It is known that every program or every algorithm can be converted to Turing machine. But what about the reverse process? Is there algorithm (or research trend that considers such algorithm) to convert some Turing machine into algorithm as expressed in some high level programming language (e.g. in Algol, Java, Prolog, Haskell)? The comprehensiveness/conciseness or … Read more
I am reading Long-term Temporal Convolutions for Action Recognition and under the Section 3.1, I read this: To investigate the impact of long-term temporal convolutions, we here study network inputs with different temporal extents….. and then As illustrated in Figure 2, the temporal resolution in our 60f network corresponds to 60, 30, 15, 7 and … Read more
Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it’s on-topic for Computer Science Stack Exchange. Closed 2 years ago. Improve this question Please note that I am talking in about theory rather than ”what someone would do in a real, practical situation”. Given … Read more
There is evolving notion of stacked reinforcement learning systems, e.g. https://www.ijcai.org/proceedings/2018/0103.pdf – where one RL systems executes actions of the second RL system and it itself executes action of the thrid and the reward and value flows back. So, one can consider the RL system RLn with: S – space of states; A={Ai, Ainner, Ao} … Read more
In models like GPT-2, TXL and Grover, is there a good way to know which input weights (tokens) resulted in each token of the output? Answer AttributionSource : Link , Question Author : Ahmedn1 , Answer Author : Community
Suppose I have only three independent features (x, y, z) as the input to some machine learning routine (e.g. neural network). From some domain knowledge, I know that the output o(x, y, z) must have the specific functional form o(x, y, z) = f(x)*g(y)*g(z) where the g(.) are the same function. The details of f(.) … Read more
I am currently working my way through Michael Nielsen’s ebook Neural Networks and Deep Learning and I am reading about overfitting and (L2) regularization. In this subsection, the process of L2 (a.k.a weight decay) regularization is introduced as being the process of adding the term λ2n∑w2 to the cost function so that it becomes C=C0+λ2n∑w2 … Read more
I found a paper titled Multimodal representation: Kneser-Ney Smoothing/Skip-Gram based neural language model. I am curious about how the Kneser-Ney Smoothing technique can be integrated into a feed-forward neural language model with one linear hidden layer and a softmax activation. What is the purpose of the Kneser-Ney in such a neural network, and how can … Read more
Most of the discussion about RNN and LSTM alludes to the varying ability of different RNNs to capture “long term dependency”. However, most demonstrations use generated text to show the absence of long term dependency for vanilla RNN. Is there any way to explicitly measure the time dependency of a given trained RNN, much like … Read more
I’m trying to implement SAT solver, based on backtracking algorithm and BCP. This SAT solver is trying to pick one literal from each clause, from 3-CNF SAT instances. I’ve implemented a neural network based heuristic to pick valid literal at first, and degree of how valid is given literal from clause is determined by neural … Read more
