Adeer Khan

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Adeer Khan

Adeer Khan

@AdeerKhan

KAIST | Machine Learning | Gen Ai | Digital Twin

Daejeon, Republic of Korea Katılım Ağustos 2019
543 Takip Edilen105 Takipçiler
Adeer Khan
Adeer Khan@AdeerKhan·
They introduce a large-scale synthetic dataset, SpatialGen Dataset, featuring 12,328 structured annotated scenes with 57,440 rooms, and 4.7M photorealistic 2D renderings.
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Qwen
Qwen@Alibaba_Qwen·
🎙️ Meet Qwen3-ASR — the all-in-one speech recognition model! ✅ High-accuracy EN/CN + 9 more languages: ar, de, en, es, fr, it, ja, ko, pt, ru, zh ✅ Auto language detection ✅ Songs? Raps? Voice with BGM? No problem. <8% WER ✅ Works in noise, low quality, far-field ✅ Custom context? Just paste ANY text — names, jargon, even gibberish 🧠 ✅ One model. Zero hassle.Great for edtech, media, customer service & more. API:bailian.console.alibabacloud.com/?tab=doc#/doc/… ModelScope Demo: modelscope.cn/studios/Qwen/Q… Hugging Face Demo: huggingface.co/spaces/Qwen/Qw… Blog:qwen.ai/blog?id=41e4c0…
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Cosmos Scholarship Network
Cosmos Scholarship Network@ObengCosmos·
PhD Opportunities in Energy Transport and Conversion - University of Maryland We are seeking highly motivated PhD students who are passionate about transforming the global energy landscape and tackling grand challenges in science and engineering.
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Emm | scenario.com
Emm | scenario.com@emmanuel_2m·
Generated isometric café
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MrNeRF
MrNeRF@janusch_patas·
GeoSplat: A Deep Dive into Geometry-Constrained Gaussian Splatting Abstract: A few recent works explored incorporating geometric priors to regularize the optimization of Gaussian splatting, further improving its performance. However, those early studies mainly focused on the use of low-order geometric priors (e.g., normal vector), which are unreliably estimated by noise-sensitive methods like local principal component analysis. To address their limitations, we first present GeoSplat, a general geometry-constrained optimization framework that exploits both first-order and second-order geometric quantities to improve the entire training pipeline of Gaussian splatting, including Gaussian initialization, gradient update, and densification. As an example, we initialize the scales of 3D Gaussian primitives in terms of principal curvatures, leading to a better coverage of the object surface than random initialization. Secondly, based on certain geometric structures (e.g., local manifold), we introduce efficient and noise-robust estimation methods that provide dynamic geometric priors for our framework. We conduct extensive experiments on multiple datasets for novel view synthesis, showing that our framework, GeoSplat, significantly improves the performance of Gaussian splatting and outperforms previous baselines.
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Cursor
Cursor@cursor_ai·
Cursor is now free for students. Enjoy!
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fofr
fofr@fofrAI·
Ideogram v3 again: > in swirling free-flowing words "Enjoy a slice of fofr's perfectly melted ai" written in beautiful stretched lettering with interesting interconnections and glyphs and ligatures, fills the space, repeating pattern of monstera leaves, all the letters effortlessly flow into each other
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ComfyUI
ComfyUI@ComfyUI·
📢 New Comfy Drop /w @SyntaxDiffusion + @yoland_yan 🎤 Host: @PurzBeats ⌚ May 6th - 5:30pm EST / 2:30pm PST 📍 YouTube, X, and Twitch Change of plans - something's coming.
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AK
AK@_akhaliq·
LLaMA-Omni2 is out on Hugging Face LLM-based Real-time Spoken Chatbot with Autoregressive Streaming Speech Synthesis
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Air Katakana
Air Katakana@airkatakana·
with claude on the $20 tier im coding for 30 mins at a time, reaching usage limits, and getting kicked off for 5 hours. no one works in 30 min sprints every 5 hours, this is completely unusable
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Adeer Khan
Adeer Khan@AdeerKhan·
After Flux, we get this!
Stability AI@StabilityAI

Stability AI 🤝 @ComfyUI since day one. Together, we've laid the foundation for one of the most vibrant communities in generative AI. Now, it's easier to join in. Stable Diffusion 3.5 Large and Stable Image Ultra are now available in ComfyUI's new native API nodes, making it ideal for users who want to utilize our advanced image generation capabilities — all within the UI they're already comfy with. You can learn more here: bit.ly/3GFkNHf

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Daniel Nguyen
Daniel Nguyen@daniel_nguyenx·
BoltAI — All AI models in one app.
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SUN YOUNG HWANG ᯅ 🇰🇷
SUN YOUNG HWANG ᯅ 🇰🇷@SOSOHAJALAB·
Love to see how beautifully run the QwQ-32B effectively via unsloth! Hope to see how fine-tune the QwQ-32B via @UnslothAI soon! 👍🤗
Daniel Han@danielhanchen

Having endless repetitions with QwQ-32B? I made a guide to help debug stuff! When using repetition penalties to counteract looping, it rather causes looping! Try adding this to llama.cpp: --samplers "top_k;top_p;min_p;temperature;dry;typ_p;xtc" I also uploaded dynamic 4bit quants & GGUFs. Thanks to @krist486 and @kalomaze for the discussions as well. The official recommended settings of: > temperature = 0.6 > top-k = 40 (20 to 40 suggested) > min-p = 0.1 (optional, but works well!) > top-p = 0.95 work well, but adding repetition-penalty = 1.1 and dry-multiplier 0.5 seem to work better. However naively adding it will break the model! Changing the ordering of samplers seems to work well! QwQ is also sensitive to quantization - the first and last few layers should be left unquantized, and I uploaded dynamic 4bit quants (works in vLLM inference natively and Unsloth finetuning / inference) to huggingface.co/unsloth/QwQ-32… I also uploaded GGUFs to huggingface.co/unsloth/QwQ-32… Full guide on running QwQ 32B: docs.unsloth.ai/basics/tutoria…

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