'Gradient ascent' diaporamas de présentation

Biologically Inspired Intelligent Systems

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IV. Neural Network Learning

IV. Neural Network Learning. A. Neural Network Learning. Supervised Learning. Produce desired outputs for training inputs Generalize reasonably & appropriately to other inputs Good example: pattern recognition Feedforward multilayer networks. input layer. output layer. hidden layers.

850 views • 71 slides

IV. Neural Network Learning

IV. Neural Network Learning. A. Neural Network Learning. Supervised Learning. Produce desired outputs for training inputs Generalize reasonably & appropriately to other inputs Good example: pattern recognition Feedforward multilayer networks. input layer. output layer. hidden layers.

733 views • 71 slides

IV. Neural Network Learning

IV. Neural Network Learning. A. Neural Network Learning. Supervised Learning. Produce desired outputs for training inputs Generalize reasonably & appropriately to other inputs Good example: pattern recognition Feedforward multilayer networks. input layer. output layer. hidden layers.

719 views • 71 slides

Logistic Regression

Logistic Regression. Rong Jin. Logistic Regression Model. In Gaussian generative model: Generalize the ratio to a linear model Parameters: w and c. Logistic Regression Model. In Gaussian generative model: Generalize the ratio to a linear model Parameters: w and c.

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Statistical Classification

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Fooling ConvNets and Adversarial Examples

Fooling ConvNets and Adversarial Examples. Presented by: Aroma Mahendru. ECE 6504 Deep Learning for Perception. Presentation Overview. I ntriguing properties of neural networks Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus

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RL for Large State Spaces: Policy Gradient

RL for Large State Spaces: Policy Gradient. Alan Fern. RL via Policy Gradient Search. So far all of our RL techniques have tried to learn an exact or approximate value function or Q-function Learn optimal value of being in a state, or taking an action from state.

390 views • 31 slides

Regression and the Bias-Variance Decomposition

Regression and the Bias-Variance Decomposition. William Cohen 10-601 April 2008. Readings: Bishop 3.1,3.2. Regression. Technically: learning a function f( x )=y where y is real-valued , rather than discrete .

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RL for Large State Spaces: Policy Gradient

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RL for Large State Spaces: Policy Gradient

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Parameter learning in Markov nets

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Chap 6 Bayesian Learning (3)

Chap 6 Bayesian Learning (3). ?????. Bayesian Belief Networks(6). Inference Bayes net is used to infer the (probabilities of ) values of one or more network variables, given observed value or others. In general case, the problem is NP hard.

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Hill-Climbing Search and Dynamic Programming:

Hill-Climbing Search and Dynamic Programming:. Heuristics. Motivations. If a problem has polynomial-time solutions, traditional programming techniques are used to implement the solutions. AI problems, however, are exponential in nature, e.g., chess, traveling salesman.

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Independent Factor Analysis

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Logistic Regression

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Online convex optimization Gradient descent without a gradient

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Logistic Regression

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Logistic Regression

319 views • 11 slides

Policy Improvement for POMDPs using gradient ascent

Policy Improvement for POMDPs using gradient ascent. Gaurav Marwah. Introduction. POMDP stands for partially observable markov decision processes. Framework for planning under uncertainty in actions and observations. Optimal planning requires storing entire event history.

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