Generalized Additive Models (GAMs) for Meta-Regression using brms – Matthew B. Jané https://
bit.ly/4gH6OhH
#AI #MachineLearning #DeepLearning #LLMs #DataScience
RESEARCH
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Generalized Additive Models for Meta-Regression using brms
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ML Experts Discuss Speculative Decoding for Fast Token Generation
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🎥 Watch as two of our #ML experts discuss the nuances of #GenAI with @v_mohan_.
— SambaNova (@SambaNovaAI) 6 octobre 2024
In this episode, they talk about speculative decoding and its role in being able to achieve fast token generation. ⚡️
Accelerate your #dev journey ⤵️Watch as two of our #ML experts discuss the nuances of #GenAI with @v_mohan_
. In this episode, they talk about speculative decoding and its role in being able to achieve fast token generation. Accelerate your #dev journey -
Public discourse on AI declining into anti-intellectual rhetoric
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Sigh. Such a shame to see people you look up to resort to discourse that seems fit for the movie idiocracy
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AI-Generated Video Detection: Challenges and Implications
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Seguro que piensas que a día de hoy eres capaz de detectar sin problemas un vídeo generado por inteligencia artificial. Pero esta tecnología está empezando y lo más probable es que en poco tiempo seamos incapaces de notar la diferencia.
— Juan Merodio (@juanmerodio) 6 octobre 2024
Esto tiene unas implicaciones muy… pic.twitter.com/UrriNTSSNUSeguro que piensas que a día de hoy eres capaz de detectar sin problemas un vídeo generado por inteligencia artificial. Pero esta tecnología está empezando y lo más probable es que en poco tiempo seamos incapaces de notar la diferencia. Esto tiene unas implicaciones muy
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Designing Priors for Few-Shot Image Synthesis with GANs
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10). Designing Priors for Better Few-Shot Image Synthesis – training generative models like GAN with limited data is difficult; current Implicit Maximum Likelihood Estimation approaches (IMLE) have an inadequate correspondence between latent code selected for training and those
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LLM Reasoning Gaps in Grade-School Math Problem Solving
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8). Not All LLM Reasoners Are Created Equal – investigates in depth the grade-school math problem-solving capabilities of LLMs; reports that LLMs show a significant gap in reasoning; finds that LLMs display a huge performance difference when solving compositional pairs and
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Comprehensive Evaluation of OpenAI’s o1-preview LLM Model
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9). Evaluation of o1 – provides a comprehensive evaluation of OpenAI's o1-preview LLM; shows strong performance across many tasks such as competitive programming, generating coherent and accurate radiology reports, high school-level mathematical reasoning tasks, chip design
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o1-preview Analysis: Advanced Reasoning Models Show Similar LLM Trends
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6). An Analysis of o1-preview – reports that large reasoning models like o1-preview, while improving on more difficult tasks, display similar qualitative trends as previous LLMs…
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FRAMES: Framework for Evaluating LLM Factuality and Reasoning
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7). FRAMES – a unified framework to evaluate an LLM’s ability to provide factual responses, assess retrieval capabilities, and the reasoning required to generate final responses…
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Architecture Search Framework Optimizes LLM Inference Techniques
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4). Architecture Search Framework for Inference-Time Techniques – introduces a modular framework for building and optimizing LLMs by combining multiple inference-time techniques…