Unanswered Questions
4,545 questions with no upvoted or accepted answers
16
votes
1
answer
392
views
Will parameter sweeping on one split of data followed by cross validation discover the right hyperparameters?
Let's call our dataset splits train/test/evaluate. We're in a situation where we require months of data. So we prefer to use the evaluation dataset as infrequently as possible to avoid polluting our ...
CommunityBot
- 1
11
votes
0
answers
414
views
Extending FaceNet’s triplet loss to object recognition
FaceNet uses a novel loss metric (triplet loss) to train a model to output embeddings (128-D from the paper), such that any two faces of the same identity will have a small Euclidean distance, and ...
CommunityBot
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10
votes
1
answer
934
views
Are Cellular Neural Networks one type of Neural Networks?
I am researching Cellular Neural Networks and have already read Chua's two articles (1988). In cellular neural networks, a cell is only in relation with its neighbors. So it is easy to use them for ...
CommunityBot
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8
votes
2
answers
939
views
Are there transformer-based architectures that can produce fixed-length vector encodings given arbitrary-length text documents?
BERT encodes a piece of text such that each token (usually words) in the input text map to a vector in the encoding of the text. However, this makes the length of the encoding vary as a function of ...
CommunityBot
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8
votes
0
answers
284
views
Is the Bellman equation that uses sampling weighted by the Q values (instead of max) a contraction?
It is proved that the Bellman update is a contraction (1).
Here is the Bellman update that is used for Q-Learning:
$$Q_{t+1}(s, a) = Q_{t}(s, a) + \alpha*(r(s, a, s') + \gamma \max_{a^*} (Q_{t}(s',
...
CommunityBot
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8
votes
2
answers
462
views
How can I solve the zero subset sum problem with hill climbing?
I want to solve the zero subset sum problem with the hill-climbing algorithm, but I am not sure I found a good state space for this.
Here is the problem: consider we have a set of numbers and we want ...
CommunityBot
- 1
8
votes
0
answers
144
views
Normalizing Normal Distributions in Thompson Sampling for online Reinforcement Learning
In my implementation of Thompson Sampling (TS) for online Reinforcement Learning, my distribution for selecting $a$ is $\mathcal{N}(Q(s, a), \frac{1}{C(s,a)+1})$, where $C(s,a)$ is the number of times ...
- 41.1k
8
votes
1
answer
188
views
What is the impact of using multiple BMUs for self-organizing maps?
Here's a sort of a conceptual question. I was implementing a SOM algorithm to better understand its variations and parameters. I got curious about one bit: the BMU (best matching unit == the neuron ...
CommunityBot
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8
votes
0
answers
125
views
What are the current trends/open questions in logics for knowledge representation?
What are the future prospects in near future from a theoretical investigation of description logics, and modal logics in the context of artificial intelligence research?
CommunityBot
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8
votes
0
answers
1k
views
Is there a difference in the architecture of deep reinforcement learning when multiple actions are performed instead of a single action?
I've built a deep deterministic policy gradient reinforcement learning agent to be able to handle any games/tasks that have only one action. However, the agent seems to fail horribly when there are ...
- 41.1k
8
votes
1
answer
4k
views
Does it make sense to use batch normalization in deep (stacked) or sparse auto-encoders?
Does it make sense to use batch normalization in deep (stacked) or sparse auto-encoders?
I cannot find any resources for that. Is it safe to assume that, since it works for other DNNs, it will also ...
CommunityBot
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7
votes
2
answers
170
views
Can training a model on a dataset composed by real images and drawings hurt the training process of a real-world application model?
I'm training a multi-label classifier that's supposed to be tested on underwater images. I'm wondering if feeding the model drawings of a certain class plus real images can affect the results badly. ...
CommunityBot
- 1
7
votes
0
answers
2k
views
How is the rollout from the MCTS implemented in both of the AlphaGo Zero and the AlphaZero algorithms?
In the vanilla Monte Carlo tree search (MCTS) implementation, the rollout is usually implemented following a uniform random policy, that is, it takes random actions until the game is finished and only ...
- 41.1k
7
votes
1
answer
155
views
How does the network know which objects to track in the paper "Label-Free Supervision of Neural Networks with Physics and Domain Knowledge"?
I was reading the paper Label-Free Supervision of Neural Networks with Physics and Domain Knowledge, published at AAAI 2017, which won the best paper award.
I understand the math and it makes sense. ...
CommunityBot
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7
votes
2
answers
1k
views
What is the current state-of-the-art in Reinforcement Learning regarding data efficiency?
In other words, which existing reinforcement method learns with fewest episodes? R-Max comes to mind, but it's very old and I'd like to know if there is something better now.
CommunityBot
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