CS 510: Lecture 8. Deep Learning, Fairness, and Bias


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1 CS 510: Lecture 8 Deep Learning, Fairness, and Bias
2 Next Week All Presentations, all the time Upload your presentation before class if using slides Sign up for a timeslot google doc, if you haven t already done so
3 Artificial Neural Networks: History Belief that it was necessary to model underlying brain architecture for AI In contrast to encoded symbolic knowledge (best represented by expert systems) Hebb  learning is altering strength of synaptic connections
4 Neural Networks Attempt to build a computation system based on the parallel architecture of brains Characteristics: Many simple processing elements Many connections Simple messages Adaptive interaction
5 Brains neurons of > 20 types, synapses, 1ms 10ms cycle time Signals are noisy spike trains of electrical potential Axonal arborization Synapse Axon from another cell Dendrite Axon Nucleus Synapses Cell body or Soma Chapter 20, Section 5 3
6 Benefits of NN User friendly (well, reasonably) Nonlinear Noise tolerant Many applications Credit fraud/assignment Robotic Control
7 Neurons Inputs (either from outside or other neurons ) Weighted connections that correspond to synaptic efficiency Threshold values to weight the inputs Passed through activation function to determine output
8 Example Unit Binary input/output Rule 1 if w0*i0 + w1*i1 +wb > 0 0 if w0*i0 + w1*i1 +wb <= 0
9 Activation functions g(in i ) g(in i ) (a) in i (b) in i (a) is a step function or threshold function (b) is a sigmoid function 1/(1 + e x ) Changing the bias weight W 0,i moves the threshold location Note similarity to logistic regression... Chapter 20, Section 5 5
10 0.06 W12.5 W2 f(x) W3 1.4
11 f(x) x = =
12 How to Adapt? Perceptron Learning Rule change the weight by an amount proportional to the difference between the desired output and the actual output. As an equation: ΔWi = η * (D  Y)Ii, where D is desired output and Y is actual output Stop when converges
13 Limits of Perceptrons Minsky and Papert 1969 Fails on linearly inseparable instances XOR linearly separable  pattern space can be separated by single hyperplane
14 Perceptrons vs Decision Trees
15 Multilayer Perceptrons (MLP)
16 Back Propagation Start with a set of known examples (supervised approach) Assign random initial weights Run examples through and calculate the meansquared error Propagate the error by making small changes to the weights at each level Use chain rule to calculate the gradient efficiently Lather, rinse, repeat
17 Gradient Descent Algorithm Have some function Want Outline: Start with some Keep changing to reduce until we hopefully end up at a minimum
18 The gradient of J ( J) at a point can be thought of as a vector indicating which way is uphill J(θ 0,θ 1 ) If J is an error function we want to move downhill  opposite to the gradient
19 Gradient descent algorithm Have function J Want to produce vectors s.t. J(θ1)>J(θ2)>... start w/ θ0 θi+1 = θi  ɑi J(θi) ɑ(alpha) is the learning rate
20 Stochastic Gradient Descent Update J every time you look at a training example
21 Some nonlinear activation functions
22 Most common activation function
23 A dataset Fields class etc
24 Training the neural network Fields class etc
25 Training data Fields class etc Initialise with random weights
26 Training data Fields class etc Present a training pattern
27 Training data Fields class etc 1.4 Feed it through to get output
28 Training data Fields class etc 1.4 Compare with target output error 0.8
29 Training data Fields class etc 1.4 Adjust weights based on error error 0.8
30 Training data Fields class etc Present a training pattern
31 Training data Fields class etc 6.4 Feed it through to get output
32 Training data Fields class etc 6.4 Compare with target output error 0.1
33 Training data Fields class etc 6.4 Adjust weights based on error error 0.1
34 Training data Fields class etc 6.4 And so on error 0.1 Repeat this thousands, maybe millions of times each time taking a random training instance, and making slight weight adjustments Algorithms for weight adjustment are designed to make changes that will reduce the error
35 The decision boundary perspective Initial random weights
36 The decision boundary perspective Present a training instance / adjust the weights
37 The decision boundary perspective Present a training instance / adjust the weights
38 The decision boundary perspective Present a training instance / adjust the weights
39 The decision boundary perspective Present a training instance / adjust the weights
40 The decision boundary perspective Eventually.
41 The point I am trying to make weightlearning algorithms for NNs are dumb they work by making thousands and thousands of tiny adjustments, each making the network do better at the most recent pattern, but perhaps a little worse on many others but, by dumb luck, eventually this tends to be good enough to learn effective classifiers for many real applications
42 Some other points Detail of a standard NN weight learning algorithm later If f(x) is nonlinear, a network with 1 hidden layer can, in theory, learn perfectly any classification problem. A set of weights exists that can produce the targets from the inputs. The problem is finding them.
43 Some other by the way points If f(x) is linear, the NN can only draw straight decision boundaries (even if there are many layers of units)
44 Some other by the way points NNs use nonlinear f(x) so they can draw complex boundaries, but keep the data unchanged
45 Some other by the way points NNs use nonlinear f(x) so they can draw complex boundaries, but keep the data unchanged SVMs only draw straight lines, but they transform the data first in a way that makes that OK
46 Neural network vocabulary Neuron = logistic regression or similar function Input layer = input training/test vector Bias unit = intercept term/always on feature Activation = response Activation function is a logistic (or similar sigmoid nonlinearity) Backpropagation = running stochastic gradient descent across a multilayer network Weight decay  regularization or Bayesian prior
47 Deep Learning Most current machine learning works well because of humandesigned representations and input features Machine learning becomes just optimizing weights to best make a final prediction Representation learning attempts to automatically learn good features or representations Deep learning algorithms attempt to learn multiple levels of representation of increasing complexity/abstraction
48 Deep Architecture
49 Deep Learning Overview Train networks with many layers (vs. shallow nets with just a couple of layers) Multiple layers work to build an improved feature space First layer learns 1st order features (e.g. edges ) 2nd layer learns higher order features (combinations of first layer features, combinations of edges, etc.) In current models layers often learn in an unsupervised mode and discover general features of the input space serving multiple tasks related to the unsupervised instances (image recognition, etc.) Then final layer features are fed into supervised layer(s) And entire network is often subsequently tuned using supervised training of the entire net, using the initial weightings learned in the unsupervised phase Could also do fully supervised versions, etc. (early BP attempts)
50 Why Deep Learning?
51 Learning Representations Handcrafting features is timeconsuming incomplete domain/... The features are often both overspecified and The work has to be done again for each task/ We must move beyond handcrafted features and simple ML Humans develop representations for learning and reasoning Our computers should do the same
52 The Curse of Dimensionality
53 Unsupervised Feature and Weight Learning Today, most practical, good NLP& ML methods require labeled training data (i.e., supervised learning) But almost all data is unlabeled Most information must be acquired unsupervised Fortunately, a good model of observed data can really help you learn classification decisions
54 Learning Multiple Levels of Representation
55 Successive Layers Learn Deeper Representations object models object parts (combination of edges) edges pixels
56 Impressive Results Especially on Large Datasets Object Recognition  better than anything out there Speech Recognition (google voice search) Many other perceptual tasks in vision and NLP
57 Why now? Bigger Data  deep learning works best Better Hardware  multicore CPUs and GPUs Better Algorithms  autoencoders, deep belief networks, etc Let us train multiple inner layers well
58 Breakthrough: Unsupervised Pretraining
59 Difficulties with Supervised Networks Early layers of MLP do not get trained well Diffusion of Gradient error attenuates as it propagates to earlier layers Leads to very slow training Exacerbated since top couple layers can usually learn any task "pretty well" and thus the error to earlier layers drops quickly as the top layers "mostly" solve the task lower layers never get the opportunity to use their capacity to improve results, they just do a random feature map Need a way for early layers to do effective work Often not enough labeled data available while lots of unlabeled data Can we use unsupervised/semisupervised approaches to take advantage of the unlabeled data Deep networks tend to have more local minima problems than shallow networks during supervised training
60 Semisupervised Learning
61 Semisupervised Learning
62 Training Deep Networks Build a feature space Note that this is what we do with SVM kernels, or trained hidden layers in BP, etc., but now we will build the feature space using deep architectures Unsupervised training between layers can decompose the problem into distributed subproblems (with higher levels of abstraction) to be further decomposed at subsequent layers
63 Greedy Layerwise Training Train first layer using your data without the labels (unsupervised) Since there are no targets at this level, labels don't help. Could also use the more abundant unlabeled data which is not part of the training set (i.e. selftaught learning). Freeze the first layer parameters and start training the second layer using the output of the first layer as the unsupervised input to the second layer Repeat this for as many layers as desired This builds our set of robust features Use the outputs of the final layer as inputs to a supervised layer/model and train the last supervised layer(s) (leave early weights frozen) Unfreeze all weights and fine tune the full network by training with a supervised approach, given the preprocessed weight settings
64 Greedy Layerwise Training Greedy layerwise training avoids many of the problems of trying to train a deep net in a supervised fashion Each layer gets full learning focus in its turn since it is the only current "top" layer Can take advantage of the unlabeled data When you finally tune the entire network with supervised training the network weights have already been adjusted so that you are in a good error basin and just need fine tuning. This helps with problems of Ineffective early layer learning Deep network local minima We will discuss the two most common approaches Stacked AutoEncoders Deep Belief Networks
65 The new way to train multilayer NNs Train this layer first
66 The new way to train multilayer NNs Train this layer first then this layer
67 The new way to train multilayer NNs Train this layer first then this layer then this layer
68 The new way to train multilayer NNs Train this layer first then this layer then this layer then this layer
69 The new way to train multilayer NNs Train this layer first then this layer then this layer then this layer finally this layer
70 The new way to train multilayer NNs EACH of the (nonoutput) layers is trained to be an autoencoder Basically, it is forced to learn good features that describe what comes from the previous layer
71 an autoencoder is trained, with an absolutely standard weightadjustment algorithm to reproduce the input
72 an autoencoder is trained, with an absolutely standard weightadjustment algorithm to reproduce the input By making this happen with (many) fewer units than the inputs, this forces the hidden layer units to become good feature detectors
73 One Autoencoder 73
74 Stacked Autoencoders Stack sparse autoencoders on top of each other, drop decode layer each time 74
75 Stacked autoencoders Do supervised training on last layer Then do supervised training on whole network to fine tune the weights 75
76 Manifold Learning Hypothesis 76
77 Caveats Prevent the layers from just learning the identity (learn Features instead) Undercomplete  middle layer smaller than input Sparsity  penalize hidden unit activations Use regularization to keep most nodes at or near 0 Denoising  Stochastically corrupt training instance, but train encoder to decode uncorrupted instance Contractive  force encoder to have small derivatives (stay on manifold) 77
78 Fairness and Learning Going to show video of Aylin Link on course website to her talk (may be easier for online students if there is feedback). 78
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