Follow us @dair_ai for upcoming top trending ML papers of the week. ICYMI, here is the list of ML papers from last week:
MACHINE LEARNING
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DeepMatcher: Transformer Network Achieves State-of-the-Art Feature Matching
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DeepMatcher is a transformer-based network showing robust local feature matching, outperforming the state-of-the-art methods on several benchmarks. 10 of 11
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Transformers in Reinforcement Learning: A Comprehensive Survey
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A Survey on Transformers in Reinforcement Learning 8 of 11
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Large Language Models Simulate Universal Turing Machine with Memory
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Investigates the use of existing LMs (e.g, Flan-U-PaLM 540B) combined with associative read-write memory to simulate the execution of a universal Turing machine. 7 of 11
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New Multimodal Deep Learning Book Published on ArXiv
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Multimodal deep learning is a new book published on ArXiv. 4 of 11
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DeepMind Tracr: Compiler Converting RASP to Transformer Weights
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DeepMind proposes Tracr, a compiler for converting RASP programs into transformer weights. This way of constructing NNs weights enables the development and evaluation of new interpretability tools. 3 of 11
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Top Machine Learning Papers of the Week
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Top ML Papers of the Week (Jan 9-15): – DreamerV3
– DeepMatcher
– Multimodal deep learning
– Transformer compiler for RASP
– Potential misuses of LMs and mitigations
– Scaling laws for generative mixed-modal LMs
– Time series forecasting with generative modeling
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DreamerV3 Achieves Minecraft Diamond Collection Without Human Data
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DreamerV3 is a general algorithm to collect diamonds in Minecraft from scratch without human data or curricula, a long-standing challenge in AI.
— DAIR.AI (@dair_ai) 15 janvier 2023
2 of 11https://t.co/pvsmnX3TPODreamerV3 is a general algorithm to collect diamonds in Minecraft from scratch without human data or curricula, a long-standing challenge in AI. 2 of 11
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Machine Learning Types and Applications Infographic Guide
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Want to know more about #machinelearning? Check out the #infographic for a breakdown of the different types and their applications.
Learn more with @ingliguori #AI #deeplearning #DigitalTransformation #ArtificialIntelligence #Python #datascience #NeuralNetworks #GenerativeAI -
LLMs Lack True Compositional Reasoning Capabilities
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Read http://
rebooting.ai; it went to press just before LLMs became popular but lays out many challenges for genuine intelligence. I don’t see how LLMs resolve any of them. It’s not compositional and can’t reason, so it’s just a giant but superficial statistical approximator