Neural Stored-program Memory
Neural networks powered with external memory simulate computer behaviors. These models, which use the memory to store data for a neural controller, can learn algorithms and other complex tasks. In this paper, we introduce a new memory to store weights for the controller, analogous to the stored-program memory in modern computer architectures. The proposed model, dubbed Neural Stored-program Memory, augments current memory-augmented neural networks, creating differentiable machines that can switch programs through time, adapt to variable contexts and thus fully resemble the Universal Turing Machine or Von Neumann Architecture. A wide range of experiments demonstrate that the resulting machines not only excel in classical algorithmic problems, but also have potential for compositional, continual, few-shot learning and question-answering tasks.
= Summary A variation of Neural Turing Machines (and derived models) storing the configuration of the controller in a separate memory, which is then *softly* read during evaluation of the NTM. Experiments show moderate improvements on some simple multi-task problems. = Strong/Weak Points + The idea of generalising NTMs to *universal* TMs is interesting in itself ... - ... however, the presented solution seems to be only half-way there, as the memory used for the *program* is still separate from the memory the NUTM operates on. Hence, modifying the program itself is not possible, which UTMs can do (even though it*s never useful in practice...) - The core novelty relative to standard NTMs is that in principle, several separate programs can be stored, and that at each timestep, the *correct* one can be read. However this read mechanism is weak, and requires extra tuning with a specialized loss (Eq. (6)) ~ It remains unclear where this is leading - clearly NTMs and NUTMs (or their DNC siblings) are currently not useful for interesting tasks, and it remains unclear what is missing to get there. The current paper does not try show the way there. - The writing is oddly inconsistent, and important technical details (such as the memory read/write mechanism) are not documented. I would prefer the paper to be self-contained, to make it easier to understand the differences and commonalities between NTM memory reads and the proposed NSM mechanism. = Recommendation Overall, I don*t see clear, actionable insights in this submission, and thus believe that it will not provide great value to the ICLR audience; hence I would recommend rejecting the paper to allow the authors to clarify their writing and provide more experimental evidence of the usefulness of their contribution. = Minor Comments + Page 6: *As NUTM requires fewer training samples to converge, it generalizes better to unseen sequences that are longer than training sequences.* - I don*t understand the connecting between the first and second part of the sentence. This seems pure speculation, not a fact.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Neural Stored-program Memory
Neural networks powered with external memory simulate computer behaviors. These models, which use the memory to store data for a neural controller, can learn algorithms and other complex tasks. In this paper, we introduce a new memory to store weights for the controller, analogous to the stored-program memory in modern computer architectures. The proposed model, dubbed Neural Stored-program Memory, augments current memory-augmented neural networks, creating differentiable machines that can switch programs through time, adapt to variable contexts and thus fully resemble the Universal Turing Machine or Von Neumann Architecture. A wide range of experiments demonstrate that the resulting machines not only excel in classical algorithmic problems, but also have potential for compositional, continual, few-shot learning and question-answering tasks.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



= Summary A variation of Neural Turing Machines (and derived models) storing the configuration of the controller in a separate memory, which is then *softly* read during evaluation of the NTM. Experiments show moderate improvements on some simple multi-task problems. = Strong/Weak Points + The idea of generalising NTMs to *universal* TMs is interesting in itself ... - ... however, the presented solution seems to be only half-way there, as the memory used for the *program* is still separate from the memory the NUTM operates on. Hence, modifying the program itself is not possible, which UTMs can do (even though it*s never useful in practice...) - The core novelty relative to standard NTMs is that in principle, several separate programs can be stored, and that at each timestep, the *correct* one can be read. However this read mechanism is weak, and requires extra tuning with a specialized loss (Eq. (6)) ~ It remains unclear where this is leading - clearly NTMs and NUTMs (or their DNC siblings) are currently not useful for interesting tasks, and it remains unclear what is missing to get there. The current paper does not try show the way there. - The writing is oddly inconsistent, and important technical details (such as the memory read/write mechanism) are not documented. I would prefer the paper to be self-contained, to make it easier to understand the differences and commonalities between NTM memory reads and the proposed NSM mechanism. = Recommendation Overall, I don*t see clear, actionable insights in this submission, and thus believe that it will not provide great value to the ICLR audience; hence I would recommend rejecting the paper to allow the authors to clarify their writing and provide more experimental evidence of the usefulness of their contribution. = Minor Comments + Page 6: *As NUTM requires fewer training samples to converge, it generalizes better to unseen sequences that are longer than training sequences.* - I don*t understand the connecting between the first and second part of the sentence. This seems pure speculation, not a fact.
assistant
= Summary A variation of Neural Turing Machines (and derived models) storing the configuration of the controller in a separate memory, which is then *softly* read during evaluation of the NTM. Experiments show moderate improvements on some simple multi-task problems. = Strong/Weak Points + The idea of generalising NTMs to *universal* TMs is interesting in itself ... - ... however, the presented solution seems to be only half-way there, as the memory used for the *program* is still separate from the memory the NUTM operates on. Hence, modifying the program itself is not possible, which UTMs can do (even though it*s never useful in practice...) - The core novelty relative to standard NTMs is that in principle, several separate programs can be stored, and that at each timestep, the *correct* one can be read. However this read mechanism is weak, and requires extra tuning with a specialized loss (Eq. (6)) ~ It remains unclear where this is leading - clearly NTMs and NUTMs (or their DNC siblings) are currently not useful for interesting tasks, and it remains unclear what is missing to get there. The current paper does not try show the way there. - The writing is oddly inconsistent, and important technical details (such as the memory read/write mechanism) are not documented. I would prefer the paper to be self-contained, to make it easier to understand the differences and commonalities between NTM memory reads and the proposed NSM mechanism. = Recommendation Overall, I don*t see clear, actionable insights in this submission, and thus believe that it will not provide great value to the ICLR audience; hence I would recommend rejecting the paper to allow the authors to clarify their writing and provide more experimental evidence of the usefulness of their contribution. = Minor Comments + Page 6: *As NUTM requires fewer training samples to converge, it generalizes better to unseen sequences that are longer than training sequences. * - I don*t understand the connecting between the first and second part of the sentence. This seems pure speculation, not a fact.