Questions tagged [definitions]
For questions about the definition of terms used in artificial intelligence research and development, including the definition of intelligence, algorithms, jargon, principles, methodologies, mathematical terms, concepts, topologies, architectures, designs, jargon, and AI domains such as robotics, network training, or automated vehicles.
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How exactly does self-play work, and how does it relate to MCTS?
I am working towards using RL to create an AI for a two-player, hidden-information, a turn-based board game. I have just finished David Silver's RL course and Denny Britz's coding exercises, and so am ...
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What is local consistency in constraint satisfaction problems?
In the Constraint Propagation in CSP, it is often stated that pre-processing can solve the whole problem, so no search is required at all. And the key idea is local consistency. What does this ...
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Is a mathematical formula a form of intelligence?
Warning: This question takes us into VALIS territory, but I wouldn't underestimate the profundity of that particular philosopher.
There is a non-AI definition of intelligence which is simply "...
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What is 'fairness' in machine learning?
How does one define the concept of fairness in machine learning? I've seen the term lots of times but never used it myself in research (1, 2). Is there a generally agreed-upon definition of fairness ...
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What exactly does meta-learning in reinforcement learning setting mean?
We can use DDPG to train agents to stack objects. And stacking objects can be viewed as first grasping followed by pick and place. In this context, how does meta-reinforcement learning fit? Does it ...
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Does self-supervised learning require auxiliary tasks?
Self-supervised learning algorithms provide labels automatically. But, it is not clear what else is required for an algorithm to fall under the category "self-supervised":
Some say, self-...
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Is $s_0$ the current state in policy gradients?
As far as I understand from here (source: OpenAI), the objective function in Policy Gradient is:
$$J(\pi_{\theta})=E_{\tau\sim\pi_{\theta}}[R(\tau)],$$
where $R(\tau)=r_0+r_1+...+r_T$, with $r_t$ ...
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What is 'system card'?
What is 'system card' in these context:
https://ai.meta.com/blog/system-cards-a-new-resource-for-understanding-how-ai-systems-work/
Additionally, individual model developers may provide ...
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Can I call any function a signal?
While reading the Notation of the paper titled Geometric Deep Learning Grids, Groups, Graphs, Geodesics, and Gauges, I came across the following notations.
$$
\...
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What are the definitions for the content and style of an image without using deep neural network?
In deep learning, an image is said to contain two types of features. One is the content of the image and the other is the style of the image.
Deep neural networks are generally used to obtain both ...
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Is there any reasonable notion of regret for infinite horizon discounted MDPs?
I am thinking about episodic MDPs. Usually, in episodic MDPs, it seems that we have a finite fixed horizon per episode and no discount factor. Then, a very intuitive notion of regret after $T$ ...
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What is a conditional random field?
I new in machine learning, especially in Conditional Random Fields (CRF).
I have read several articles and papers and in there is always associated with HMM and sequences classification. I don't ...
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What is a continuous-attractor neural network?
I am reading about CANN. However, I do not seem to grasp what it is. Maybe someone who has worked with it can explain it? I found out about it while reading about RatSLAM. I understand that it helps ...
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What is the definition of Q when the discount factor depends on time?
Suppose I want to find out the Q value of a particular state $s$ bu doing action $a$ at a particular timestep $t$. I know that the Q-value when the discount factor is given by,
$$Q(a,s)=E_{\pi}\big[...
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What is an epistemic graph in AI and how is it related to cognitive science?
I found this paper Epistemic graphs for representing and reasoning with positive and negative influences of arguments.
I haven't found any definition of or Wikipedia article on epistemic graphs on the ...
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What is meant by correlation structure?
I know only about the Pearson's correlation coefficient in literature.
Covariance between two random variables $X$ and $Y$ is defined as
$$Cov[X, Y] = \mathbb{E}[(X - \mathbb{E}[X])(Y-\mathbb{E}[Y])]$$...
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What is meant by "Zero-Shot Visual Recognition"?
Many recent research papers contain the phrase "Zero-Shot Visual Recognition".
What exactly is meant by zero-shot visual recognition? Does the task need only images or also the other data ...
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Is the formula $\frac {1}{s}\sum _{j=1}^{s}|d_{j}-y_{j}(t)|$ the correct form of 0-1 loss function, in the context of Perceptron?
Per page 7 of this MIT lecture notes, the original single-layer Perceptron uses 0-1 loss function.
Wikipedia uses
$${\displaystyle {\frac {1}{s}}\sum _{j=1}^{s}|d_{j}-y_{j}(t)|} \tag{1}$$
to denote ...
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What is dynamic data sampling in federated learning?
I am trying to learn about Federated Learning (FL), but I have a question.
What is dynamic data sampling in FL?
Cai, Lingshuang, et al. "Dynamic Sample Selection for Federated Learning with ...
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What is the definition of pre-training?
I want to pre-train a model (combined by two popular modules A and B, and both are large blocks), then fine-tune it on downstream tasks.
What if for the weight initialization for pre-training, module ...
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How to relate the definition for entailment, with soundness and completeness?
Is it fair enough to say for a language model, φ, which makes certain variable A true, and if φ also makes another variable B true, then we can conclude:
A ⊨ B
And for a certain inference calculus c,...
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Hot to calculate Maximum Normalized log Probability for Active Learning with BERT
I have encountered difficulties understanding the calculation of Maximum Normalized Log Probabilities acording to Shen et al..
With n being the sequence length, yi the label of word i. Xij is the ...
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Pooling vs Subsampling: Multiple Definitions?
I have seen people using pooling and subsampling synonymously. I have also seen people use them as different processes. I am not sure though if I have correctly inferred what they mean, when they use ...
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how to go from mathematical problem to neural network (and back)?
I am a little confused on how, you can find online papers that describe complex Machine Learning formulas in a mathematical/probabilistic way, and, in the other hands, easy tutorials that teach you ...
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