ece, Deep Learning – Assignment 1

Submit by Sept. , pm

tldr: Perform linear regression of a noisy sinewave using a set of gaussian basis

functions with learned location and scale parameters. Model parameters are

learned with stochastic gradient descent. Use of automatic differentiation is

required. Hint: note your limits!

Problem Statement Consider a set of scalars {x1, x2, . . . , xN } drawn from U(0, 1)

and a corresponding set {y1, y2, . . . , yN } where:

yi = sin (2πxi) + ϵi (1)

and ϵi

is drawn from N (0, σnoise). Given the following functional form:

yˆi =

∑

M

j=1

wjϕj (xi

| µj

, σj ) + b (2)

with:

ϕ(x | µ, σ) = exp

−(x − µ)

2

σ

2

(3)

find estimates ˆb, {µˆj}, {σˆj}, and {wˆj} that minimize the loss function:

J(y, yˆ) = 1

2

(y − yˆ)

2

(4)

for all (xi

, yi) pairs. Estimates for the parameters must be found using stochastic

gradient descent. A framework that supports automatic differentiation must be

used. Set N = 50, σnoise = 0.1. Select M as appropriate. Produce two plots. First,

show the data-points, a noiseless sinewave, and the manifold produced by the

regression model. Second, show each of the M basis functions. Plots must be of

suitable visual quality.

−4 −2 0 2 4

x

−1.5

−1.0

−0.5

0.0

0.5

1.0

1.5

y

Fit 1

−4 −2 0 2 4

x

0.0

0.2

0.4

0.6

0.8

1.0

y

Bases for Fit 1

−4 −2 0 2 4

x

−1.5

−1.0

−0.5

0.0

0.5

1.0

1.5

y

Fit 2

−4 −2 0 2 4

x

0.0

0.2

0.4

0.6

0.8

1.0

y

Bases for Fit 2

Figure 1: Example plots for models with equally spaced sigmoid and gaussian basis functions.

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# Deep Learning – Assignment 1

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ece, Deep Learning – Assignment 1

Submit by Sept. , pm

tldr: Perform linear regression of a noisy sinewave using a set of gaussian basis

functions with learned location and scale parameters. Model parameters are

learned with stochastic gradient descent. Use of automatic differentiation is

required. Hint: note your limits!

Problem Statement Consider a set of scalars {x1, x2, . . . , xN } drawn from U(0, 1)

and a corresponding set {y1, y2, . . . , yN } where:

yi = sin (2πxi) + ϵi (1)