The sequence of mating operations that can be carried out to assemble a group of parts is constrained by the geometric and mechanical properties of the parts, their assembled configuration, and the stability of the resulting subassemblies. An approach to representation and reasoning about these sequences is described here and leads to several alternative explicit and implicit plan representations. The Pleiades system will provide an interactive software environment for designers to evaluate alternative systems and product designs through their impact on the feasibility and complexity of the resulting assembly sequences.
I was reading over Block's sequence generators, which seem to use RNN's with attention mechanisms to generate sequences. I'm not completely sure (I couldn't find any example of them being used), but they seem to be designed for training in a way where they will generate sequences, then calculate loss based on the generated sequence, rather than just predict the next character like Char-RNN. For Char-RNN's they seem to be trained in a discriminative fashion, but they can be used to sample the next character in a sequence, then feed in a new string with the predicted/sampled character appended to the string. This is more of a general discussion than a single question. Is there a fundamental difference between learning a probability distribution and sampling from it (like Char-RNN), or is Char-RNN also somehow implicitly learning to become a generative model like an RBM?
Classical probability theory gives all sequences of fair coin tosses of the same length the same probability. Indeed, we might well want to say that the second sequence is entirely random, whereas the first one is entirely nonrandom. But what are we to make in this context of, say, the sequence obtained by taking our first sequence, tossing a coin for each bit, and if the coin comes up heads, replacing that bit by the corresponding one in the second sequence? There are deep and fundamental questions involved in trying to understand why some sequences should count as "random," or "partially random," and others as "predictable," and how we can transform our intuitions about these concepts into meaningful mathematical notions.
Sequence-to-sequence models have achieved impressive results on various tasks. However, they are unsuitable for tasks that require incremental predictions to be made as more data arrives or tasks that have long input sequences and output sequences. This is because they generate an output sequence conditioned on an entire input sequence. In this paper, we present a Neural Transducer that can make incremental predictions as more input arrives, without redoing the entire computation. Unlike sequence-to-sequence models, the Neural Transducer computes the next-step distribution conditioned on the partially observed input sequence and the partially generated sequence.