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Farshad Noravesh
United States
เข้าร่วมเมื่อ 29 มิ.ย. 2015
I am open to collaborating with researchers on "semantic parsing" and "machine reasoning".
I also use reinforcement learning and continual learning to model human brain as well as artificial intelligence. I use "separation of concerns principle".
Email: noraveshfarshad@gmail.com
"In your relationships with one another, have the same mindset as Christ Jesus" -- Philippians 2:5
"Then Jesus said unto His disciples, If any man will come after Me, let him deny himself and take up his cross and follow Me."-- Matthew 15:24
"No one can come to me unless the Father who sent me draws him. And I will raise him up on the last day."--John 6:44
"Jesus answered, I am the way and the truth and the life. No one comes to the Father except through Me." --John 14:6
"Do not be conformed to this world, but be transformed by the renewal of your mind, that by testing you may discern what is the will of God, what is good and acceptable and perfect" -- Romans 12:2
I also use reinforcement learning and continual learning to model human brain as well as artificial intelligence. I use "separation of concerns principle".
Email: noraveshfarshad@gmail.com
"In your relationships with one another, have the same mindset as Christ Jesus" -- Philippians 2:5
"Then Jesus said unto His disciples, If any man will come after Me, let him deny himself and take up his cross and follow Me."-- Matthew 15:24
"No one can come to me unless the Father who sent me draws him. And I will raise him up on the last day."--John 6:44
"Jesus answered, I am the way and the truth and the life. No one comes to the Father except through Me." --John 14:6
"Do not be conformed to this world, but be transformed by the renewal of your mind, that by testing you may discern what is the will of God, what is good and acceptable and perfect" -- Romans 12:2
วีดีโอ
Part 44: optimal representations for covariate shift
มุมมอง 34 ชั่วโมงที่ผ่านมา
Part 44: optimal representations for covariate shift
Part 43: tackling concept shift in text classification using entailment-style modeling
มุมมอง 87 ชั่วโมงที่ผ่านมา
Part 43: tackling concept shift in text classification using entailment-style modeling
Part 42: Generating to evolving domains with latent structure-aware sequential autoencoder
มุมมอง 712 ชั่วโมงที่ผ่านมา
Part 42: Generating to evolving domains with latent structure-aware sequential autoencoder
Part 41: exploring covariate and concept shift for detection and confidence calibration of ...
มุมมอง 1916 ชั่วโมงที่ผ่านมา
Part 41: exploring covariate and concept shift for detection and confidence calibration of ...
Part 40: overcoming concept shift in domain-aware settings through consolidated internal distrib...
มุมมอง 1819 ชั่วโมงที่ผ่านมา
Part 40: overcoming concept shift in domain-aware settings through consolidated internal distrib...
Part 39: adversarial domain adaptation with conditional and label shift: infer, align and iterate
มุมมอง 1121 ชั่วโมงที่ผ่านมา
Part 39: adversarial domain adaptation with conditional and label shift: infer, align and iterate
Part 38: on the label shift in domain adaptation via Wasserstein distance
มุมมอง 19วันที่ผ่านมา
Part 38: on the label shift in domain adaptation via Wasserstein distance
Part 37: A class-aware optimal transport approach with higher-order moment matching for unsupervi...
มุมมอง 10วันที่ผ่านมา
Part 37: A class-aware optimal transport approach with higher-order moment matching for unsupervi...
Part 36: LAMDA: label matching deep domain adaptation
มุมมอง 19วันที่ผ่านมา
Part 36: LAMDA: label matching deep domain adaptation
Part 35: class-imbalanced domain adaptation : an empiricial odyssey
มุมมอง 17วันที่ผ่านมา
Part 35: class-imbalanced domain adaptation : an empiricial odyssey
Part 34: adversarial unsupervised domain adaptation with conditional and label shift: infer,....
มุมมอง 1214 วันที่ผ่านมา
Part 34: adversarial unsupervised domain adaptation with conditional and label shift: infer,....
Part 33: interpretable domain adaptation for hidden subdomain alignment in the context of pretra...
มุมมอง 914 วันที่ผ่านมา
Part 33: interpretable domain adaptation for hidden subdomain alignment in the context of pretra...
Part 32: discriminative feature alignment: improving transferability of unsupervised domain adap...
มุมมอง 1014 วันที่ผ่านมา
Part 32: discriminative feature alignment: improving transferability of unsupervised domain adap...
Part 31: domain adaptation with auxiliary target domain-oriented classifier
มุมมอง 814 วันที่ผ่านมา
Part 31: domain adaptation with auxiliary target domain-oriented classifier
Part 30: Don't stop pretraining: Adapt language models to domains and tasks
มุมมอง 714 วันที่ผ่านมา
Part 30: Don't stop pretraining: Adapt language models to domains and tasks
Part 28: beyond sharing weights for deep domain adaptation
มุมมอง 2221 วันที่ผ่านมา
Part 28: beyond sharing weights for deep domain adaptation
Part 27: overcoming catasrophic forgetting during domain adaptation of seq2seq language generation
มุมมอง 2821 วันที่ผ่านมา
Part 27: overcoming catasrophic forgetting during domain adaptation of seq2seq language generation
Part 26: semi-supervised domain adaptation with source label adaptation
มุมมอง 1921 วันที่ผ่านมา
Part 26: semi-supervised domain adaptation with source label adaptation
Part 25: model adaptation: unsupervised domain adaptation without source data
มุมมอง 2021 วันที่ผ่านมา
Part 25: model adaptation: unsupervised domain adaptation without source data
Part 24: unsupervised domain adaptation for joint information extraction
มุมมอง 921 วันที่ผ่านมา
Part 24: unsupervised domain adaptation for joint information extraction
Part 1: Four case studies in dealing with supervisors
มุมมอง 2421 วันที่ผ่านมา
Part 1: Four case studies in dealing with supervisors
Part 23: discriminator-free unsupervised domain adaptation for multi-label image classification
มุมมอง 921 วันที่ผ่านมา
Part 23: discriminator-free unsupervised domain adaptation for multi-label image classification
Part 22: guiding pseudo-labels with uncertainty estimation for source-free unsupervised domain...
มุมมอง 1621 วันที่ผ่านมา
Part 22: guiding pseudo-labels with uncertainty estimation for source-free unsupervised domain...
Part 21: unsupervised intr-domain adaptation for semantic segmentation through self-supervision
มุมมอง 1828 วันที่ผ่านมา
Part 21: unsupervised intr-domain adaptation for semantic segmentation through self-supervision
Part 20: Adversarial Discriminative Domain Adaptation
มุมมอง 1528 วันที่ผ่านมา
Part 20: Adversarial Discriminative Domain Adaptation
太棒了先生🍅
great ..thanks
Hilbert has Norm expressed as Inner products.
y would be x - (1/alpha)grad f(x) at 11:44
if you understand one word in the whole fking video, then you have no life! Go make better use of your time.
Great job ! I am impressed !
in these two videos of RKHS you mentioned your videos in which you have talked about MMD and KL, how can I find them in your channel. what is the titles of the videos or playlist ? thanks.
the list are 2 videos
so happy i find your channel. Good video!
HI , thanks for such good work , but what if I want to let the HMM learns directly distribution from data instead of me imposing the "gaussian" in probabilityType ? is this possible ? your input is highly appreciated
hello sir Farshad thanks about this clip. very good
are these types of models applicable / implementable on a cloud infrastructure like IBM Watson
Pretty impressive!!! I love the approach, can't wait to see an update
Thanks a lot for this great series . is there a prerequisite for this course or any text book
Thank you so much for your lecture!
Excellent explanation! For what purpose did you create these lectures?
Thanks!
It's great! Can you share this document in pdf, sir? Thank you very much.
These videos don't get the views they deserve. I do my best to share them wherever possible!
Some brilliant videos you have on your channel where can beginners start off , could you make a playlist that we can walk through to learn steb by step.
Will the optimization channel continue to be updated?
Hi! do you know how to use a predictor based on dissimilarity functions?
Can you provide an explanation on Truncated rayleigh flow method (rifle) please
awesome thanks!!
you should explain it with practical explanation so we could understand it better
Thank you!
Keep up the good work !
Brilliant
thats brilliant how have you measured the regime ? or can you just use volatility
For me selective inference is a new science which applies topology to data analysis. So it covers all bayesians systematically !!! It also covers information theory. So strange that we are talking about these important intuitions in 2020 and not in 2000.
The content is fairly technical. It would be nice if you could break it down/ give links/ state prerequisites
Maybe you should publish the code, because certain parts are impossible to read. Greetings!
سلام چطور میتونم با شما در ارتباط باشم؟
Farshad, good job, I have same idea to use Deep Reinforcement Learning for trading, but I am behind you and study RL now.