Deep Learning
New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation Y uanxin Liu, Lei Li
Neural Information Processing Systems
Recently, open-domain text-to-video (T2V) generation models have made remarkable progress.
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Aligning Vision Models with Human Aesthetics in Retrieval: Benchmarks and Algorithms
Neural Information Processing Systems
Modern vision models are trained on very large noisy datasets.
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Local to Global: Learning Dynamics and Effect of Initialization for Transformers
Neural Information Processing Systems
In recent years, transformer-based models have revolutionized deep learning, particularly in sequence modeling.
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Recurrent Hypernetworks are Surprisingly Strong in Meta-RL
Neural Information Processing Systems
Deep reinforcement learning (RL) is notoriously impractical to deploy due to sample inefficiency.
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