78 points by johndoe 1 year ago flag hide 16 comments
john_doe 1 year ago next
Fascinating approach to data compression! I wonder how it compares to existing compression algorithms in terms of efficiency and practicality.
john_doe 1 year ago next
@code_nerd - definitely, the original paper goes into implementation and limitations in detail. I think it's more beneficial to apply this on more 'real-world' dataset sizes.
code_nerd 1 year ago prev next
Great summary of neural compression! I'd like to see the implementation details and limitations of this study for better understanding.
research_fan 1 year ago next
@alice_87 - it's a good question, I think the authors hinted at the fact that their implementation can handle streaming data, but you'd need to test the variable-length cases on your own.
alice_87 1 year ago next
@research_fan - the concern about the variable-length data is important, as data is often dynamic and comes in various sizes, which affects latency and throughput.
deep_learner 1 year ago next
@alice_87 - that's a valid concern. I'll look forward to seeing the authors' plan for supporting dynamic data sizes and performance optimization.
alice_87 1 year ago prev next
How does this method handle variable-length data? Is there any potential for compression of streaming data?
code_nerd 1 year ago next
@john_doe - I agree! I love these concepts, but it's essential to see how they perform in real-world scenarios and discuss the caveats.
john_doe 1 year ago next
The authors did mention that they plan to release their datasets, but I don't think they mentioned the code. However, it should be possible to recreate their implementation.
research_fan 1 year ago next
@john_doe - definitely. It's critical to consider the practical aspect of these methods regarding their computation needs and ease of usage in a variety of environments.
deep_learner 1 year ago prev next
The paper seems very interesting. I wonder if the authors plan to make their datasets and code publicly available for further research and benchmarking.
code_nerd 1 year ago next
@deep_learner - that's a useful contribution if they do, as it'd help others reproduce their results and enhance the algorithms for different use-cases.
code_nerd 1 year ago next
@deep_learner - I second that. Having an open and accessible codebase is important for scientific advancement and reproducibility.
hadooper 1 year ago prev next
I'm curious about the computational resources required to use this neural compression method. How feasible is it for widespread use, especially for smaller organizations?
john_doe 1 year ago next
@hadooper - the authors didn't provide specific details about computation required, but it's an important aspect to consider when working with neural methods like these.
quant_analyst 1 year ago prev next
It seems they used a recurrent neural network (RNN) as the backbone of the compression. What do you think about using a transformer-based architecture instead, considering recent advances?