Trending Research
Baichuan 2: Open Large-scale Language Models
Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering.
3D Gaussian Splatting for Real-Time Radiance Field Rendering
graphdeco-inria/gaussian-splatting
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Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos.
OverFlow: Putting flows on top of neural transducers for better TTS
coqui-ai/TTS
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Neural HMMs are a type of neural transducer recently proposed for sequence-to-sequence modelling in text-to-speech.
Ranked #11 on
Text-To-Speech Synthesis
on LJSpeech
(using extra training data)
MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning
haozhezhao/mic
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Specifically, the current VLMs primarily emphasize utilizing multi-modal data with a single image some, rather than multi-modal prompts with interleaved multiple images and text.
LayoutNUWA: Revealing the Hidden Layout Expertise of Large Language Models
Graphic layout generation, a growing research field, plays a significant role in user engagement and information perception.
InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation
Leveraging our new pipeline, we create, to the best of our knowledge, the first one-step diffusion-based text-to-image generator with SD-level image quality, achieving an FID (Frechet Inception Distance) of $23. 3$ on MS COCO 2017-5k, surpassing the previous state-of-the-art technique, progressive distillation, by a significant margin ($37. 2$ $\rightarrow$ $23. 3$ in FID).
GPTFUZZER : Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts
Notably, even starting with suboptimal seed templates, \fuzzer maintains over 90\% attack success rate against ChatGPT and Llama-2 models.
Focused Transformer: Contrastive Training for Context Scaling
This novel approach enhances the structure of the (key, value) space, enabling an extension of the context length.
EfficientViT: Lightweight Multi-Scale Attention for On-Device Semantic Segmentation
Unlike prior semantic segmentation models that rely on heavy self-attention, hardware-inefficient large-kernel convolution, or complicated topology structure to obtain good performances, our lightweight multi-scale attention achieves a global receptive field and multi-scale learning (two critical features for semantic segmentation models) with only lightweight and hardware-efficient operations.
Ranked #19 on
Semantic Segmentation
on Cityscapes val
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
To tackle this issue, we introduce an Omnidirectionally calibrated Quantization (OmniQuant) technique for LLMs, which achieves good performance in diverse quantization settings while maintaining the computational efficiency of PTQ by efficiently optimizing various quantization parameters.
