stem/AI/Neural Networks/Properties+Capabilities.md

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# Linearity
- Neurons can be linear or non-linear
- Network of non-linear neurons is non-linear
- Non-linearity is distributed
- Helpful if target signals are generated non-linearly
# Input-Output Mapping
- Map input signal to desired response
- Supervised learning
- Similar to non-parametric statistical inference
- Non-parametric as in no prior assumptions
- No probabilistic model
# Adaptivity
- Synaptic weights
- Can be easily retrained
- Stationary environment
- Essential statistics can be learned
- Model can then be frozen
- Non-stationary environments
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- Can change weights in real-time
- In general, more adaptive = more robust
- Not always though
- Short time-constant system may be thrown by short-time spurious disturbances
- Stability-plasticity dilemma
- Not equipped to track statistical variations
- Adaptive system
- Linear adaptive filter
- Linear combiner
- Single neuron operating in linear mode
- Mature applications
- Nonlinear adaptive filters
- Less mature
- Environments typically considered pseudo-stationary
- Speech stationary over short windows
- Retrain network at regular intervals to account for fluctuations
- E.g. stock market
- Train network on short time window
- Add new data and pop old
- Slide window
- Retrain network
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# Evidential Response
- Decisions are made with evidence not just declared
- Confidence value
# Contextual Information
- [[Neural Networks#Knowledge|Knowledge]] represented by structure and activation weight
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- Any neuron can be affected by global activity
- Contextual information handled naturally
# Fault Tolerance
- Hardware implementations
- Performance degrades gracefully with adverse conditions
- If some of it breaks, it won't cause the whole thing to break
- Like a real brain
# VLSI Implementability
- Very large-scale integration
- Chips with millions of transistors (MOS)
- E.g. microprocessor, memory chips
- Massively parallel nature
- Well suited for VLSI
# Uniformity in Analysis
- Are domain agnostic in application
- Analysis methods are the same
- Can share theories, learning algorithms
# Neurobiological Analogy
- Design analogous to brain
- Already a demonstrable fault-tolerant, powerful, fast, parallel processor
- To slight changes
- Rotation of target in images
- Doppler shift in radar
- Network needs to be invariant to these transformations
# Invariance
1. Invariance by Structure
- Synaptic connections created so that transformed input produces same output
- Set same weight for neurons of some geometric relationship to image
- Same distance from centre e.g.
- Number of connections becomes prohibitively large
2. Invariance by Training
- Train on different views/transformations
- Take advantage of inherent pattern classification abilities
- Training for invariance for one object is not necessarily going to train other classes for invariance
- Extra load on network to do more training
- Exacerbated with high dimensionality
3. Invariant Feature Space
- Extract invariant features
- Use network as classifier
- Relieves burden on network to achieve invariance
- Complicated decision boundaries
- Number of features applied to network reduced
- Invariance ensured
- Required prior knowledge