Rephonic
Artwork for Daily Paper Cast
Large Language Models
Learning Video Representations Without Natural Videos
Synthetic Data
Artificial Intelligence
Action Classification
Research Methodology
Videomae
UCF101
Machine Learning
Natural Images
HMDB51
Llamo
Imagenet
Code Generation
Natural Language Processing
Gpt-4
Kinetics 400
Constraint Back Translation
Instruction Tuning
Bitstack

We update every weekday to discuss highest-voted papers from Huggingface Daily Paper (huggingface.co/papers). Both the podcast scripts and audio are generated by AI. Feedback and suggestions are welcome! Email us: dailypapercast.ai@gmail.com Creator: Jingwen Liang, 3D ML, www.linkedin.com/in/jingwen-liang/ Gengyu Wang, LLM ML, wanggengyu.com Listen on: Spotify: open.... more

PublishesDailyEpisodes1610Foundeda year ago
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Latest Episodes

🤗 Upvotes: 139 | cs.CV, cs.AI, cs.CY

Authors:

Yu Wang, Yi Wang, Rui Dai, Yujie Wang, Kaikui Liu, Xiangxiang Chu, Yansheng Li

Title:

Urban Socio-Semantic Segmentation with Vision-Language Reasoning more

🤗 Upvotes: 130 | cs.CV

Authors:

Ailin Huang, Chengyuan Yao, Chunrui Han, Fanqi Wan, Hangyu Guo, Haoran Lv, Hongyu Zhou, Jia Wang, Jian Zhou, Jianjian Sun, Jingcheng Hu, Kangheng Lin, Liang Zhao, Mitt Huang, Song Yuan, Wenwe... more

🤗 Upvotes: 111 | cs.LG, cs.CL

Authors:

Zhiyuan Hu, Yucheng Wang, Yufei He, Jiaying Wu, Yilun Zhao, See-Kiong Ng, Cynthia Breazeal, Anh Tuan Luu, Hae Won Park, Bryan Hooi

Title:

Rewarding the Rare: ... more

🤗 Upvotes: 64 | cs.AI, cs.CL

Authors:

Zhiyuan Hu, Yunhai Hu, Juncheng Liu, Shuyue Stella Li, Yucheng Wang, Zhen Xu, See-Kiong Ng, Anh Tuan Luu, Xinxing Xu, Bryan Hooi, Cynthia Breazeal, Hae Won Park

Title:

... more

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Recent Guests

Alan Turing
Author from Stepfun
Stepfun
Episode: PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning
John Doe
Author from Tsinghua University
Tsinghua University
Episode: PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning
Jane Smith
Author from MIT
MIT
Episode: PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning
Shaobo Wang
Co-author on the discussed paper, researcher at Epic Lab
Epic Lab, Shanghai Jiao Tong University
Episode: Winning the Pruning Gamble: A Unified Approach to Joint Sample and Token Pruning for Efficient Supervised Fine-Tuning
Jiaming Wang
Co-author on the discussed paper, researcher at Epic Lab
Epic Lab, Shanghai Jiao Tong University
Episode: Winning the Pruning Gamble: A Unified Approach to Joint Sample and Token Pruning for Efficient Supervised Fine-Tuning
Linfeng Zhang
Co-author on the discussed paper, researcher at Epic Lab
Epic Lab, Shanghai Jiao Tong University
Episode: Winning the Pruning Gamble: A Unified Approach to Joint Sample and Token Pruning for Efficient Supervised Fine-Tuning
Yuying Ge
Researcher and team member from ARC Lab
Tencent PCG
Episode: ARC-Hunyuan-Video-7B: Structured Video Comprehension of Real-World Shorts
Benyou Wang
One of the authors of the research paper discussed
Chinese University of Hong Kong, Shenzhen
Episode: On the Compositional Generalization of Multimodal LLMs for Medical Imaging
Zhenyang Cai
One of the authors of the research paper discussed
Chinese University of Hong Kong, Shenzhen
Episode: On the Compositional Generalization of Multimodal LLMs for Medical Imaging

Hosts

Echo
One of the hosts leading discussions on AI research papers, bringing insights from both academic and practical perspectives.
Nova
Co-Host who actively engages in unraveling complex AI concepts and methodologies presented by recent research.

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Apple Podcasts
#239
Philippines/Science

Talking Points

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QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs
Q: What does QeRL aim to solve?
QeRL aims to solve the efficiency and exploration issues in reinforcement learning for large language models.
BitStack: Fine-Grained Size Control for Compressed Large Language Models in Variable Memory Environments
Q: How does BitStack tackle memory availability?
BitStack breaks down the weight matrices of large language models iteratively using singular value decomposition, which allows for dynamic adjustments based on available memory.
InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model
Q: How does the IXC-2.5-Reward work?
The IXC-2.5-Reward model aims to enhance multimodal conversations using reinforcement learning by constructing a comprehensive multimodal preference dataset across diverse domains and using this data to train more robust AI models.
NovelSeek: When Agent Becomes the Scientist -- Building Closed-Loop System from Hypothesis to Verification
Q: What data sets or benchmarks did they use to validate all of this?
They validated AHGT on IEEE benchmarks, particularly scenarios involving renewable energy sources and perturbed grid networks.
NovelSeek: When Agent Becomes the Scientist -- Building Closed-Loop System from Hypothesis to Verification
Q: What exactly does EENH pool bring to the table?
EENH pool integrates both global and local features, maintaining critical structural information while simplifying the graph.

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Frequently Asked Questions About Daily Paper Cast

What is Daily Paper Cast about and what kind of topics does it cover?

A unique exploration of cutting-edge research, this engaging audio series focuses on the most acclaimed academic papers sourced from Huggingface Daily Paper. Through lively discussions, the hosts break down complex topics related to artificial intelligence and machine learning, emphasizing their implications and applications in practical scenarios. The podcast, delivered on a daily basis, is particularly notable for its innovative approach, utilizing AI to generate both the scripts and audio, allowing a seamless flow of new, relevant content for listeners consistently eager for knowledge in the fast-evolving tech landscape. Each episode brings fresh insights to both seasoned researchers and curious minds alike, making it a valuable resource... more

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1. Alan Turing
2. John Doe
3. Jane Smith
4. Shaobo Wang
5. Jiaming Wang
6. Linfeng Zhang
7. Yuying Ge
8. Benyou Wang

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