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Interpretability	of	Deep	Neural	Nets
Agenda
• Case	for	Interpretability
• Just	how	black	is	the	Deep	Neural	Nets(DNNs)	box	?
• Recent	research	papers/solutions	to	Interpretability	of	DNNs
• Demo	/	Code	walk	through
ML	everywhere
Big	Data +	Vast	computing	resources +	Key	algo breakthroughs
“People	worry	that	computers	will	get	too	
smart	and	take	over	the	world,	but	the	real	
problem	is	that	they’re	too	stupid	and	
they’ve	already	taken	over	the	world.”		
- Pedro	Domingos “The	Master	Algorithm”	
Intelligible	Models	for	Heathcare:	Predicting	
Pneumonia	Risk	and	Hospital	30	day	Readmission
(Rich	Caruana et	al	2015	,	Microsoft	Research)
- Goal	:	Predict	POD	for	InPatient or	OutPatient care
- Neural	Network	predicted	Asthma	as	OutPatient
- Historically	Asthama patients	were	sent	start	to	IC
- Model	was	“downgraded”	to	logistic	regression
Interpretability	in	machine	learning
⋮
𝑥#
𝑥$
𝑥%
𝑥&
Evaluation	
Metrics
𝒚∗
𝒚) Results
Interpretation
What	is	Interpretability	?
“Ability	to	explain or	to	present	in	understandable	terms	to	a	human”
Explanation	– “Currency in	which	we	exchange	beliefs”
Towards	a	rigorous	science	of	Interpretability	in	Machine	Learning,	(Been	Kim	et.al.)
DARPA	– XAI	program	concept
Explainable	Models
Credit:	DARPA	XAI	project
Native	interpretability	?
Linear	and	
Monotonic
Linear	and	Non	
Monotonic
Non	Linear	and	
Non	Monotonic
- Linear	regressions
- Decision	trees
Multi	Adaptive	
Regression	Splines
- Boosted	models
- Non-linear	SVMs
- DNNs
When	do	we	need	interpretability	?
Stems	from	incompleteness	in	problem	formalization	
(under-specification)
No	amount	of	data	may	“fix	it”
Visual	Q/A
Image
Translation
Playing	Go
Research
Debug
Mismatched	
Objective
Safety
Scientific	
Inquiry
Human	
Training
Fairness
Voice
NLP
Multi	modal	
learning
Model	
Lifecycle	Mgt
Weak
At	Par
Better
Domain	
Adaption
Scope	of	Interpretability
Global
The	complete	conditional	distribution
Relationships	between	input	and	the	dependent	variables
Relationship	between	the	ML	algo and	results
Local
Local	region	of	the	conditional	distribution
“Why	did	the	model	predict	this	particularly	?”
“What	if	this	particular	input	was	absent	?”
Deep	Neural	Nets
Input	space	for	CNNs	trained	on	Imagenet
Per	Image:
Num of	Pixels	=	256	X	256	X	3
Values	each	pixel	can	take	=	256
Domain	of	all	image	=	256($./0$./0%)
Total	Input	:	1.2M	across	1000	classes
:	3×256$×1200 ~	236M/class
:	Very	very	tiny	fraction	of	Domain
Parameter	to	Training	set		(6
&⁄ )	:	4	to	110
But… SGD	always	converges	to	a	good	solution
Samoyed White-wolf
Recent	research	on	understanding	DNNs
Why	does	deep	and	cheap	learning	work	so	we	?	
(Henry	W.	Lin (Harvard), Max	Tegmark (MIT), David	Rolnick (MIT))
• Physics	centric	theory	
Understanding	deep	learning	requires	rethinking	generalization
(Chiyuan Zhang,	Samy Bengio,	Maritz	Hardt,	Benjamin	Recht,	Oriol	Vinyals (Google	Brain	and	Deepmind))
• Revisits	learning	theory,	esp generalization	bounds	in	empirical	risk	minimization
Opening	the	Black	Box	of	Deep	Neural	Networks	via	Information
(Ravid Shwartz-Ziv,	Naftali	Tishby)
• Information	bottleneck	theory
Randomization	Test
Partially	corrupted	labels	:	independent	relabeling	with	probability	p
Random	labels		:	all	labels	replaced	with	a	random	ones
Shuffled	pixels	:	random	permutation	of	the	pixels	applied	to	all	images
Random	pixels	:	a	different	random	permutation	to	each	image
Gaussian :	A	Gaussian	dist.	(same	mean/var of	original image)	of	pixels
All	hyper-parameters	were	
kept	same
Training	error	was	0	in	all	
models
Role	of	Regularization
• Typical	regularizations:	Dropouts,	batchnormalization,	
data	augmentation,	early	stopping,	weight	decay	(l2)
• Explicit	regularizers may	improve	generalization	
performance
• Implicit	regularizers like	model	architecture	and	SGD	are	a	
better	controller	of	generalization	error
Takeaways
• Effective	capacity	of	a	neural	networks	is	sufficient	for	memorizing	
the	entire	data	set
• Optimization	!=	Generalization
• DNNs	are	fragile	to	overfitting,	it	will	shatter	any	input	space
• Regularizers may	improve	performance	but	are	not	necessary	or	itself	
sufficient	for	controlling	generalization	error
Interpretability	of	DNNs.					Hmmm..
Notwithstanding	a	lack	of	unifying	theory	on	Deep	Networks,	they	
work	great
Having	85-90%	accuracy	in	classification	problems	is	almost	easy	
(of	course	with	state	of	the	art	models,	and	careful	hyper-parameter	tuning)	
How	can	we	build	trust	?
Disentanglement	and	separation	of	features
Learn	facial	expressions:	
- Same	individual	are	close	in	pixel	space
- To	extract	expression	must	disentangle	and	
separate	expression	from	face	
A	four	layer	NN	can	
separate	the	spirals
Credit	:	https://colah.github.io/posts/2014-03-NN-Manifolds-Topology/
Visualizing	representations t-SNE	
• Embeds	a	high	dim	
probability	distribution	
to	a	2-D	plane
• Uses	SGD	to	minimize	
KLD
Embedding	of	2-d	representation	of	the	
final	conv	layer	in	AlexNet trained	on	
Imagenet images
Visually	inspect	clusters	for	
feature	coherence
Can	be	a	tool	for	global	
visualization	of	feature	
separation
Is	not	trivial	to	get	good	
results
Credit	:	Karpathy,	t-SNE	vizualization of	CNN	codes
Proxy	models	– Knowledge	Distillation
DNNs	learns	probability	
dist between	target	(Dark	
knowledge,	Hinton)
• First:	Train	a	large	
network	on	training
• Train	against	point	
mass	probability	
distribution
• Next:	Train	a	shallow	
model	using	richer	
probability	distribution	
between	targets
Credit	:	H2O.ai
Local	interpretations
Saliency	/	Attribution	maps
Visualize	the	features	in	the	input	space	that	mattered	for	the	classification
Sensitivity	analysis	on	model
What	would	happen	to	output	𝒚)	?
If	we	perturb	the	input	𝒙	 → 𝒙 + 	𝝐,	𝝐 can	be	feature,	data,	specific	inputs
Saliency	maps	– Grad-CAM	
- Backprops target	class	activations	from	final	conv	layer
- Does	not	need	any	retraining	or	architecture	change
- Quite	fast;	single	operation	in	most	frameworks
- Uses	guided	backprop to	only	propagate	positive	
activations
- Negative	gradients	get	zero-ed out
• Misses	negatively	correlated	inputs
Credit	:	Grad-CAM:	Visual	Explanations	from	Deep	Networks	via	Gradient-based	Localization
Attribution	maps	
DeepLift (Deep	Learning	Important	FeaTures)	
- Explain	“difference	of	reference	value”	of	output	in	terms	of	“difference	from	reference	value”	of	input:	
• ∆	𝑡	 → ∆𝑥#, ∆𝑥$, . . , ∆𝑥&	
- Assign	contributions	𝐶∆0C,∆D:
• ∑ 𝐶∆0C,∆D
&
G 	= ∆	𝑡
- Can	account	for	–ve contributions
- Very	new,	hasn’t	been	depicted	in	non	MNIST	dataset.	Also	reference	value	is	empirical
Integrated	Gradients
- Pick	some	reference	values,	eg image	with	0	pixel	values
- Scale	input	values	linearly	to	actual	value,	do
gradient	*	∆𝑖𝑛𝑝𝑢𝑡 at	each	step
↪	∆𝑖𝑛𝑝𝑢𝑡	 ∗	∑ 𝑔𝑟𝑎𝑑0C
	
- Very	fine	grained	– at	a	pixel	level
Learning	Important	Features	Through	Propagating	Activation	Differences
Axiomatic	Attribution	for	Deep	Networks
Sensitivity	analysis
Change	the	input	by	𝝐 and	then	observe	
prediction	probability
- Occlusion	based	
- Idea	is	probability	score	will	drop	as	
important	areas	are	occluded
- Superpixel based
- Same	idea	as	above	but	better	
coherence
Credit	:	http://blog.qure.ai/notes/visualizing_deep_learning
LIME	
Local	Interpretable	Model	Agnostic	Explanations
Key	insights
Local	vrs Global	interpretation
Globally	faithful	interpretation	might	be	impossible
To	explain	individual	decision	need	to	know	the	small	local	region
Global	trust
If	we	trust	individual	reasonings
Repeat	with	a	good	coverage	over	the	input	space
Local	explanation	
of	the	+	data	
points
Locally	fitted	sparse	
linear	model
Example	- CNN
• Segments	the	image	using	
opencv
• Build	a	linear	model	based	
on	prediction	scores	against	
segments
Example	– NLP	(Topic	modelling)
Example	– Tabular	Data	(RandomForest)
Conclusion
Interpretability	is	not	a	“good	to	have”	feature
This	is	just	the	beginning	and	future	is	bright
• “Right	to	explanation”	– EU	General	Data	Protection	Regulation
• SR	11-7: Guidance	on	Model	Risk	Management
• Explainable	Artificial	Intelligence	– Darpa
• https://www.darpa.mil/program/explainable-artificial-intelligence
Backup	Slides
Learning	theory
Given	Input	{𝑥G, 𝑥$, . . , 𝑥&}	∈ 	𝒳 eg images	;		Output	{𝑦#,	 𝑦$	, … . , 𝑦&}	∈ 	Υ eg labels	;	
Hypothesis	space	Η set	of	functions
Goal	of	supervised	learning	is	to	learn	a	function	->		𝑓[ ∶	 𝑦6]^_ =	 𝑓[ 𝑥&^`
Define	a	loss	function	ℓ 𝑓[ 𝑥 , 𝑦
Define	emprical loss	:	ℓ[ =
#
b
Σ 𝑓, 𝑧 	𝑤ℎ𝑒𝑟𝑒	𝑧 = 𝑥G, 𝑦G
We	want	 lim
&klm
𝑙[ 𝑓[ 	− 𝑙 𝑓[ = 0		;	Ie	training	set	error	and	real	error	converge	to	0	as	n	tends	to	infinity
No	of	trainable	parameters	indicative	of	model	complexity	
Regularization	is	used	to	penalize	complexity	and	reduce	
variance
Generalization	Error	=	|training	error	– validation	error|
Model	Selection		:	Bias	- Variance	Tradeoff
Deep	Neural	
Nets
Under-specification	Bias
Scientific	Understanding:
• We	have	no	complete	way	to	state	
what	knowledge	is
• Best	we	can	do	is	ask	for	
explanation
Safety:
• Complex	tasks	is	almost	never	
end-to-end	testable
• Query	model	for	explanation
Ethics:
• Encoding	all	protections	a	
priori,	not	possible	
• Guard	against	discrimination
Mismatched	objectives:
• Optimizing	an	incomplete	
objective
All	these	may	
address	
depressions.	
But	which	side	
effect	are	you	
willing	to	accept	?
Debugging:
• We	may	not	know	the	internals
• Domain	mismatches
• Mislabeled	Training	set
Model	lifecycle	management:
• Compare		different	models
• Training	set	evolution
Your	own	:
• …

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