Most current methods used to train neural networks, whether with gradient descent or evolution strategies, aim to solve for the value of each individual weight parameter of a given neural network. The thickness represents the magnitude of its weight. The weight of each connection is a function of its location within the network. Here are examples (taken from ) of fully connected networks ( phenotype) where the weights are not individually trained, but generated by a smaller neural network ( genotype). HyperNEAT is an indirect encoding method that can generate a variety of weight patterns with structured regularities. ( Wikipedia) of neural networks and possibly offer a fresh perspective for approaching out-of-domain generalization problems. We believe that the foundations laid by the work on indirect encoding can help us gain a better understanding of the inductive biases The inductive bias of a learning algorithm is the set of assumptions that the learner uses to predict outputs given inputs that it has not yet encountered.
#Monkik plastic the noun project free#
Analogous to the pruning of lottery ticket solutions, indirect encoding methods allow for both the expressiveness of large neural architectures while minimizing the number of free model parameters. There is actually a whole area of related research within the neurevolution field on evolving this genetic bottleneck, which is called an indirect encoding. These innate abilities make it easier for animals to generalize and quickly adapt to different environments. Innate processes and behaviors are encoded by evolution into the genome, and as such many of the neuronal circuits in animal brains are pre-wired, and ready to operate from birth.
) point out that animals are born with highly structured brain connectivity that are far too complex to be specified explicitly in the genome and must be compressed through a “genomic bottleneck”-information encoded into the genome that specify a set of rules for wiring up a brain. Recent neuroscience critiques of deep learning (e.g. These solutions can then be pruned to form sub-networks with useful inductive biases that have desirable generalization properties. ) suggests, it is because larger networks allow the optimization algorithm to find good solutions, or lottery tickets, within a small fraction of the allowable solution space. While larger neural networks generalize better than smaller networks, the reason is not that they have more weight parameters, but as recent work (e.g. There is much discussion in the deep learning community about the generalization properties of large neural networks. Since our agent attends to only task-critical visual hints, they are able to generalize to environments where task irrelevant elements are modified while conventional methods fail. We argue that self-attention has similar properties as indirect encoding, in the sense that large implicit weight matrices are generated from a small number of key-query parameters, thus enabling our agent to solve challenging vision based tasks with at least 1000x fewer parameters than existing methods.
We find neuroevolution ideal for training self-attention architectures for vision-based reinforcement learning tasks, allowing us to incorporate modules that can include discrete, non-differentiable operations which are useful for our agent. By constraining access to only a small fraction of the visual input, we show that their policies are directly interpretable in pixel space. Motivated by selective attention, we study the properties of artificial agents that perceive the world through the lens of a self-attention bottleneck. It is a consequence of the selective attention in perception that lets us remain focused on important parts of our world without distraction from irrelevant details. Inattentional blindness is the psychological phenomenon that causes one to miss things in plain sight.
0 kommentar(er)
