Vladlen Koltun | Learning to Act |
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Reinforcement Learning
- Reinforcement learning is learning what to do - how to map situations to actions - so as to maximise a numerical reward signal.
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Why is it called Reinforcement ? —> Conditioned reflexes - Pavlov 1927
- reward and punishment
- History (Pavlov and the dog , Skinner and pigeons - behaviorism -> animal behavior can be explained in terms of conditioning)
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Natural Supervision Example
- act and observe the consequences
- learn to predict the consequences of actions
- Learn to map observarion x action -> future observation
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LEARNING TO ACT (Markov decition proces)
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Problems:
- State - “Grid world” don’t work with 3D real world situations (“perspective”)
- Reward - “What is the reward for life?”,
- Discounting - Its extremely aggressive. In reality you can chose something with smaller discount
- Markov Assumption - No history
Emilio Frazzoli | Title |
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- Mobility as a service is the way to go
- Easier to scale / unroll
- Cost efficient for consumer and provider
- Controlled Environoment
- Levels of autonomy is misleading
- OEMs try to increasy autonomy step by step
- MaaS (AVs as a service (MaaS)) approach allows to unroll by scaling up the scope
- Utilize prior Knowledge where applicable
- Rules of the Road don't need to be learned
- Use formal logic for decision making
- Utilize graph theory for planning
- Combine traditional methods with learning
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Hao Li | Title |
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- Allow realistic digital communication
- with everyday hardware
- Requires to capture fine grane details
- to transport non-verbal aspects of communication
- Use deep learning to infer those details
- instead of expensive sensoring equipment
Trevor Darrell | Title |
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- Domain adaptation
- Use adversial network for cross-domain adaptation
- Force the network to learn a representation which is indistinguisable between two domains
- (future project?)
- Natural Supervision
- Curious learning
- Use loss as reward in RL
- Visual Explanations
- Use attention to make model interpretable
Sanja Fidler | Learning Embettings of Images |
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- Use word2vec (or similar) to learn embetting for words
- RNNs for sequential models
- Tasks:
- Image Captioning
- Classification using description (zero short)
Vittorio Ferrari | Learning Embettings of Images |
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- Weakly supervised learning: Learn from a lower degree of supervision
- Semi-supervised learning: combine labeled unlabeled data (useful project?)
- Utilize that different combinations of labels appear
- Face recoginition example
- Training: EM
- Weakly Supervised Deep Detection Networks
- One point per class (Use object model)
Ross Girshick | From Visual Perception to Visual Reasoning |
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- Respect Variation:
- Translational Variation
- Rotational Variation
- Scale Variation
- Deformation Variation
- Intra-class Variation
- Problem might be Invariances or Equivariance w.r.t. variation
- Look at old papers
- You might find good ideas
- Mask-RCNN
- Modularised Approach
- Feature Pyramid Networks for Object Detection
- Kluger Hans & Visual Question Answering
- is VQA only nearest neighboor?
- How do we know our models are as sophisticated as we think
- Diagnosing Errors in Object Detectors
- Useful Error diagnosation tool
- CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
- Dataset for analysing VQA
Abhinav Gupta | Self-supervised Learning of Visual Representations |
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- Use auxiliary tasks as pretraining
- For classification the following tasks are useful:
- predict color channel (given RGB channel)
- predict relative positions of text
- jigsaw puzzl predict position of patches
- predict content of hole
- Kluger Hans Issue (shortcuts)
- low level image structures (edges)
- camera artifacts
- Solution: Jitter input
- Augment position
- drop color channel
- Natural supervised training not yet competative with ImageNet pretraining
- (ImageNet labels as natural supervision for some tasks?)
- Validate weakly supervision
- only finetune few layers
- use view data
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Jan Peters | Learning Visuomotor Skills in Robotics |
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- Machine Learning is not Robot Learning
- Robot Learning has its own unique challanges
- Use prior knowledge (e.g. about physics) where avaible
- we have very good models, e.g. for motion planning
- not everythink needs to be learned from scratch
Raquel Urtasun | Towards Affordable Self Driving Cars |
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- combine computer vision knowledge with
- e.g. solve stereo by computing matchings
- use flow for detection / segmentation
- geometric aware cnn
- prediction (of road user behaviour) is an important task
- Visual Odometry (localization / mapping) needs more research
- Cityscapes offers poor meta-data
- 3D detection is required
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Andrew Fitzgibbon | Optimiztion |
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- Finding solution fast (speed) is the key goal
- To find global optima, search for local optima
- on some problem spaces (quasi-convex) reaching global optima is garantied
- otherwise use random research
- optimization is an ongoing field of research
- better optimization algorithms can improve machine learning
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