This work addresses the time series forecasting problem with generative modeling; involves a bidirectional VAE backbone equipped with diffusion, denoising for prediction accuracy, and disentanglement for model interpretability. 11 of 11
RESEARCH
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Top trending ML papers from last week
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Follow us @dair_ai for upcoming top trending ML papers of the week. ICYMI, here is the list of ML papers from last week:
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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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Scaling Laws for Generative Mixed-Modal Language Models
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Introduces scaling laws for generative mixed-modal language models. 9 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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OpenAI Research on Generative LM Disinformation Risks
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OpenAI publishes new work analyzing how generative LMs could potentially be misused for disinformation and how to mitigate these types of risks. 5 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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