Arxiv Papers

May 16 2024 8
Arxiv Papers Podcast artwork

Running out of time to catch up with new arXiv papers? We take the most impactful papers and present them as convenient podcasts. If you're a visual learner, we offer these papers in an engaging video format. Our service fills the gap between overly brief paper summaries and time-consuming full paper reads. You gain academic insights in a time-efficient, digestible format. Code behind this work: https://github.com/imelnyk/ArxivPapers Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/support

RSS feed Share
Copy RSS


Subscribe on Podcast Addict


[QA] Chameleon: Mixed-Modal Early-Fusion Foundation Models

May 16 2024 8 mins  
https://arxiv.org/abs//2405.09818 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers --- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/support

Chameleon: Mixed-Modal Early-Fusion Foundation Models

May 16 2024 19 mins  
https://arxiv.org/abs//2405.09818 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers --- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/support

[QA] LoRA Learns Less and Forgets Less

May 16 2024 8 mins  
LoRA is a parameter-efficient finetuning method for large language models, but underperforms full finetuning in most cases. It offers strong regularization and diverse generations. https://arxiv.org/abs//2405.09673 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id169

LoRA Learns Less and Forgets Less

May 16 2024 13 mins  
LoRA is a parameter-efficient finetuning method for large language models, but underperforms full finetuning in most cases. It offers strong regularization and diverse generations. https://arxiv.org/abs//2405.09673 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id169

[QA] The Platonic Representation Hypothesis

May 16 2024 8 mins  
The paper argues that representations in AI models, especially deep networks, are converging towards a shared statistical model of reality, termed the platonic representation. https://arxiv.org/abs//2405.07987 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id16924760

The Platonic Representation Hypothesis

May 16 2024 17 mins  
The paper argues that representations in AI models, especially deep networks, are converging towards a shared statistical model of reality, termed the platonic representation. https://arxiv.org/abs//2405.07987 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id16924760

[QA] Improving Transformers using Faithful Positional Encoding

May 15 2024 8 mins  
New positional encoding method for Transformers improves time-series classification by preserving positional order information without loss, based on rigorous mathematics. https://arxiv.org/abs//2405.09061 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016

Improving Transformers using Faithful Positional Encoding

May 15 2024 9 mins  
New positional encoding method for Transformers improves time-series classification by preserving positional order information without loss, based on rigorous mathematics. https://arxiv.org/abs//2405.09061 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016

[QA] Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory

May 15 2024 8 mins  
Increasing Transformer model size doesn't always improve performance. A theoretical framework using associative memories and Hopfield networks explains memorization and performance dynamics in transformer-based language models. https://arxiv.org/abs//2405.08707 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https

Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory

May 15 2024 13 mins  
Increasing Transformer model size doesn't always improve performance. A theoretical framework using associative memories and Hopfield networks explains memorization and performance dynamics in transformer-based language models. https://arxiv.org/abs//2405.08707 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https