Time2Vec: Learning a Vector Representation of Time
Time is an important feature in many applications involving events that occur synchronously and/or asynchronously. To effectively consume time information, recent studies have focused on designing new architectures. In this paper, we take an orthogonal but complementary approach by providing a model-agnostic vector representation for time, called Time2Vec, that can be easily imported into many existing and future architectures and improve their performances. We show on a range of models and problems that replacing the notion of time with its Time2Vec representation improves the performance of the final model.
# Summary This paper proposes a simple representation of time (Time2Vec) for modelling sequential data. The idea is to apply multiple sine functions to the time with trainable period and offset and concatenate them together, which is similar to positional encoding [Vaswani et al.] except that the periods and offsets are learned. The results on several sequential modelling datasets show that Time2Vec performs better than naive representations and alternative baselines. # Originality - Although the proposed representation looks simple and similar to positional encoding [Vaswani et al.], the idea of parameterizing sine functions is novel and interesting. # Quality - The proposed idea is very simple but seems very effective in practice as shown by the empirical results. - The paper also provides in-depth analysis and ablation studies showing that each of the proposed component is helpful. - Although the results presented in this paper look very promising, it would be much stronger if the paper presented results on other sequential modelling tasks such as machine translation and language modelling that the research community cares about much more. For example, it would be great if the paper showed that replacing the fixed positional encoding with Time2Vec improves the performance on a machine translation dataset. - It would be good to show the effect of the number of sine units (k) in Time2Vec. - (Minor) Clockwork RNN [Koutn´?k et al.] introduces a nice toy periodic sequential prediction problem, where the model has to recover a mixture of sine/cosine function. It looks like a natural task to show in this paper as well (which can potentially replace the synthesized data experiment). # Clarity - The paper is overall well-written. - Figure 4 is not mentioned in the main text. - Figure 5 is mentioned earlier than Figure 3. It would be good to swap them. # Significance - This paper proposes a simple but effective idea that can be potentially widely used by the research community. The paper would be stronger if it included more results on high-impact sequential modelling tasks and datasets such as machine translation.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Time2Vec: Learning a Vector Representation of Time
Time is an important feature in many applications involving events that occur synchronously and/or asynchronously. To effectively consume time information, recent studies have focused on designing new architectures. In this paper, we take an orthogonal but complementary approach by providing a model-agnostic vector representation for time, called Time2Vec, that can be easily imported into many existing and future architectures and improve their performances. We show on a range of models and problems that replacing the notion of time with its Time2Vec representation improves the performance of the final model.
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 This paper proposes a simple representation of time (Time2Vec) for modelling sequential data. The idea is to apply multiple sine functions to the time with trainable period and offset and concatenate them together, which is similar to positional encoding [Vaswani et al.] except that the periods and offsets are learned. The results on several sequential modelling datasets show that Time2Vec performs better than naive representations and alternative baselines. # Originality - Although the proposed representation looks simple and similar to positional encoding [Vaswani et al.], the idea of parameterizing sine functions is novel and interesting. # Quality - The proposed idea is very simple but seems very effective in practice as shown by the empirical results. - The paper also provides in-depth analysis and ablation studies showing that each of the proposed component is helpful. - Although the results presented in this paper look very promising, it would be much stronger if the paper presented results on other sequential modelling tasks such as machine translation and language modelling that the research community cares about much more. For example, it would be great if the paper showed that replacing the fixed positional encoding with Time2Vec improves the performance on a machine translation dataset. - It would be good to show the effect of the number of sine units (k) in Time2Vec. - (Minor) Clockwork RNN [Koutn´?k et al.] introduces a nice toy periodic sequential prediction problem, where the model has to recover a mixture of sine/cosine function. It looks like a natural task to show in this paper as well (which can potentially replace the synthesized data experiment). # Clarity - The paper is overall well-written. - Figure 4 is not mentioned in the main text. - Figure 5 is mentioned earlier than Figure 3. It would be good to swap them. # Significance - This paper proposes a simple but effective idea that can be potentially widely used by the research community. The paper would be stronger if it included more results on high-impact sequential modelling tasks and datasets such as machine translation.
assistant
# Summary This paper proposes a simple representation of time (Time2Vec) for modelling sequential data. The idea is to apply multiple sine functions to the time with trainable period and offset and concatenate them together, which is similar to positional encoding [Vaswani et al.] except that the periods and offsets are learned. The results on several sequential modelling datasets show that Time2Vec performs better than naive representations and alternative baselines. # Originality - Although the proposed representation looks simple and similar to positional encoding [Vaswani et al. ], the idea of parameterizing sine functions is novel and interesting. # Quality - The proposed idea is very simple but seems very effective in practice as shown by the empirical results. - The paper also provides in-depth analysis and ablation studies showing that each of the proposed component is helpful. - Although the results presented in this paper look very promising, it would be much stronger if the paper presented results on other sequential modelling tasks such as machine translation and language modelling that the research community cares about much more. For example, it would be great if the paper showed that replacing the fixed positional encoding with Time2Vec improves the performance on a machine translation dataset. - It would be good to show the effect of the number of sine units (k) in Time2Vec. - (Minor) Clockwork RNN [Koutn´?k et al.] introduces a nice toy periodic sequential prediction problem, where the model has to recover a mixture of sine/cosine function. It looks like a natural task to show in this paper as well (which can potentially replace the synthesized data experiment). # Clarity - The paper is overall well-written. - Figure 4 is not mentioned in the main text. - Figure 5 is mentioned earlier than Figure 3. It would be good to swap them. # Significance - This paper proposes a simple but effective idea that can be potentially widely used by the research community. The paper would be stronger if it included more results on high-impact sequential modelling tasks and datasets such as machine translation.