The video that accompanies that paper is crazy. Shows the confusion of a conv-net classifying frames of a video of a polar bear:https://youtu.be/M4ys8c2NtsE
-
-
- 3 more replies
New conversation -
-
-
I think it is quite refreshing that the community is working hard and honestly to quantify the limitations of the current SOTA. I’m not a die-hard advocate of DNNs. Let’s turn this from negative to positive. Gary, I’d love to hear more about what you advocate as the way forward.
-
Judea Pearl is advocating a different approach,
#causality instead of correlation:https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X … - 4 more replies
New conversation -
-
-
It's a bit like those reinforcement learning algorithms that stop working if you simply rescale the reward function by a constant. It's outrageous that these algorithms don't satisfy basic invariance requirements
- 3 more replies
New conversation -
-
-
Glad to see a lot of research examining more comprehensively previous research. The size of the DL community is unmatched. Cannot say the same about other AI tribes.
Thanks. Twitter will use this to make your timeline better. UndoUndo
-
-
-
Hinton has been poking at CNN (and backprop) for a while - they are 2D representations. Don’t account fir brilliance, orientation, and color (I think he calls them pose.) Require lots of samples with differing angles, color, brightness
Thanks. Twitter will use this to make your timeline better. UndoUndo
-
-
-
I'm surprised this is news. Another way to confuse a DNN is to reclassify a screen grab of an image. The .jpg compression often caused the original and the screengrab to be classified differently when I tried this (though I can't remember which net I used)
- 2 more replies
New conversation -
Loading seems to be taking a while.
Twitter may be over capacity or experiencing a momentary hiccup. Try again or visit Twitter Status for more information.