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GLBench: A Comprehensive Benchmark for Graph with Large Language Models
The emergence of large language models (LLMs) has revolutionized the way we interact with graphs, leading to a new paradigm called GraphLLM.
Learning Temporal Pose Estimation from Sparsely-Labeled Videos
Gedas Bertasius, Christoph Feichtenhofer, Du Tran, Jianbo Shi, Lorenzo Torresani
Neural Information Processing Systems http://nips.cc/
MintNet: Building Invertible Neural Networks with Masked Convolutions
Yang Song, Chenlin Meng, Stefano Ermon
Neural Information Processing Systems http://nips.cc/
BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing
Our model is built using the pre-trained Stable Diffusion model trained on web-scraped datasets. Proper content moderation and regulation are highly advised to prevent undesirable consequence. In Figure 1, we outline common failure cases of the model. Subject images used for finetuning are shown on the left. We briefly introduce these methods below.
Deepcode: Feedback Codes via Deep Learning
Hyeji Kim, Yihan Jiang, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
SNR = 10 log10 2 Figure 1: AWGNchannelwithnoisyfeedback (left). Figure 5: Noiseinfirstphasevs. firstparitybit (left) andsecondparitybit (right).