18 research outputs found
Combining Static and Dynamic Features for Multivariate Sequence Classification
Model precision in a classification task is highly dependent on the feature
space that is used to train the model. Moreover, whether the features are
sequential or static will dictate which classification method can be applied as
most of the machine learning algorithms are designed to deal with either one or
another type of data. In real-life scenarios, however, it is often the case
that both static and dynamic features are present, or can be extracted from the
data. In this work, we demonstrate how generative models such as Hidden Markov
Models (HMM) and Long Short-Term Memory (LSTM) artificial neural networks can
be used to extract temporal information from the dynamic data. We explore how
the extracted information can be combined with the static features in order to
improve the classification performance. We evaluate the existing techniques and
suggest a hybrid approach, which outperforms other methods on several public
datasets.Comment: Presented at IEEE DSAA 201
Inimaju arvutuslikke protsesside mÔistmine masinÔpe mudelite tÔlgendamise kaudu. AndmepÔhine lÀhenemine arvutuslikku neuroteadusesse
Modelleerimine on inimkonna pĂ”line viis keerulistest nĂ€htustest arusaamiseks. Planeetide liikumise mudel, gravitatsiooni mudel ja osakestefĂŒĂŒsika standardmudel on nĂ€ited selle lĂ€henemise edukusest. Neuroteaduses on olemas kaks viisi mudelite loomiseks: traditsiooniline hĂŒpoteesipĂ”hine lĂ€henemine, mille puhul kĂ”igepealt mudel sĂ”nastatakse ja alles siis valideeritakse andmete peal; ja uuem andmepĂ”hine lĂ€henemine, mis toetub masinĂ”pele, et sĂ”nastada mudeleid automaatselt.
HĂŒpoteesipĂ”hine viis annab tĂ€ieliku mĂ”istmise sellest, kuidas mudel töötab, aga nĂ”uab aega, kuna iga hĂŒpotees peab olema sĂ”nastatud ja valideeritud kĂ€sitsi. AndmepĂ”hine lĂ€henemine toetub ainult andmetele ja arvutuslikele ressurssidele mudelite otsimisel, aga ei seleta kuidas tĂ€pselt mudel jĂ”uab oma tulemusteni. Me vĂ€idame, et neuroandmestike suur hulk ja nende mahu kiire kasv nĂ”uab andmepĂ”hise lĂ€henemise laiemat kasutuselevĂ”ttu neuroteaduses, nihkes uurija rolli mudelite tööprintsiipide tĂ”lgendamisele.
Doktoritöö koosneb kolmest nĂ€itest neuroteaduse teadmisi avastamisest masinĂ”ppe tĂ”lgendamismeetodeid kasutades. Esimeses uuringus tĂ”lgendatava mudeli abiga me kirjeldame millised ajas muutuvad sageduskomponendid iseloomustavad inimese ajusignaali visuaalsete objektide tuvastamise ĂŒlesande puhul. Teises uuringus vĂ”rdleme omavahel signaale inimese aju ventraalses piirkonnas ja konvolutsiooniliste tehisnĂ€rvivĂ”rkude aktivatsioone erinevates kihtides. SÀÀrane vĂ”rdlus vĂ”imaldas meil kinnitada hĂŒpoteesi, et mĂ”lemad sĂŒsteemid kasutavad hierarhilist struktuuri. Viimane nĂ€ide kasutab topoloogiat sĂ€ilitavat mÔÔtmelisuse vĂ€hendamise ja visualiseerimise meetodit, et nĂ€ha, millised ajusignaalid ja mĂ”tteseisundid on ĂŒksteisele sarnased.
Viimased tulemused masinÔppes ja tehisintellektis nÀitasid et mÔned mehhanismid meie ajus on sarnased mehhanismidega, milleni jÔuavad Ôppimise kÀigus masinÔppe algoritmid. Oma tööga me rÔhutame masinÔppe mudelite tÔlgendamise tÀhtsust selliste mehhanismide avastamiseks.Building a model of a complex phenomenon is an ancient way of gaining knowledge and understanding of the reality around us. Models of planetary motion, gravity, particle physics are examples of this approach. In neuroscience, there are two ways of coming up with explanations of reality: a traditional hypothesis-driven approach, where a model is first formulated and then tested using the data, and a more recent data-driven approach, that relies on machine learning to generate models automatically.
Hypothesis-driven approach provides full understanding of the model, but is time-consuming as each model has to be conceived and tested manually. Data-driven approach requires only the data and computational resources to sift through potential models, saving time, but leaving the resulting model itself to be a black box. Given the growing amount of neural data, we argue in favor of a more widespread adoption of the data-driven approach, reallocating part of the human effort from manual modeling.
The thesis is based on three examples of how interpretation of machine-learned models leads to neuroscientific insights on three different levels of neural organization. Our first interpretable model is used to characterize neural dynamics of localized neural activity during the task of visual perceptual categorization. Next, we compare the activity of human visual system with the activity of a convolutional neural network, revealing explanations about the functional organization of human visual cortex. Lastly, we use dimensionality reduction and visualization techniques to understand relative organization of mental concepts within a subject's mental state space and apply it in the context of brain-computer interfaces.
Recent results in neuroscience and AI show similarities between the mechanisms of both systems. This fact endorses the relevance of our approach: interpreting the mechanisms employed by machine learning models can shed light on the mechanisms employed by our brainhttps://www.ester.ee/record=b536057
Identifying task-relevant spectral signatures of perceptual categorization in the human cortex
International audienceAbstract Human brain has developed mechanisms to efficiently decode sensory information according to perceptual categories of high prevalence in the environment, such as faces, symbols, objects. Neural activity produced within localized brain networks has been associated with the process that integrates both sensory bottom-up and cognitive top-down information processing. Yet, how specifically the different types and components of neural responses reflect the local networksâ selectivity for categorical information processing is still unknown. In this work we train Random Forest classification models to decode eight perceptual categories from broad spectrum of human intracranial signals (4â150âHz, 100 subjects) obtained during a visual perception task. We then analyze which of the spectral features the algorithm deemed relevant to the perceptual decoding and gain the insights into which parts of the recorded activity are actually characteristic of the visual categorization process in the human brain. We show that network selectivity for a single or multiple categories in sensory and non-sensory cortices is related to specific patterns of power increases and decreases in both low (4â50âHz) and high (50â150âHz) frequency bands. By focusing on task-relevant neural activity and separating it into dissociated anatomical and spectrotemporal groups we uncover spectral signatures that characterize neural mechanisms of visual category perception in human brain that have not yet been reported in the literature
Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex
International audienc
Results of games between multiplayer DQN, single-player DQN and four hand-coded algorithms.
<p>The values correspond to an average of 10 games with different random seeds. Score difference means the points scored by the agent mentioned first minus the points of the agent mentioned second. (a) Multi and Single DQNâs performance against each other and against <i>HC</i><sub><i>N</i>=4</sub> in function of training time. (b) Scores of Single DQN and Multi DQN agents against 4 versions of a handcoded agent trying to keep the center of the paddle level with the ball. N refers to the number of frames a selected action is repeated by the algorithm before selecting a new action (the smaller the better).</p