Category: AI
-
GFlowNet Study – Dec 11, 2023
How is this done and what do “theories shared across examples” refer to? This makes GFlowNets amortized probabilistic inference machines that can be used both to sample latent variables (as in [14]) or parameters and theories shared across examples (as in [5]). TO_BE_ANSWERED. How is approximate and amortized marginalization done with GFlowNet? They can also…
-
GFlowNet Study – Dec 10, 2023
One more question on this statement: “Importantly, when the reward function R represents the product of a prior (over some random variable) times a likelihood (measuring how well that choice of value of the random variable fits some data), the GFlowNet will learn to sample from the corresponding Bayesian posterior.” What does the random variable…
-
GFlowNet Study – Dec 8, 2023
I am reading… when the reward function R represents the product of a prior (over some random variable) times a likelihood (measuring how well that choice of value of the random variable fits some data), the GFlowNet will learn to sample from the corresponding Bayesian posterior https://milayb.notion.site/95434ef0e2d94c24aab90e69b30be9b3 In Bayesian Statistics, why do we go through…
-
GFlowNet Study – Dec 7, 2023
How does GFlowNet represent distributions and sampling over graphs? Related description in GFlowNet tutorial: “Because of the sequential construction of objects x (with an arbitrary number of steps), it is very convenient to generate variable-size structured objects, like sets, graphs, lists, programs or other recursively constructed data structures.” My thought: that means GFlowNet is capable…
-
Causal Inference Study – Dec 6, 2023
Note: a difference definition provided in Yoshua Bengio’s blog post scaling-in-the-service-of-reasoning-model-based-ml: A causal model can be understood as an exponentially large family of distributions indexed by a choice of intervention, … How can this be useful? Compared to Peter Spirtes’ one (i.e., a stat model and a causal graph) My thought: I don’t think I…
-
Causal Inference Study – Nov 29, 2023
Now I have finished studying Peter Spirtes’ Introduction to Causal Inference. I am coming back to these big questions. The bigger question to ask when I study causal inference: what principles I can draw from the field of causal inference to build causal understanding into Yoshua Bengio’s world models? My Thought: To see their relationship,…
-
Causal Inference – Nov 28, 2023
Are random variables in causal inference equivalent to features in ML? My Answer: yes. My answer after reading ChatGPT: yes and no. They both represent aspects/elements of a data set or a model, but they are established under different contexts and serve different purposes. Random variables in CI are mainly used to discover causal relationships…
-
Causal Inference Study – Nov 24, 2023
What is the “oracle” that serves as input to a constraint based search algorithm? Answer: the oracle typically refers to a black-box function that provides answers to queries made by the algorithm. Critique: this answer still doesn’t make much sense, I can create algorithms that can do anything with such an oracle. How does the…
-
Causal Inference Study – Nov 23, 2023
A list of introductory papers by major contributors in causal inference A list of texts in causal inference
-
Causal Inference Study – Nov 22, 2023
What does modeling really mean in statistical studies? Answer after reading: modeling is the process of representing a real-world phenomenon of interest with a mathematical model, usually simplified, in order to understand the phenomenon, answer questions and make predictions. The crucial steps include: identify the problem, collect data, select model, fit model (parameters) to data,…
Gang Fang's Blog 