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All of these frameworks are "relatively new". TensorFlow: 1.6 years. CNTK: 1 year. PyTorch: 0.5 year. Are they really impossible to compare? > When people say

All of these frameworks are "relatively new". TensorFlow: 1.6 years. CNTK: 1 year. PyTorch: 0.5 year. Are they really impossible to compare? > When people say PyTorch is better for research, they mean That's not what "people" say. They tend to say the opposite. Maybe we can ask OP what he meant when he said it. > it is easier to implement non-trivial network architectures with it, such as recursive network It is interesting that you mention recursive networks. There are only a few dozens of researchers who work with recursive networks, and they are all accounted for, we know what tools they use. They use Chainer and DyNet.

chenzhekl 9y ago
> All of these frameworks are "relatively new". TensorFlow: 1.6 years. CNTK: 1 year. PyTorch: 0.5 year. Are they really impossible to compare? 1.6 years is a long time in DL community. > That's not what "people" say. They tend to say the opposite. Maybe we can ask OP what he meant when he said it. Go ahead! Ask it. > They use Chainer and DyNet. You know Chainer came far before PyTorch, which heavily influenced PyTorch's design. You are always saying XX is using XX. Why not talk about frameworks themselves? If you insisted to your idea, let's settle down. I don't want to start a framework war. I just want all frameworks to be equally considered.
pilooch 9y ago
With all due respect you don't appear to know much about what you are commenting about. There is a huge community of research and applications around rnn and all other architectures. Pytorch has an extremely vivid and fast growing codebase, across all neural architectures and applications, it's remarkable actually. One reason might be because it is simple an effective, including easy debugging. Another is because research now is much focused on new possibly complex and exotic architectures, new optimizers, inner guts and behavior understanding, including theoretically. And it appears pytorch makes that easy. Don't throw it away too fast as a DL enthusiast :)

chenzhekl 9y ago

> All of these frameworks are "relatively new". TensorFlow: 1.6 years. CNTK: 1 year. PyTorch: 0.5 year. Are they really impossible to compare? 1.6 years is a long time in DL community. > That's not what "people" say. They tend to say the opposite. Maybe we can ask OP what he meant when he said it. Go ahead! Ask it. > They use Chainer and DyNet. You know Chainer came far before PyTorch, which heavily influenced PyTorch's design. You are always saying XX is using XX. Why not talk about frameworks themselves? If you insisted to your idea, let's settle down. I don't want to start a framework war. I just want all frameworks to be equally considered.

pilooch 9y ago

With all due respect you don't appear to know much about what you are commenting about. There is a huge community of research and applications around rnn and all other architectures. Pytorch has an extremely vivid and fast growing codebase, across all neural architectures and applications, it's remarkable actually. One reason might be because it is simple an effective, including easy debugging. Another is because research now is much focused on new possibly complex and exotic architectures, new optimizers, inner guts and behavior understanding, including theoretically. And it appears pytorch makes that easy. Don't throw it away too fast as a DL enthusiast :)