquired from individual hospitals cannot be directly combined because their embedding spaces are different, and naive pooling renders combined embeddings useless...
May 16, 2018: JMIR Medical Informatics
Alfred O Effenberg, Gerd Schmitz
Many domains of human behavior are based on multisensory representations. Knowledge about the principles of multisensory integration is useful to configure real-time movement information for the online support of perceptuomotor processes (motor perception, control, and learning). A powerful method for generating real-time information is movement sonification. Remarkable evidence exists on movement-acoustic real-time information being effective in behavioral domains (music training, motor rehabilitation, sports)...
May 16, 2018: Annals of the New York Academy of Sciences
Wanwen Zeng, Mengmeng Wu, Rui Jiang
BACKGROUND: Precise identification of three-dimensional genome organization, especially enhancer-promoter interactions (EPIs), is important to deciphering gene regulation, cell differentiation and disease mechanisms. Currently, it is a challenging task to distinguish true interactions from other nearby non-interacting ones since the power of traditional experimental methods is limited due to low resolution or low throughput. RESULTS: We propose a novel computational framework EP2vec to assay three-dimensional genomic interactions...
May 9, 2018: BMC Genomics
Annabelle S Redfern, Christopher P Benton
In this study, we investigate the contribution of expression variability in the formation of face representations. We trained participants to learn new identities from face images either low or high in expressiveness, and compared their performance in a recognition test. After low expressiveness training, recognition of novel test images was modulated by image expressiveness: the more expressive the image, the slower the response. This differed from recognition after high expressiveness training, which showed little evidence of expression dependence...
May 12, 2018: Vision Research
Evgeny Putin, Arip Asadulaev, Yan Ivanenkov, Vladimir Aladinskiy, Benjamin Sánchez-Lengeling, Alán Aspuru-Guzik, Alex Zhavoronkov
In silico modeling is a crucial milestone in modern drug design & development. Although computer-aided approaches in this field are well-studied, the application of deep learning methods in this research area is at the beginning. In this work, we present an original deep neural network (DNN) architecture named RANC (Reinforced Adversarial Neural Computer) for the de novo design of novel small-molecule organic structures based on generative adversarial network (GAN) paradigm and reinforcement learning (RL)...
May 15, 2018: Journal of Chemical Information and Modeling
Benedicte M Babayan, Naoshige Uchida, Samuel J Gershman
Learning to predict future outcomes is critical for driving appropriate behaviors. Reinforcement learning (RL) models have successfully accounted for such learning, relying on reward prediction errors (RPEs) signaled by midbrain dopamine neurons. It has been proposed that when sensory data provide only ambiguous information about which state an animal is in, it can predict reward based on a set of probabilities assigned to hypothetical states (called the belief state). Here we examine how dopamine RPEs and subsequent learning are regulated under state uncertainty...
May 14, 2018: Nature Communications
Brian A Anderson, Haena Kim
The role of associative reward learning in the guidance of feature-based attention is well established. The extent to which reward learning can modulate spatial attention has been much more controversial. At least one demonstration of a persistent spatial attention bias following space-based associative reward learning has been reported. At the same time, multiple other experiments have been published failing to demonstrate enduring attentional biases towards locations at which a target, if found, yields high reward...
May 11, 2018: Cognition
Xun Yang, Meng Wang, Dacheng Tao
Despite the promising progress made in recent years, person re-identification remains a challenging task due to complex variations in human appearances from different camera views. This paper presents a logistic discriminant metric learning method for this challenging problem. Different with most existing metric learning algorithms, it exploits both original data and auxiliary data during training, which is motivated by the new machine learning paradigm-learning using privileged information. Such privileged information is a kind of auxiliary knowledge, which is only available during training...
February 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Fang Zhao, Jiashi Feng, Jian Zhao, Wenhan Yang, Shuicheng Yan
Face recognition techniques have been developed significantly in recent years. However, recognizing faces with partial occlusion is still challenging for existing face recognizers, which is heavily desired in real-world applications concerning surveillance and security. Although much research effort has been devoted to developing face de-occlusion methods, most of them can only work well under constrained conditions, such as all of faces are from a pre-defined closed set of subjects. In this paper, we propose a robust LSTM-Autoencoders (RLA) model to effectively restore partially occluded faces even in the wild...
February 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Marta M Stepniewska-Dziubinska, Piotr Zielenkiewicz, Pawel Siedlecki
Motivation: Structure based ligand discovery is one of the most successful approaches for augmenting the drug discovery process. Currently, there is a notable shift towards machine learning (ML) methodologies to aid such procedures. Deep learning has recently gained considerable attention as it allows the model to "learn" to extract features that are relevant for the task at hand. Results: We have developed a novel deep neural network estimating the binding affinity of ligand-receptor complexes...
May 10, 2018: Bioinformatics
Povilas Karvelis, Aaron R Seitz, Stephen M Lawrie, Peggy Seriès
Recent theories propose that schizophrenia/schizotypy and autistic spectrum disorder are related to impairments in Bayesian inference i.e. how the brain integrates sensory information (likelihoods) with prior knowledge. However existing accounts fail to clarify: i) how proposed theories differ in accounts of ASD vs. schizophrenia and ii) whether the impairments result from weaker priors or enhanced likelihoods. Here, we directly address these issues by characterizing how 91 healthy participants, scored for autistic and schizotypal traits, implicitly learned and combined priors with sensory information...
May 14, 2018: ELife
Volker L Deringer, Chris J Pickard, Gábor Csányi
The allotropes of boron continue to challenge structural elucidation and solid-state theory. Here we use machine learning combined with random structure searching (RSS) algorithms to systematically construct an interatomic potential for boron. Starting from ensembles of randomized atomic configurations, we use alternating single-point quantum-mechanical energy and force computations, Gaussian approximation potential (GAP) fitting, and GAP-driven RSS to iteratively generate a representation of the element's potential-energy surface...
April 13, 2018: Physical Review Letters
Nobuyoshi Iwaki, Saeko Tanaka
False memories endorsed with higher confidence are more likely to be corrected by feedback than those endorsed with lower confidence (hypercorrection effect). Errors made with high confidence and correct responses made with low confidence are both associated with large meta-memory mismatches. Therefore, they both represent a type of unexpected event which automatically captures participant attention, such that correct information provided via feedback is well-encoded. On the other hand, a study that measured participants' perceived practical value for items suggested that voluntary allocation of attention might involve the hypercorrection effect...
May 9, 2018: Brain and Cognition
Jordan Crivelli-Decker, Liang-Tien Hsieh, Alex Clarke, Charan Ranganath
Many theoretical models suggest that neural oscillations play a role in learning or retrieval of temporal sequences, but the extent to which oscillations support sequence representation remains unclear. To address this question, we used scalp electroencephalography (EEG) to examine oscillatory activity over learning of different object sequences. Participants made semantic decisions on each object as they were presented in a continuous stream. For three "Consistent" sequences, the order of the objects was always fixed...
May 10, 2018: Neurobiology of Learning and Memory
Ralf-Dieter Hilgers, Malgorzata Bogdan, Carl-Fredrik Burman, Holger Dette, Mats Karlsson, Franz König, Christoph Male, France Mentré, Geert Molenberghs, Stephen Senn
BACKGROUND: IDeAl (Integrated designs and analysis of small population clinical trials) is an EU funded project developing new statistical design and analysis methodologies for clinical trials in small population groups. Here we provide an overview of IDeAl findings and give recommendations to applied researchers. METHOD: The description of the findings is broken down by the nine scientific IDeAl work packages and summarizes results from the project's more than 60 publications to date in peer reviewed journals...
May 11, 2018: Orphanet Journal of Rare Diseases
Cong Shi, Jiajun Li, Ying Wang, Gang Luo
This paper presents a lightweight statistical learning framework potentially suitable for low-cost event-based vision systems, where visual information is captured by a dynamic vision sensor (DVS) and represented as an asynchronous stream of pixel addresses (events) indicating a relative intensity change on those locations. A simple random ferns classifier based on randomly selected patch-based binary features is employed to categorize pixel event flows. Our experimental results demonstrate that compared to existing event-based processing algorithms, such as spiking convolutional neural networks (SCNNs) and the state-of-the-art bag-of-events (BoE)-based statistical algorithms, our framework excels in high processing speed (2× faster than the BoE statistical methods and >100× faster than previous SCNNs in training speed) with extremely simple online learning process, and achieves state-of-the-art classification accuracy on four popular address-event representation data sets: MNIST-DVS, Poker-DVS, Posture-DVS, and CIFAR10-DVS...
2018: IEEE Access: Practical Innovations, Open Solutions
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