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can a neural network learn to walk as a physical object in a physics simulation? here I train walking neural nets with an evolutionary algorithm. The input nodes/feet are activated by sine waves at learned phases & connections between two neurons extend based on their difference
This is a baby GPT with two tokens 0/1 and context length of 3, viewing it as a finite state markov chain. It was trained on the sequence "111101111011110" for 50 iterations. The parameters and the architecture of the Transformer modifies the probabilities on the arrows. E.g. we…
youtube.com/watch?v=GSV5UD… Top Comment: Lesson: don't argue when you became parents, or your kids will install Arch.
Just wondering why all the linux mail clients are so old-fashioned. They are powerful, plugin-enriched, but ugly.
Want a remote Data Science / ML job? Here are 5 jobs that are remote and pay in USD, -- Thread --
📢 A 🧵on the future of NLP model inputs. What are the options and where are we going? 🔭 1. Task-specific finetuning (FT) 2. Zero-shot prompting 3. Few-shot prompting 4. Chain of thought (CoT) 5. Parameter-efficient finetuning (PEFT) 6. Dialog [1/]
The term 'spurious correlations' is often used informally in NLP to denote any undesirable feature-label correlations. But are all spurious features alike? #EMNLP2022 paper tries to address this question through a causal lens - arxiv.org/abs/2210.14011 (w/ Xiang & @hhexiy)
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Who to follow
-
Tiantian Feng
@tiantiaf -
Zhe Zeng
@zhezeng0908 -
Tian Li
@litian0331 -
Chacha Chen
@chachaachen -
Lin Gui
@ybnbxb -
Yu Gui
@YuChicago1234 -
Ying Jin
@YingJin531 -
Kexun Zhang✈️NeurIPS 2024
@kexun_zhang -
Florida, yes
@funandgames333 -
Zihao Wang
@wzihao12 -
Limeng Cui
@lmcui -
Pengyu Cheng
@cheng_pengyu -
Cong (Clarence) Jiang
@statsCong -
Xuxing Chen
@XuxingChen3 -
Yibo Jiang
@yibophd
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