Yi Lin Sung @yilin_sung
CS PhD student @unccs @uncnlp | Previously intern @MetaAI @MSFTResearch | Multi-modal DL, Efficient fine-tuning. ylsung.github.io Chapel Hill, NC Joined March 2013-
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🚨 We have postdoc openings at UNC 🙂 Exciting+diverse NLP/CV/ML topics**, freedom to create research agenda, competitive funding, very strong students, many collabs w/ other faculty & universities+companies, superb quality of life/weather. Please apply + help spread the word…
Can we design an efficient & versatile framework to reuse+adapt existing pretrained ControlNets to accurately guide any video/image diffusion model and support diverse controls? 🚨 Introducing Ctrl-Adapter: ➡️ Flexible Compatibility: Adapts any pretrained ControlNet…
It was such a pleasure to appear on this podcast (which has literally hundreds of episodes with great AI folks)! Thanks to @samcharrington for a great conversation on the topics below👇
It was such a pleasure to appear on this podcast (which has literally hundreds of episodes with great AI folks)! Thanks to @samcharrington for a great conversation on the topics below👇
🚨 Introducing SegNext, our #CVPR2024 project, combining the best of specialist and generalist designs for interactive segmentation! ➡️ Low Latency ➡️ High Quality ➡️ Diverse Prompts Recent interactive segmentation methods are taking either one of two approaches: (1)…
🚨 Introducing SegNext, our #CVPR2024 project, combining the best of specialist and generalist designs for interactive segmentation! ➡️ Low Latency ➡️ High Quality ➡️ Diverse Prompts Recent interactive segmentation methods are taking either one of two approaches: (1)…
Gemini 1.5 Pro - A highly capable multimodal model with a 10M token context length Today we are releasing the first demonstrations of the capabilities of the Gemini 1.5 series, with the Gemini 1.5 Pro model. One of the key differentiators of this model is its incredibly long…
Stability AI presents SD3-Turbo Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation Diffusion models are the main driver of progress in image and video synthesis, but suffer from slow inference speed. Distillation methods, like the
🚨 EnvGen --> LLM iteratively decides+generates visual/embodied environments that are most effective to let the RL game agent play+learn in --> so as to automatically target/focus on progressively/adaptively improving the agent's weaker skills --> and hence very efficient…
🚨 EnvGen --> LLM iteratively decides+generates visual/embodied environments that are most effective to let the RL game agent play+learn in --> so as to automatically target/focus on progressively/adaptively improving the agent's weaker skills --> and hence very efficient…
Can we adaptively generate training environments with LLMs to help small embodied RL game agents learn useful skills that they are weak at? 🤔 👉 Check out EnvGen, an effective+efficient framework in which an LLM progressively generates and adapts training environments based on…
🎉Our work ADaPT on enabling LLM agents to dynamically “adapt” to task complexity & LLM capabilities via recursive decomposition is accepted as #NAACL2024 findings!😄 Many thanks to @alkoller M Hartmann, P Clark, @Ashish_S_AI @mohitban47 @tusharkhot @ai2_aristo @allen_ai @uncnlp
🎉Our work ADaPT on enabling LLM agents to dynamically “adapt” to task complexity & LLM capabilities via recursive decomposition is accepted as #NAACL2024 findings!😄 Many thanks to @alkoller M Hartmann, P Clark, @Ashish_S_AI @mohitban47 @tusharkhot @ai2_aristo @allen_ai @uncnlp
📢New Paper! We introduce EnvGen, a new effective and efficient framework for embodied RL game agents to adaptively learn their weak skills by using LLMs to generate useful/proper training environments! 😆 ▶️A lightweight (4M params) RL agent trained w/ EnvGen outperforms a…
📢New Paper! We introduce EnvGen, a new effective and efficient framework for embodied RL game agents to adaptively learn their weak skills by using LLMs to generate useful/proper training environments! 😆 ▶️A lightweight (4M params) RL agent trained w/ EnvGen outperforms a…
Introducing Evolutionary Model Merge: A new approach bringing us closer to automating foundation model development. We use evolution to find great ways of combining open-source models, building new powerful foundation models with user-specified abilities! sakana.ai/evolutionary-m…
Fine-tuning the LLaMA-2-Chat model may degrade its original capabilities (arxiv.org/abs/2401.03129). But here's a lifeline: Chat Vector (arxiv.org/abs/2310.04799) keeps a chat model's original capability (it also works on Mistral). Recommend to everyone fine-tuning their LLMs.
🚀Check out our #NAACL2024 paper on continual pre-training of language models (LMs)! In the real world, LMs need to unlearn/overwrite outdated information with updated ones. To evaluate the temporal adaptation capabilities of LMs, we introduce the temporally evolving QA…
Some really cool work by our soon-to-be-intern @JialuLi96 link: selma-t2i.github.io
Modern VidQA models are static, operating on fixed training datasets. In contrast, real-world applications demand adaptability to continually changing training domains. We present a parameter-efficient method for continual VidQA learning. arxiv.org/abs/2403.08755 🧵
➡️➡️How to improve multiple skills in Text-to-Image (T2I) models via self-training? 👉 SELMA 👉 -- Generates multiple skill-specific image-text pairs using LLM+T2I. -- Learns skill-specific LoRA experts in parallel (to minimize "knowledge interference" between skills). --…
➡️➡️How to improve multiple skills in Text-to-Image (T2I) models via self-training? 👉 SELMA 👉 -- Generates multiple skill-specific image-text pairs using LLM+T2I. -- Learns skill-specific LoRA experts in parallel (to minimize "knowledge interference" between skills). --…
Introducing SELMA! We teach multiple skills to a T2I model with the following recipes: (1) Automatically generate skill-specific image-text pairs with LLM + T2I (2) Learn skill-specific experts with LoRA in parallel (3) Merge the expert models to obtain a final multi-skill T2I…
Introducing SELMA! We teach multiple skills to a T2I model with the following recipes: (1) Automatically generate skill-specific image-text pairs with LLM + T2I (2) Learn skill-specific experts with LoRA in parallel (3) Merge the expert models to obtain a final multi-skill T2I…
SELMA improves T2I models' text faithfulness and has a better human preference ✅Skill-specific data generation ✅Efficient skill-specific LoRA fine-tuning & Merging ✅Boosting 5 metrics & human eval ✅Weak-to-strong generalization in T2I ✅Comparable performance with GT data
SELMA improves T2I models' text faithfulness and has a better human preference ✅Skill-specific data generation ✅Efficient skill-specific LoRA fine-tuning & Merging ✅Boosting 5 metrics & human eval ✅Weak-to-strong generalization in T2I ✅Comparable performance with GT data
Pointing to an image region should help models focus, but standard VLMs fail to understand visual markers/prompts (e.g., boxes/masks). 🚨Contrastive Region Guidance: Training-free method that increases focus on visual prompts by reducing model priors. arxiv.org/abs/2403.02325 🧵
Are unified VL models consistent across predictions for different tasks on the same image? Thrilled to share our @TmlrOrg paper where we find that VL models show significant cross-task inconsistency in their predictions for the same image across tasks. adymaharana.github.io/cococon/ 🧵
Colin Raffel @colinraffel
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Yichen Jiang @YichenJiang9
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156 Followers 210 Following Postdoc at @GeorgiaTech. Ph.D. from @UofIllinois. Focus on privacy + graph learning. #MachineUnlearning #DifferentialPrivacy #DP #GNN
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Haokun Liu @liu_haokun
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980K Followers 905 Following 🧑🍳. Previously Director of AI @ Tesla, founding team @ OpenAI, CS231n/PhD @ Stanford. I like to train large deep neural nets 🧠🤖💥
AI at Meta @AIatMeta
532K Followers 255 Following Together with the AI community, we are pushing the boundaries of what’s possible through open science to create a more connected world.
Yi Tay @YiTayML
29K Followers 97 Following chief scientist / cofounder @RekaAILabs 🫠 past: research scientist @google brain 🤯 currently learning to be a dad 🍼
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1K Followers 892 Following PhD student at @UNCCS @UNCNLP Previously at @GoogleAI, @MSFTResearch, @AdobeResearch, @Allen_AI, @official_naver, and @SeoulNatlUni
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Yoav Artzi @yoavartzi
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39K Followers 611 Following Cofounder @openai, lead post-training for ChatGPT and the API. Interested in reinforcement learning, alignment, birds, jazz music
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91K Followers 173 Following Reverse engineering neural networks at @AnthropicAI. DMs open! Previously @distillpub, OpenAI Clarity Team, Google Brain. Personal account.
Petar Veličković @PetarV_93
30K Followers 555 Following Staff Research Scientist @GoogleDeepMind | Affiliated Lecturer @Cambridge_Uni | Associate @clarehall_cam | GDL Scholar @ELLISforEurope. Monoids. 🇷🇸🇲🇪🇧🇦
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29K Followers 1K Following Open-Endedness Team Lead @GoogleDeepMind, Professor of AI @AI_UCL, PI @UCL_DARK, @ELLISforEurope Scholar. ex @MetaAI (FAIR), @CompSciOxford. Opinions my own.
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6K Followers 1K Following assoc. prof at @umasscs, member of @UMass_NLP. i work on natural language processing and deep learning
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6K Followers 1K Following Assistant Prof. at Georgia Tech @ICatGT, researcher at @NVIDIAAI | Ph.D. @StanfordAILab | Making robots smarter
Omar Sanseviero @osanseviero
32K Followers 2K Following Chief Llama Officer @huggingface 🦙 Founder @AI_Learners. Xoogler (SWE @Google Assistant, 20% PM TF Graphics). 100% Hacker Llama🇵🇪🇲🇽
Bolei Zhou @zhoubolei
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Taco Cohen @TacoCohen
21K Followers 3K Following Deep learner at FAIR. Into codegen, equivariance, generative models. Spent time at Qualcomm, Scyfer (acquired), UvA, Deepmind, OpenAI.
Zhiyu Zoey Chen @ZhiyuChen4
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Hang Zhao @zhaohang0124
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Ruohan Gao @RuohanGao1
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366 Followers 139 Following Associate Professor in the Graduate School of AI & School of Computing @ KAIST
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Sara Hooker @sarahookr
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Allie K. Miller @alliekmiller
49K Followers 2K Following #1 Most Followed Voice in AI Business (1.5M followers). Nat’l AAAS Ambassador. Former Amazon, IBM. Fortune 500 and startup AI advisor, public speaker.🚨 We have postdoc openings at UNC 🙂 Exciting+diverse NLP/CV/ML topics**, freedom to create research agenda, competitive funding, very strong students, many collabs w/ other faculty & universities+companies, superb quality of life/weather. Please apply + help spread the word…
We have a new preprint out - your language model is not a reward, it’s a Q function! 1. The likelihood of the preferred answer must go down - it’s a policy divergence 2. MCTS guided decoding on language is equivalent to likelihood search on DPO 3. DPO learns credit assignment
It’s here! Meet Llama 3, our latest generation of models that is setting a new standard for state-of-the art performance and efficiency for openly available LLMs. Key highlights • 8B and 70B parameter openly available pre-trained and fine-tuned models. • Trained on more…
Introducing 🔌Ctrl-Adapter🔌, our plug-and-play framework that reuses any existing ControlNet for any video/image diffusion model! ➡️ Plug-and-Play: Adapts any (smaller) ControlNet to any (larger) image (e.g., SDXL) or video diffusion (e.g., Stable Video Diffusion, I2VGen-XL,…
Can we design an efficient & versatile framework to reuse+adapt existing pretrained ControlNets to accurately guide any video/image diffusion model and support diverse controls? 🚨 Introducing Ctrl-Adapter: ➡️ Flexible Compatibility: Adapts any pretrained ControlNet…
🎉Excited to share our new Ctrl-Adapter framework which can efficiently adapt any existing pretrained ControlNet to any video/image diffusion model! In <10 hours of GPU training, Ctrl-Adapter can outperform baselines/methods. Ctrl-Adapter also enables a broad range of…
Can we design an efficient & versatile framework to reuse+adapt existing pretrained ControlNets to accurately guide any video/image diffusion model and support diverse controls? 🚨 Introducing Ctrl-Adapter: ➡️ Flexible Compatibility: Adapts any pretrained ControlNet…
Can we design an efficient & versatile framework to reuse+adapt existing pretrained ControlNets to accurately guide any video/image diffusion model and support diverse controls? 🚨 Introducing Ctrl-Adapter: ➡️ Flexible Compatibility: Adapts any pretrained ControlNet…
It was such a pleasure to appear on this podcast (which has literally hundreds of episodes with great AI folks)! Thanks to @samcharrington for a great conversation on the topics below👇
Today we're joined by @peterbhase from @uncnlp to discuss mechanistic interpretability, scalable oversight, and how matrix probing techniques begin to illuminate the "black box" of large neural networks. 🎧 / 🎥 Listen or watch the full episode at: twimlai.com/go/679. 📖…
Today we're joined by @peterbhase from @uncnlp to discuss mechanistic interpretability, scalable oversight, and how matrix probing techniques begin to illuminate the "black box" of large neural networks. 🎧 / 🎥 Listen or watch the full episode at: twimlai.com/go/679. 📖…
The next chapter about transformers is up on YouTube, digging into the attention mechanism: youtu.be/eMlx5fFNoYc The model works with vectors representing tokens (think words), and this is the mechanism that allows those vectors to take in meaning from context.
Introducing our #CVPR2024 paper 🔥 SegNext 🔥 We propose SegNext as a next-generation interactive image segmentation method to support all the following useful features: ➡️ low latency ➡️ high quality ➡️ diverse prompts arxiv.org/abs/2404.00741
🚨 Introducing SegNext, our #CVPR2024 project, combining the best of specialist and generalist designs for interactive segmentation! ➡️ Low Latency ➡️ High Quality ➡️ Diverse Prompts Recent interactive segmentation methods are taking either one of two approaches: (1)…
Introducing our #CVPR2024 paper 🔥 SegNext 🔥 We propose SegNext as a next-generation interactive image segmentation method to support all the following useful features: ➡️ low latency ➡️ high quality ➡️ diverse prompts arxiv.org/abs/2404.00741
🔊Check out our preprint, AVSiam 🤝. We use a single shared ViT to process audio and visual inputs, improving its parameter efficiency⚡, reducing the GPU memory footprint💻, and allowing us to scale📈 to larger datasets and model sizes. arxiv.org/abs/2403.19638 w. @gberta227 👇
``Siamese Vision Transformers are Scalable Audio-visual Learners,'' Yan-Bo Lin, Gedas Bertasius, ift.tt/feFJkwQ
Can we improve narrative closure with bookending, i.e., relating the last sentence back to the first sentence? We present RENarGen, a paradigm for both LMs and LLMs that generates narratives with related endpoint sentences. Accepted at #NAACL2024
Exciting new work with @AnnelieseB_ and @zhaochaocs on generating stories that provide narrative closure. It will be presented at NAACL'24 @UNCResearch @unccs @uncnlp
Can we improve narrative closure with bookending, i.e., relating the last sentence back to the first sentence? We present RENarGen, a paradigm for both LMs and LLMs that generates narratives with related endpoint sentences. Accepted at #NAACL2024
That's the exact opposite IMO! $10M to train a GPT3.5 level model whereas it probably cost OAI at least 10-20x more just a year or two ago. The more we improve as a field thanks to open-source, the cheaper & more efficient it gets to produce the same capabilities. Let's go…
Our team at Google DeepMind has a full-time Research Scientist position available at our Mountain View site. Minimum qualification: PhD in ML/NLP. Please email me with: your CV and Google Scholar link; a brief description of the impactful work you have done; and what you aim…
Introduce 3D-VLA, a 3D Generative World Model! Humans use mental models to predict and plan for the future. Similarly, 3D-VLA achieves this by linking 3D perception, future prediction, and action executions through a generative world model. vis-www.cs.umass.edu/3dvla
Should we acquire Stability and open-source SD3?
Our paper, "The LLM Surgeon," accepted at ICLR 2024, achieves SOTA in LLM pruning in all unstructured, semi-structured, and the most challenging but most effective structured pruning that removes entire matrix rows/columns. Happy to share that code is now publicly available.…
The LLM Surgeon paper page: huggingface.co/papers/2312.17… State-of-the-art language models are becoming increasingly large in an effort to achieve the highest performance on large corpora of available textual data. However, the sheer size of the Transformer architectures makes it…
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