bet, i was thinking there was some computer vision edge detection going on in the images themselves — do we know what gen 3 video to video uses? depth + rgb + other stuff?
MACHINE LEARNING
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Almost-Linear RNNs Deliver Interpretable Symbolic Codes
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Almost-Linear RNNs Yield Highly Interpretable Symbolic Codes in Dynamical Systems Reconstruction Brenner et al.: https://
arxiv.org/abs/2410.14240 #Artificialintelligence #DeepLearning #MachineLearning -
Choosing Right Embedding Models for RAG Applications
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Choosing the right embedding model for your RAG application: a comprehensive guide – Unstructured https://
bit.ly/4ewHUzJ
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
AI as New Global Trade Frontier: Learning to Navigate
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Why AI is the new frontier global trade must learn to cross
#AI #AIio #BigData #ML #NLU #Futureofwork @IIoT_World @ipfconline1 @jblefevre60 @JimMarous @insurtechtalk @KirkDBorne @kuriharan @LouisSerge -
Pangea: Open Multilingual Multimodal LLM for 39 Languages
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Pangea: A Fully Open Multilingual Multimodal LLM for 39 Languages
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Paper: https://
arxiv.org/abs/2410.16153
Project website: https://
neulab.github.io/Pangea/
Model: https://
huggingface.co/neulab/Pangea-
7B
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Extended post by @xiangyue96 : -
CMU releases NaturalBench for multimodal LLM evaluation
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This week, we(two amazing research groups at CMU) released two projects in multimodal evaluation and multilingual multimodal LLMs. – NaturalBench: A vision-centric VQA evaluation benchmark that challenges all leading multimodal LLMs while using natural images and simple
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NaturalBench: Vision-Language Models Evaluation on Adversarial Samples
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Here are links to the projects and related Twitter posts NaturalBench: Evaluating Vision-Language Models on Natural Adversarial Samples
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Paper: https://
arxiv.org/abs/2410.14669
Project website: https://
linzhiqiu.github.io/papers/natural
bench/
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Extended post by @ZhiqiuLin
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OpenAI o1 Model Reasoning Patterns and Performance Analysis
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10). Reasoning Patterns of OpenAI’s o1 Model – when compared with other test-time compute methods, o1 achieved the best performance across most datasets; the authors observe that the most commonly used reasoning patterns in o1 are divide and conquer and self-refinement.
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Data Synthesis and Augmentation Techniques for Large Language Models
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4). A Survey on Data Synthesis and Augmentation for LLMs – provides a comprehensive summary of data generation techniques in the lifecycle of LLMs; includes discussions on data preparation, pre-training, fine-tuning, instruction-tuning, preference alignment, and applications.
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LongRAG Enhances RAG Understanding Long-Context Knowledge
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5). LongRAG – enhances RAG's understanding of long-context knowledge which includes global information and factual details; consists of a hybrid retriever, an LLM-augmented information extractor, a CoT-guided filter, and an LLM-augmented generator…