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Unsupervised Machine Learning Hidden Markov Models in Python

#artificialintelligence

The Hidden Markov Model or HMM is all about learning sequences. A lot of the data that would be very useful for us to model is in sequences. Stock prices are sequences of prices. Language is a sequence of words. Credit scoring involves sequences of borrowing and repaying money, and we can use those sequences to predict whether or not you're going to default.


Unsupervised Machine Learning Hidden Markov Models in Python

#artificialintelligence

Created by Lazy Programmer Inc. English [Auto], Portuguese [Auto]Preview this Course - GET COUPON CODE The Hidden Markov Model or HMM is all about learning sequences. A lot of the data that would be very useful for us to model is in sequences. Stock prices are sequences of prices. Language is a sequence of words. Credit scoring involves sequences of borrowing and repaying money, and we can use those sequences to predict whether or not you're going to default.


Financial Engineering and Artificial Intelligence in Python

#artificialintelligence

Have you ever thought about what would happen if you combined the power of machine learning and artificial intelligence with financial engineering? Today, you can stop imagining, and start doing. This course will teach you the core fundamentals of financial engineering, with a machine learning twist. We will learn about the greatest flub made in the past decade by marketers posing as "machine learning experts" who promise to teach unsuspecting students how to "predict stock prices with LSTMs". You will learn exactly why their methodology is fundamentally flawed and why their results are complete nonsense.


Artificial Intelligence

#artificialintelligence

Learn to write programs using the foundational AI algorithms powering everything from NASA's Mars Rover to DeepMind's AlphaGo Zero. Learn to write AI programs using the algorithms powering everything from NASA's Mars Rover to DeepMind's AlphaGo Zero.


Amazon.com: Probability and Statistics for Data Science: Math + R + Data (Chapman & Hall/CRC Data Science Series) (9781138393295): Matloff, Norman: Books

#artificialintelligence

I believe that the book describes itself quite well when it says: Mathematically correct yet highly intuitive…This book would be great for a class that one takes before one takes my statistical learning class. I often run into beginning graduate Data Science students whose background is not math (e.g., CS or Business) and they are not ready…The book fills an important niche, in that it provides a self-contained introduction to material that is useful for a higher-level statistical learning course. I think that it compares well with competing books, particularly in that it takes a more "Data Science" and "example driven" approach than more classical books." "This text by Matloff (Univ. of California, Davis) affords an excellent introduction to statistics for the data science student…Its examples are often drawn from data science applications such as hidden Markov models and remote sensing, to name a few… All the models and concepts are explained well in precise mathematical terms (not presented as formal proofs), to help students gain an intuitive understanding."


A cell type-specific cortico-subcortical brain circuit for investigatory and novelty-seeking behavior

Science

Curiosity is what drives organisms to investigate each other and their environment. It is considered by many to be as intrinsic as hunger and thirst, but the neurobiological mechanisms behind curiosity have remained elusive. In mice, Ahmadlou et al. found that a specific population of genetically identified γ-aminobutyric acid (GABA)—ergic neurons in a brain region called the zona incerta receive excitatory input in the form of novelty and/or arousal information from the prelimbic cortex, and these neurons send inhibitory projections to the periaqueductal gray region (see the Perspective by Farahbakhsh and Siciliano). This circuitry is necessary for the exploration of new objects and conspecifics. Science , this issue p. [eabe9681][1]; see also p. [684][2] ### INTRODUCTION Motivational drives are internal states that can be different even in similar interactions with external stimuli. Curiosity as the motivational drive for novelty-seeking and investigating the surrounding environment is for survival as essential and intrinsic as hunger. Curiosity, hunger, and appetitive aggression drive three different goal-directed behaviors—novelty seeking, food eating, and hunting—but these behaviors are composed of similar actions in animals. This similarity of actions has made it challenging to study novelty seeking and distinguish it from eating and hunting in nonarticulating animals. The brain mechanisms underlying this basic survival drive, curiosity, and novelty-seeking behavior have remained unclear. ### RATIONALE In spite of having well-developed techniques to study mouse brain circuits, there are many controversial and different results in the field of motivational behavior. This has left the functions of motivational brain regions such as the zona incerta (ZI) still uncertain. Not having a transparent, nonreinforced, and easily replicable paradigm is one of the main causes of this uncertainty. Therefore, we chose a simple solution to conduct our research: giving the mouse freedom to choose what it wants—double free-access choice. By examining mice in an experimental battery of object free-access double-choice (FADC) and social interaction tests—using optogenetics, chemogenetics, calcium fiber photometry, multichannel recording electrophysiology, and multicolor mRNA in situ hybridization—we uncovered a cell type–specific cortico-subcortical brain circuit of the curiosity and novelty-seeking behavior. ### RESULTS We analyzed the transitions within action sequences in object FADC and social interaction tests. Frequency and hidden Markov model analyses showed that mice choose different action sequences in interaction with novel objects and in early periods of interaction with novel conspecifics compared with interaction with familiar objects or later periods of interaction with conspecifics, which we categorized as deep and shallow investigation, respectively. This finding helped us to define a measure of depth of investigation that indicates how much a mouse prefers deep over shallow investigation and reflects the mouse’s motivational level to investigate, regardless of total duration of investigation. Optogenetic activation of inhibitory neurons in medial ZI (ZIm), ZImGAD2 neurons, showed a dramatic increase in positive arousal level, depth of investigation, and duration of interaction with conspecifics and novel objects compared with familiar objects, crickets, and food. Optogenetic or chemogenetic deactivation of these neurons decreased depth and duration of investigation. Moreover, we found that ZImGAD2 neurons are more active during deep investigation as compared with during shallow investigation. We found that activation of prelimbic cortex (PL) axons into ZIm increases arousal level, and chemogenetic deactivation of these axons decreases the duration and depth of investigation. Calcium fiber photometry of these axons showed no difference in activity between shallow and deep investigation, suggesting a nonspecific motivation. Optogenetic activation of ZImGAD2 axons into lateral periaqueductal gray (lPAG) increases the arousal level, whereas chemogenetic deactivation of these axons decreases duration and depth of investigation. Calcium fiber photometry of these axons showed high activity during deep investigation and no significant activity during shallow investigation, suggesting a thresholding mechanism. Last, we found a new subpopulation of inhibitory neurons in ZIm expressing tachykinin 1 (TAC1) that monosynaptically receive PL inputs and project to lPAG. Optogenetic activation and deactivation of these neurons, respectively, increased and decreased depth and duration of investigation. ### CONCLUSION Our experiments revealed different action sequences based on the motivational level of novelty seeking. Moreover, we uncovered a new brain circuit underlying curiosity and novelty-seeking behavior, connecting excitatory neurons of PL to lPAG through TAC1+ inhibitory neurons of ZIm. ![Figure][3] Brain mechanism of curiosity. ( A ) How we mapped motivational level to action sequences. ( B ) Experimental battery to distinguish novelty-seeking behavior from food eating and hunting in mice with photoactivation of ZImGAD2 neurons. ( C ) Schematic of calcium activity in PL→ZIm, ZIm, and ZIm→PAG during shallow and deep investigation. ( D ) TAC1+ neurons as a subpopulation of ZImGAD2 neurons receive input from PL and project to PAG. HMM, hidden Markov model. Exploring the physical and social environment is essential for understanding the surrounding world. We do not know how novelty-seeking motivation initiates the complex sequence of actions that make up investigatory behavior. We found in mice that inhibitory neurons in the medial zona incerta (ZIm), a subthalamic brain region, are essential for the decision to investigate an object or a conspecific. These neurons receive excitatory input from the prelimbic cortex to signal the initiation of exploration. This signal is modulated in the ZIm by the level of investigatory motivation. Increased activity in the ZIm instigates deep investigative action by inhibiting the periaqueductal gray region. A subpopulation of inhibitory ZIm neurons expressing tachykinin 1 (TAC1) modulates the investigatory behavior. [1]: /lookup/doi/10.1126/science.abe9681 [2]: /lookup/doi/10.1126/science.abi7270 [3]: pending:yes


Recent Advances in Deep Learning-based Dialogue Systems

arXiv.org Artificial Intelligence

Dialogue systems are a popular Natural Language Processing (NLP) task as it is promising in real-life applications. It is also a complicated task since many NLP tasks deserving study are involved. As a result, a multitude of novel works on this task are carried out, and most of them are deep learning-based due to the outstanding performance. In this survey, we mainly focus on the deep learning-based dialogue systems. We comprehensively review state-of-the-art research outcomes in dialogue systems and analyze them from two angles: model type and system type. Specifically, from the angle of model type, we discuss the principles, characteristics, and applications of different models that are widely used in dialogue systems. This will help researchers acquaint these models and see how they are applied in state-of-the-art frameworks, which is rather helpful when designing a new dialogue system. From the angle of system type, we discuss task-oriented and open-domain dialogue systems as two streams of research, providing insight into the hot topics related. Furthermore, we comprehensively review the evaluation methods and datasets for dialogue systems to pave the way for future research. Finally, some possible research trends are identified based on the recent research outcomes. To the best of our knowledge, this survey is the most comprehensive and up-to-date one at present in the area of dialogue systems and dialogue-related tasks, extensively covering the popular frameworks, topics, and datasets. Keywords: Dialogue Systems, Chatbots, Conversational AI, Task-oriented, Open Domain, Chit-chat, Question Answering, Artificial Intelligence, Natural Language Processing, Information Retrieval, Deep Learning, Neural Networks, CNN, RNN, Hierarchical Recurrent Encoder-Decoder, Memory Networks, Attention, Transformer, Pointer Net, CopyNet, Reinforcement Learning, GANs, Knowledge Graph, Survey, Review


Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech

arXiv.org Machine Learning

Recently, denoising diffusion probabilistic models and generative score matching have shown high potential in modelling complex data distributions while stochastic calculus has provided a unified point of view on these techniques allowing for flexible inference schemes. In this paper we introduce Grad-TTS, a novel text-to-speech model with score-based decoder producing mel-spectrograms by gradually transforming noise predicted by encoder and aligned with text input by means of Monotonic Alignment Search. The framework of stochastic differential equations helps us to generalize conventional diffusion probabilistic models to the case of reconstructing data from noise with different parameters and allows to make this reconstruction flexible by explicitly controlling trade-off between sound quality and inference speed. Subjective human evaluation shows that Grad-TTS is competitive with state-of-the-art text-to-speech approaches in terms of Mean Opinion Score. We will make the code publicly available shortly.


Identity testing of reversible Markov chains

arXiv.org Machine Learning

We consider the problem of identity testing of Markov chains based on a single trajectory of observations under the distance notion introduced by Daskalakis et al. [2018a] and further analyzed by Cherapanamjeri and Bartlett [2019]. Both works made the restrictive assumption that the Markov chains under consideration are symmetric. In this work we relax the symmetry assumption to the more natural assumption of reversibility, still assuming that both the reference and the unknown Markov chains share the same stationary distribution.


Characterizing Uniform Convergence in Offline Policy Evaluation via model-based approach: Offline Learning, Task-Agnostic and Reward-Free

arXiv.org Artificial Intelligence

We study the statistical limits of uniform convergence for offline policy evaluation (OPE) problems (uniform OPE for short) with model-based methods under episodic MDP setting. Uniform OPE $\sup_\Pi|Q^\pi-\hat{Q}^\pi|<\epsilon$ (initiated by Yin et al. 2021) is a stronger measure than the point-wise (fixed policy) OPE and ensures offline policy learning when $\Pi$ contains all policies (we call it global policy class). In this paper, we establish an $\Omega(H^2 S/d_m\epsilon^2)$ lower bound (over model-based family) for the global uniform OPE, where $d_m$ is the minimal state-action distribution induced by the behavior policy. The order $S/d_m\epsilon^2$ reveals global uniform OPE task is intrinsically harder than offline policy learning due to the extra $S$ factor. Next, our main result establishes an episode complexity of $\tilde{O}(H^2/d_m\epsilon^2)$ for \emph{local} uniform convergence that applies to all \emph{near-empirically optimal} policies for the MDPs with \emph{stationary} transition. The result implies the optimal sample complexity for offline learning and separates local uniform OPE from the global case. Paramountly, the model-based method combining with our new analysis technique (singleton absorbing MDP) can be adapted to the new settings: offline task-agnostic and the offline reward-free with optimal complexity $\tilde{O}(H^2\log(K)/d_m\epsilon^2)$ ($K$ is the number of tasks) and $\tilde{O}(H^2S/d_m\epsilon^2)$ respectively, which provides a unified framework for simultaneously solving different offline RL problems.