Trending Research
GraphCast: Learning skillful medium-range global weather forecasting
Global medium-range weather forecasting is critical to decision-making across many social and economic domains.
Instruction Tuning with Human Curriculum
imoneoi/openchat
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The dominant paradigm for instruction tuning is the random-shuffled training of maximally diverse instruction-response pairs.
InRank: Incremental Low-Rank Learning
To remedy this, we design a new training algorithm Incremental Low-Rank Learning (InRank), which explicitly expresses cumulative weight updates as low-rank matrices while incrementally augmenting their ranks during training.
LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching
envision-research/luciddreamer
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The recent advancements in text-to-3D generation mark a significant milestone in generative models, unlocking new possibilities for creating imaginative 3D assets across various real-world scenarios.
Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusion
google/diffseg
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The proposed method does not require any training or language dependency to extract quality segmentation for any images.
Ranked #1 on
Semantic Segmentation
on COCO-Stuff-27
minimax: Efficient Baselines for Autocurricula in JAX
This compute requirement is a major obstacle to rapid innovation for the field.
Visual In-Context Prompting
In-context prompting in large language models (LLMs) has become a prevalent approach to improve zero-shot capabilities, but this idea is less explored in the vision domain.
Improving Sample Quality of Diffusion Models Using Self-Attention Guidance
lllyasviel/fooocus
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ICCV 2023
Denoising diffusion models (DDMs) have attracted attention for their exceptional generation quality and diversity.
A Survey of Graph Meets Large Language Model: Progress and Future Directions
First of all, we propose a new taxonomy, which organizes existing methods into three categories based on the role (i. e., enhancer, predictor, and alignment component) played by LLMs in graph-related tasks.
Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents
Large language models (LLMs) have dramatically enhanced the field of language intelligence, as demonstrably evidenced by their formidable empirical performance across a spectrum of complex reasoning tasks.
