Honghua Zhang @HonghuaZhang2
Ph.D. student at UCLA StarAI Lab web.cs.ucla.edu/~hzhang19/ Joined April 2020-
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🚨LLM RESEARCHERS🚨Want a free boost in speed and memory efficiency for your HuggingFace🤗LLM with ZERO degradation in generation quality? Introducing Prepacking, a simple method to obtain up to 6x speedup and 16x memory efficiency gains in prefilling prompts of varying lengths.…
📢Please join me in congratulating Harold Li @LiLiunian for passing his Ph.D. defense today🎉 Harold has accomplished remarkable work and has been a pioneer in vision-language foundation models. 🧵 1/
Pretraining without Attention (arxiv.org/abs/2212.10544) - BiGS is alternative to BERT trained on up to 4096 tokens. Attention can be overkill. Below shows *every* word-word interaction for every sentence over 23 layers of BiGS (no heads, no n^2).
🚨Novel Generative Model:MoAT🚨 🤔Is it possible to learn a compact mixture over all possible (exponentially many) tree-shaped Markov Random Fields? Yes, MoATs can!😱 🧐But can you do it in a way that allows for polynomial time exact likelihood arxiv.org/abs/2302.14202…
🏆 𝐂𝐇𝐀𝐌𝐏𝐈𝐎𝐍𝐒 🏆
🎉🎉Oral at #NeurIPS2022 !🎉🎉 arxiv.org/abs/2205.13554 Masked models (MAEs, MLMs) are great for representation learning, but did you know they are secretly tractable probabilistic models? By unmasking one pixel/token at a time (chain rule), they give SOTA marginal likelihoods!
Happy to share our work on Controllable Text Generation with NeurAlly-Decomposed Oracle (NADO) accepted at #Neurips2022! Looks like our model is excited about NeurIPS as much as we do. See more in arxiv.org/abs/2205.14219
Do models know bride is in white in Americans wedding while bride usually wears in red in traditional Indian weddings? We design a brand new geo-diverse commonsense probing benchmark **GeoMLAMA** to evaluate model’s geo-diversity. 1/N Paper: wadeyin9712.github.io/files/emnlp202…
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4K Followers 1K Following human being | associate prof in #ML #AI @ancAtEd | PI of #APRIL https://t.co/7uTqRZtmEd | #probabilistic #models #tractable #generative #neuro #symbolic |
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14K Followers 958 Following Teaching computers to read. Assoc. prof @MITEECS / @MIT_CSAIL (he/him). https://t.co/5kCnXHjtlY https://t.co/2A3qF5vdJw
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Haoyi Qiu @HaoyiQiu
443 Followers 559 Following First year PhD student @UCLA 💙 BS in CS&Math @UMich〽️ #NLP 🌷
Xiao Liu @xxxxiaol
158 Followers 233 Following PhD student at PKU #NLProc | Prev Visiting Researcher at UCLA
Zixiang Chen @_zxchen_
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Junkai Zhang @JunkaiZZ
437 Followers 1K Following CS Ph.D. student @UCLA | Machine Learning, Generative Model, AI for Science | Previous Mathematics B.S. @Tsinghua
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Mingyu Derek MA @mingyu_ma
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SIDI LU @sidilu_pluslab
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cats with jobs 🛠 @CatWorkers
2.2M Followers 172 Following Good cats being workers • Unionized • Mascot @JobertTheCat • Takedown/credit/submissions: [email protected]
ACLRollingReview @ReviewAcl
5K Followers 62 Following ACL Rolling Review. Deadlines 10/15, 12/15, 2/15, 4/15 Tweets by @mayhewsw, @gneubig, @karmake2, @zeeraktalat, & others
NASA Artemis @NASAArtemis
755K Followers 112 Following With #Artemis, @NASA will land the first woman and first person of color on the Moon. Take the next giant leap with us. Verification: https://t.co/T7VkNRy6Nd🚨LLM RESEARCHERS🚨Want a free boost in speed and memory efficiency for your HuggingFace🤗LLM with ZERO degradation in generation quality? Introducing Prepacking, a simple method to obtain up to 6x speedup and 16x memory efficiency gains in prefilling prompts of varying lengths.…
Harold guided me step by step when I was an undergrad and just started research. He was so nice to share his valuable research experience to junior students and introduced so many chances to help me grow up 🚀. He also taught me a lot about how to do solid research and think of…
📢Please join me in congratulating Harold Li @LiLiunian for passing his Ph.D. defense today🎉 Harold has accomplished remarkable work and has been a pioneer in vision-language foundation models. 🧵 1/
📢Please join me in congratulating Harold Li @LiLiunian for passing his Ph.D. defense today🎉 Harold has accomplished remarkable work and has been a pioneer in vision-language foundation models. 🧵 1/
Excited to share that the checkpoints are online at 🤗 HuggingFace. Feel free to check it out! SDXL-DPO: huggingface.co/mhdang/dpo-sdx… SD1.5-DPO: huggingface.co/mhdang/dpo-sd1…
Excited to announce DPO has gone multi-modal! New paper out on RLHF for text-to-image diffusion models! We obtain large-scale state of the art results with 70% win rates against Stable Diffusion XL on human evals! Deep dive below 🧵
Spent a wonderful day at the #UAI2023 Workshop on Tractable Probabilistic Modeling learning about tractable inference algorithms! Enjoyed presenting my NeurIPS paper on any-order autoregressive models, and super happy to be recognized with Best Paper Honorable Mention 🥳
🎉🎉Oral at #NeurIPS2022 !🎉🎉 arxiv.org/abs/2205.13554 Masked models (MAEs, MLMs) are great for representation learning, but did you know they are secretly tractable probabilistic models? By unmasking one pixel/token at a time (chain rule), they give SOTA marginal likelihoods!
Duality on manifolds by @ronnybergmann_ at the DP4ML workshop #ICML2023
Is likelihood the right training objective for generative models? We study normalizing flows trained using a new kind of energy-based loss which *does not require computing expensive determinants of Jacobians*. We call these energy flows. #ICML2023
.@davidweichiang from @ND_CSE presenting his latest work characterizing the expressivity of transformer encoders! (by far my favorite paper from #ICML2023) arxiv.org/abs/2301.10743
Congrats @NikilSelvam and our collaborators @sunipa17, @DanielKhashabi, @tusharkhot for winning the outstanding paper award at #ACL2023. See details of the paper 👇 x.com/kaiwei_chang/s… @uclanlp
Outstanding Papers at #ACL2023NLP. Congrats to all authors!! 2023.aclweb.org/program/best_p… 📚 Backpack Language Models 🔎 CAME: Confidence-guided Adaptive Memory Efficient Optimization 🌍 Causes and Cures for Interference in Multilingual Translation [1/n]
Thrilled to receive the Outstanding Paper Award at #ACL2023 for ‘The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks’! 🥳 Thanks to my wonderful collaborators @sunipa17 @DanielKhashabi @tusharkhot @kaiwei_chang 💛 Paper👇 x.com/nikilselvam/st…
Congrats @NikilSelvam and our collaborators @sunipa17, @DanielKhashabi, @tusharkhot for winning the outstanding paper award at #ACL2023. See details of the paper 👇 x.com/kaiwei_chang/s… @uclanlp
Excited to share our new work DesCo (arxiv.org/pdf/2306.14060…) -- an instructing object detector that takes complex language descriptions (e.g., attributes & relations). DesCo improves zero-shot detection (+9.1 APr on LVIS) and ranks 1st at the #OmniLabel Challenge of CVPR2023!
Uncertainty quantification for neural networks via Bayesian model average is compelling, but uses just a few samples in practice😥 We propose 👾CIBER, a collapsed sampler to aggregate infinite NNs via volume computations, w/ better accuracy & calibration! arxiv.org/abs/2306.09686
I got my PhD!!!! 🎉🎉🎉🎉🎉
In building TART we were inspired by many great works around understanding ICL and reasoning over / programming transformers including work from folks like @sangmichaelxie, @tsiprasd, @DimitrisPapail, @shivamg_13, and @HonghuaZhang2.
Just successfully defended my PhD! 🎓 It's been an incredible journey for the past five years. Thanks to my advisor @kaiwei_chang and the committee members. I am excited about my next chapter in NLP research. 🎉🎉🎉
Pretraining without Attention (arxiv.org/abs/2212.10544) - BiGS is alternative to BERT trained on up to 4096 tokens. Attention can be overkill. Below shows *every* word-word interaction for every sentence over 23 layers of BiGS (no heads, no n^2).
👏👏
Led by Hal Daumé III (@haldaume3), the new Institute for Trustworthy Artificial Intelligence in Law & Society (TRAILS) aims to transform AI by prioritizing ethics, human rights, and input from communities who have been previously marginalized. Read more: go.umd.edu/trailslaunch
Completely agree. This counter culture (sorry, had to pun -- lame, I know) has been going on for a few years, and it's not only annoying, but misleading about what makes progress, both on the personal level and globally on research. If anything, slow down!
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