任雨山

Seventy3

73播客,名字取材于Sheldon最喜欢的数字,内容由NotebookLM生成,每天跟随AI读AI业界论文。

Besuch unbedingt die Website des Podcasts und unterstütze die Macher: www.xiaoyuzhoufm.com

Autor

任雨山

Kategorie

Technology

Podcast-Website

www.xiaoyuzhoufm.com

Neueste Folge

23. Okt 2026

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加餐003-FAN (Fourier Analysis Network) 09.10.2024

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: FAN: Fourier Analysis Networks This briefing document reviews the key themes and findings from the research paper "FAN: Fourier Analysis Networks". The paper tackles the challenge of modeling periodicity in neural networks, a crucial aspect often overlooked by popular architectures like MLPs and Transformers. Key Problem:...

加餐002-Differential Transformer 09.10.2024

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: Differential Transformer Source: Ye, Tianzhu, et al. "Differential Transformer." arXiv preprint arXiv:2410.05258 (2024). Main Theme: The paper introduces DIFF Transformer, a novel Transformer architecture designed to enhance the attention mechanism in Large Language Models (LLMs) by mitigating the issue of over-attention t...

【第八期】RNN Encoder-Decoder解读 08.10.2024

Seventy3: 用NotebookML将论文生成播客,让大家跟着AI一起进步。 今天的主题是: Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation Source: Cho et al. "Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation" Main Themes: * This paper introduces a novel neural network architecture called RNN Encoder-Decoder for improving p...

【第七期】GRU original解读 07.10.2024

Seventy3: 用NotebookML将论文生成播客,让大家跟着AI一起进步。 今天的主题是: Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling Source: "Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling" by Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Main Focus: This paper compares the performance of different recurrent neural network (RNN...

【第六期】GRU-RNN解读 06.10.2024

Seventy3: 用NotebookML将论文生成播客,让大家跟着AI一起进步。 今天的主题是: On the Properties of Neural Machine Translation: Encoder–Decoder Approaches Source: Cho et al. "On the Properties of Neural Machine Translation: Encoder–Decoder Approaches" (2014) Main Themes: * Neural Machine Translation (NMT): This paper analyzes a relatively new approach to statistical machine translation based entirely...

加餐001-Were RNNs All We Needed? 06.10.2024

Seventy3: 用NotebookLM将论文生成播客,让大家跟着AI一起进步。 今天的主题是: Were RNNs All We Needed? Main Theme: This research paper revisits traditional recurrent neural networks (RNNs) like LSTMs and GRUs, proposing simplified versions – minLSTM and minGRU – that address the scalability limitations of their predecessors while achieving comparable performance to modern sequence models. Key Ideas...

【第五期】Movie Gen 05.10.2024

Seventy3: 用NotebookML将论文生成播客,让大家跟着AI一起进步。 今天的主题是: Movie Gen: A Cast of Media Foundation Models This briefing document reviews the key themes and findings presented in the research paper "movie-gen-research-paper.pdf", focusing on the development and capabilities of Meta's MovieGen AI system. MovieGen is a suite of AI models designed for high-quality video and audio genera...

【第四期】LSTM original解读 04.10.2024

Seventy3: 用NotebookML将论文生成播客,让大家跟着AI一起进步。 今天的主题是: Long Short-Term Memory (LSTM) Source: Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. Main Theme: This paper introduces Long Short-Term Memory (LSTM), a novel recurrent neural network (RNN) architecture designed to address the vanishing gradient problem that plagues tr...

【第三期】LSTM解读 03.10.2024

Seventy3: 用NotebookML将论文生成播客,让大家跟着AI一起进步。 今天的主题是: Long Short-Term Memory-Networks for Machine Reading Source: Cheng, J., Dong, L., & Lapata, M. (2016). Long short-term memory-networks for machine reading. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (pp. 2094-2103). Main Theme: This paper introduces the Long Short-Term Memory-N...

【第二期】Transformer: Attention is All you Need 02.10.2024

Seventy3: 用NotebookML将论文生成播客,让大家跟着AI一起进步。 今天的主题是: Attention Is All You Need Source: Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30. Main Theme: This paper introduces the Transformer, a novel neural network architecture for sequence t...

【第一期】NeRF解读 02.10.2024

Seventy3: 用NotebookML将论文生成播客,让大家跟着AI一起进步。 今天的主题是: NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis - A Detailed Briefing This briefing document reviews the key themes and findings presented in the paper "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis" by Ben Mildenhall et al. Core Idea: The paper introduces NeRF, a novel appro...

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