Trending Research
L-MAGIC: Language Model Assisted Generation of Images with Coherence
intellabs/mmpano
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However, the lack of global scene layout priors leads to subpar outputs with duplicated objects (e. g., multiple beds in a bedroom) or requires time-consuming human text inputs for each view.
MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series
multimodal-art-projection/map-neo
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To improve the transparency of LLMs, the research community has formed to open-source truly open LLMs (e. g., Pythia, Amber, OLMo), where more details (e. g., pre-training corpus and training code) are being provided.
vHeat: Building Vision Models upon Heat Conduction
MzeroMiko/vHeat
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A fundamental problem in learning robust and expressive visual representations lies in efficiently estimating the spatial relationships of visual semantics throughout the entire image.
Executable Code Actions Elicit Better LLM Agents
LLM agents are typically prompted to produce actions by generating JSON or text in a pre-defined format, which is usually limited by constrained action space (e. g., the scope of pre-defined tools) and restricted flexibility (e. g., inability to compose multiple tools).
Mora: Enabling Generalist Video Generation via A Multi-Agent Framework
Sora is the first large-scale generalist video generation model that garnered significant attention across society.
Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering
PeiJieSun/NESCL
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Using the graph-based collaborative filtering model as our backbone and following the same data augmentation methods as the existing contrastive learning model SGL, we effectively enhance the performance of the recommendation model.
Ranked #1 on
Recommendation Systems
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Looking Backward: Streaming Video-to-Video Translation with Feature Banks
This paper introduces StreamV2V, a diffusion model that achieves real-time streaming video-to-video (V2V) translation with user prompts.
FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research
With the advent of Large Language Models (LLMs), the potential of Retrieval Augmented Generation (RAG) techniques have garnered considerable research attention.
Pre-training Small Base LMs with Fewer Tokens
Here we show that smaller LMs trained utilizing some of the layers of GPT2-medium (355M) and GPT-2-large (770M) can effectively match the val loss of their bigger counterparts when trained from scratch for the same number of training steps on OpenWebText dataset with 9B tokens.
GraphAny: A Foundation Model for Node Classification on Any Graph
deepgraphlearning/graphany
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Traditional graph ML models such as graph neural networks (GNNs) trained on graphs cannot perform inference on a new graph with feature and label spaces different from the training ones.
