andy
3606944190
Affected files: .obsidian/graph.json .obsidian/workspace-mobile.json .obsidian/workspace.json Food/Meal Plan.md Lab/Linux/Alpine.md Lab/Linux/KDE.md Lab/Scratch Domain.md Lab/Windows/Active Directory.md Languages/Spanish/README.md Languages/Spanish/Spanish.md Money/Accounts.md Money/Monthly/23-04.md Money/Monthly/23-05.md Money/Monthly/23-06.md Projects/Mixonomer.md Projects/NoteCrawler.md Projects/Projects.md Projects/README.md Projects/Selector.md Projects/To Do App.md Projects/img/selector-arch.png STEM/AI/Classification/Supervised/SVM.md STEM/AI/Neural Networks/CNN/FCN/Super-Resolution.md STEM/AI/Neural Networks/CNN/GAN/GAN.md STEM/AI/Neural Networks/CNN/GAN/cGAN.md STEM/AI/Neural Networks/CNN/Interpretation.md STEM/AI/Neural Networks/Deep Learning.md STEM/AI/Neural Networks/MLP/MLP.md STEM/AI/Neural Networks/Properties+Capabilities.md STEM/AI/Neural Networks/RNN/Representation Learning.md STEM/AI/Pattern Matching/Markov/Markov.md STEM/AI/Searching/Informed.md STEM/AI/Searching/README.md STEM/AI/Searching/Searching.md STEM/AI/Searching/Uninformed.md STEM/CS/Languages/Javascript.md STEM/CS/Languages/Python.md STEM/CS/Languages/dotNet.md STEM/CS/Resources.md STEM/IOT/Cyber-Physical Systems.md STEM/IOT/Networking/Networking.md STEM/IOT/Networking/README.md STEM/IOT/Software Services.md STEM/img/cyberphysical-social-data.png STEM/img/cyberphysical-system-types.png STEM/img/cyberphysical-systems.png STEM/img/depth-first-cons.png STEM/img/depth-first.png STEM/img/iot-mesh-network.png STEM/img/iot-network-radar.png STEM/img/iot-network-types 1.png STEM/img/iot-network-types.png STEM/img/markov-start-end-matrix.png STEM/img/markov-start-end-probs.png STEM/img/markov-start-end.png STEM/img/markov-state-duration.png STEM/img/markov-state.png STEM/img/markov-weather.png STEM/img/search-bidirectional.png STEM/img/search-breadth-first.png STEM/img/search-lim-goal.png STEM/img/search-lim1.png STEM/img/search-lim2.png STEM/img/search-lim3-2.png STEM/img/search-lim3.png STEM/img/search-lim4.png STEM/img/searching-graph-tree.png STEM/img/searching-graph.png Work/Freelancing.md
98 lines
3.2 KiB
Markdown
98 lines
3.2 KiB
Markdown
# 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.md#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
|
|
- 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
|
|
|
|
# Evidential Response
|
|
- Decisions are made with evidence not just declared
|
|
- Confidence value
|
|
|
|
# Contextual Information
|
|
- [Knowledge](Neural%20Networks.md#Knowledge) represented by structure and activation weight
|
|
- 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 |