In Honour of This Year's NeurIPs Test of Time Paper Awardees
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Jaward Sesay
Jaward
AI & ML interests
I like to train large deep neural nets too 🧠🤖💥 | First Paper (AutoAgents: A Framework for Automatic Agent Generation) Accepted @ IJCAI 2024 | Role Model Karpathy
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Qwen/QVQ-72B-Preview
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7 days ago
nanoBLT: Simplified lightweight implementation of a character-level Byte Latent Transformer model (under 500 lines of code). The model is 2x4x2 (n_layers_encoder, n_layers_latent, n_layers_decoder) layer deep trained on ~1M bytes of tiny Shakespeare with a patch size of 4.
Code: https://github.com/Jaykef/ai-algorithms/blob/main/byte_latent_transformer.ipynb
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9 days ago
deepseek-ai/DeepSeek-V3-Base
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4 days ago
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nanoBLT: Simplified lightweight implementation of a character-level Byte Latent Transformer model (under 500 lines of code). The model is 2x4x2 (n_layers_encoder, n_layers_latent, n_layers_decoder) layer deep trained on ~1M bytes of tiny Shakespeare with a patch size of 4.
Code: https://github.com/Jaykef/ai-algorithms/blob/main/byte_latent_transformer.ipynb
Code: https://github.com/Jaykef/ai-algorithms/blob/main/byte_latent_transformer.ipynb
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14 days ago
btw the background songs in the videos are actually what I listen to during implementation
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14 days ago
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Implements from first-principle a discrete flow matching model for code generation- trained a small sized 2D dfm model on two variations of code for binary search. The result was amazing, code in comment:
Code: https://github.com/Jaykef/ai-algorithms/blob/main/dfm.ipynb
Code: https://github.com/Jaykef/ai-algorithms/blob/main/dfm.ipynb
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20 days ago
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25 days ago
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In Honour of This Year's NeurIPs Test of Time Paper Awardees
This year's NIPs Test of Time Paper Awards went to two groundbreaking papers:
1. Generative Adversarial Nets (Goodfellow et al)
2. Sequence to Sequence Learning with Neural Networks (Ilya et al)
Let's explore how these papers helped pioneered breakthroughs in today's AI:
Full Article: https://huggingface.co/blog/Jaward/nip
This year's NIPs Test of Time Paper Awards went to two groundbreaking papers:
1. Generative Adversarial Nets (Goodfellow et al)
2. Sequence to Sequence Learning with Neural Networks (Ilya et al)
Let's explore how these papers helped pioneered breakthroughs in today's AI:
Full Article: https://huggingface.co/blog/Jaward/nip
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25 days ago
Article
In Honour of This Year's NeurIPs Test of Time Paper Awardees
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26 days ago
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Lightweight implementation of the seminal paper “Sequence to Sequence Learning with Neural Networks”
Built, trained and eval a 2 layer deep seq2seq LSTM-based model (~10M params) on German-English corpus of Multi30K dataset. In honor of
ilya sutskever et al for winning this year’s NeurIPSConf Test of Time paper award 🫡
Code: https://github.com/Jaykef/ai-algorithms/blob/main/seq2seq.ipynb
Built, trained and eval a 2 layer deep seq2seq LSTM-based model (~10M params) on German-English corpus of Multi30K dataset. In honor of
ilya sutskever et al for winning this year’s NeurIPSConf Test of Time paper award 🫡
Code: https://github.com/Jaykef/ai-algorithms/blob/main/seq2seq.ipynb
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about 1 month ago
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Rethinking Backpropagation: Thoughts on What's Wrong with Backpropagation
As a young researcher, I've often pondered the limitations of backpropagation, especially when mapped with how learning occurs in the human brain. While backpropagation has been the workhorse of deep learning, it isn't without flaws. In this post, I aim to share some thoughts on these shortcomings from first principles.
Full article
https://huggingface.co/blog/Jaward/rethinking-backpropagation
As a young researcher, I've often pondered the limitations of backpropagation, especially when mapped with how learning occurs in the human brain. While backpropagation has been the workhorse of deep learning, it isn't without flaws. In this post, I aim to share some thoughts on these shortcomings from first principles.
Full article
https://huggingface.co/blog/Jaward/rethinking-backpropagation
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about 1 month ago
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Implements compute-efficient DeepPCR algorithm which parallelizes sequential operations thus speeding up inference and training of neural networks. DeepPCR can significantly reduce the time complexity in operations such as denoising in latent diffusion space from O(L) to O(log2 L).
Code: https://github.com/Jaykef/ai-algorithms/blob/main/deep_pcr.ipynb
Code: https://github.com/Jaykef/ai-algorithms/blob/main/deep_pcr.ipynb
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about 1 month ago
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This is supercool!!
Explores o1-like multimodal reasoning.
Multi-agents with DPO is a nice touch 👍
Paper: https://arxiv.org/pdf/2411.14432
Code: https://github.com/dongyh20/Insight-V
Explores o1-like multimodal reasoning.
Multi-agents with DPO is a nice touch 👍
Paper: https://arxiv.org/pdf/2411.14432
Code: https://github.com/dongyh20/Insight-V
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about 2 months ago
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about 2 months ago
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Ok RNNs can rap too:)
Here we implement the seminal RNN paper “Generating Text with Recurrent Neural Networks"- we train a character-level multiplicative recurrent neural network model (~250k params) for 1000 epochs with Adam opt on 2pac's "Hit 'em Up", sample was fun lol.
Code: https://github.com/Jaykef/ai-algorithms/blob/main/generating_texts_with_rnns.ipynb
Here we implement the seminal RNN paper “Generating Text with Recurrent Neural Networks"- we train a character-level multiplicative recurrent neural network model (~250k params) for 1000 epochs with Adam opt on 2pac's "Hit 'em Up", sample was fun lol.
Code: https://github.com/Jaykef/ai-algorithms/blob/main/generating_texts_with_rnns.ipynb
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about 2 months ago
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about 2 months ago
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Interesting Work on Reasoning 🤔
- explores a new take on few-shot reasoning while challenging assumptions that program synthesis is necessary for abstract reasoning.
- shows test-time training + smart inference tricks can match human-average performance, though at high computational cost. Key insight: proper compute allocation matters more than method (whether symbolic or neural).
Paper: https://ekinakyurek.github.io/papers/ttt.pdf
- explores a new take on few-shot reasoning while challenging assumptions that program synthesis is necessary for abstract reasoning.
- shows test-time training + smart inference tricks can match human-average performance, though at high computational cost. Key insight: proper compute allocation matters more than method (whether symbolic or neural).
Paper: https://ekinakyurek.github.io/papers/ttt.pdf
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2 months ago
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It's work like this that in some way signal the eventual “dominance” of AI over all the sciences.
“We train our model on the six-dimensional N-body phase space, predicting particle velocities as the time derivative of the model’s displacement outputs”
The emulator is capable of predicting
the nonlinear displacement and velocity fields for 128^3 particles in half a second on a single GPU🤯
“We train our model on the six-dimensional N-body phase space, predicting particle velocities as the time derivative of the model’s displacement outputs”
The emulator is capable of predicting
the nonlinear displacement and velocity fields for 128^3 particles in half a second on a single GPU🤯
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