Sanjay Subramanian @sanjayssub
Building/analyzing NLP and vision models. PhD student @berkeley_ai. Formerly: @allen_ai, @penn people.eecs.berkeley.edu/~sanjayss/ Berkeley, CA Joined September 2019-
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Imitation learning works™ – but you need good data 🥹 How to get high-quality visuotactile demos from a bimanual robot with multifingered hands, and learn smooth policies? Check our new work “Learning Visuotactile Skills with Two Multifingered Hands”! 🙌 toruowo.github.io/hato/
New paper from @berkeley_ai on Autonomous Evaluation and Refinement of Digital Agents! We show that VLM/LLM-based evaluators can significantly improve the performance of agents for web browsing and device control, advancing sotas by 29% to 75%. arxiv.org/abs/2404.06474 [🧵]
Do brain representations of language depend on whether the inputs are pixels or sounds? Our @CommsBio paper studies this question from the perspective of language timescales. We find that representations are highly similar between modalities! rdcu.be/dACh5 1/8
From your cell phone to your TV, images and videos are now captured in 4K resolution or better. Vision methods, however, opt to downsize or crop them, losing information. We introduce xT, our framework to model large images end-to-end on contemporary GPUs! ai-climate.berkeley.edu/xt-website/
Achieving bimanual dexterity with RL + Sim2Real! toruowo.github.io/bimanual-twist/ TLDR - We train two robot hands to twist bottle lids using deep RL followed by sim-to-real. A single policy trained with simple simulated bottles can generalize to drastically different real-world objects.
Excited to release Based, an architecture that combines two✌️ simple, familiar, attention-like primitives – short (size-64) sliding window attention and softmax-approximating linear attention – to enable high quality and efficient inference! 💨 🚀 joint w/ @EyubogluSabri,…
If you know any HBCU students interested in research experiences in AI this summer. Tell them to check out the BAIR-HBCU REU program. Application open until end of month. bair.berkeley.edu/reu.html
Thrilled to see our work, “Modular Visual Question Answering via Code Generation” featured in Google’s year-in-review! w/ @sanjayssub @kushaltk1248 Kevin Yang @NagraniArsha @CordeliaSchmid @andyzeng_ @trevordarrell Dan Klein arxiv.org/abs/2306.05392
Thrilled to see our work, “Modular Visual Question Answering via Code Generation” featured in Google’s year-in-review! w/ @sanjayssub @kushaltk1248 Kevin Yang @NagraniArsha @CordeliaSchmid @andyzeng_ @trevordarrell Dan Klein arxiv.org/abs/2306.05392
Can you make a jigsaw puzzle with two different solutions? Or an image that changes appearance when flipped? We can do that, and a lot more, by using diffusion models to generate optical illusions! Continue reading for more illusions and method details 🧵
Can Language Models Learn to Listen? w/ Evonne Ng*, @sanjayssub*, Dan Klein, @trevordarrell, @shiryginosar From just text, you can generate listener motion👀: tinyurl.com/mru7uekf Thursday (tomorrow) morning poster session! video w/🔊:
Check out our #CoRL2023 (oral) project:🥡LERF-TOGO. Check out our website at lerftogo.github.io! We use LERF (lerf.io) for zero-shot semantic grasping so we can tell robots to grasp mugs by their handles and to grasp flowers by their stems!
Check out our #CoRL2023 (oral) project:🥡LERF-TOGO. Check out our website at lerftogo.github.io! We use LERF (lerf.io) for zero-shot semantic grasping so we can tell robots to grasp mugs by their handles and to grasp flowers by their stems!
Hi all prospective grad students! Our Equal Access to Application Assistance (EAAA) program for @Berkeley_EECS is now accepting applications! Any PhD applicant to @Berkeley_EECS can submit their application for feedback by Oct 8 2023: forms.gle/dHq2EPGrkkdcSu…
Check out our new work on generating a listener's facial expression and pose from the speaker's utterance!
Check out our new work on generating a listener's facial expression and pose from the speaker's utterance!
@sanjayssub @medhini_n @kushaltk1248 @NagraniArsha @CordeliaSchmid @trevordarrell @berkeley_ai @GoogleAI CodeVQA is a remarkable achievement in zero shot multimodal reasoning. Thank you for your contribution. We have featured your work in our latest blogpost: mitraaiblog.netlify.app/computer-visio…
Reminds of me of this wonderful talk about how to write a paper by @JitendraMalikCV: youtube.com/watch?v=imEtTn… where he talks at length about how to write the opening line. Highly recommend the full talk!!
Reminds of me of this wonderful talk about how to write a paper by @JitendraMalikCV: youtube.com/watch?v=imEtTn… where he talks at length about how to write the opening line. Highly recommend the full talk!!
Thanks to @sanjayssub who joined our open science community to present Visual Reasoning with Limited Human Labels. This event was a part of our Regional Asia group. Thanks to @AhmadMustafaAn1 and @ahmadbinshafiq for hosting. ✨ Catch the replay here:📺youtu.be/0h_I5uCsL9A
Excited to share some of my recent work this Friday! Join us!
Satellite images are captured from a variety of constellations, each with different resolutions. ML models must be fine-tuned to each satellite to work well. We present Scale-MAE, a pre-training method to make models robust to resolution. Accepted at ICCV! ai-climate.berkeley.edu/scale-mae-webs…
Can we better capture harms in text generation? Our #ACL2023 paper introduces FairPrism, a dataset with detailed human annotations on AI-generated text to diagnose fairness-related harms caused by text generation systems & flaws of mitigation tools🧵 📄bit.ly/fairprism
Akari Asai @AkariAsai
11K Followers 650 Following Ph.D. student @uwcse & @uwnlp. NLP. IBM Ph.D. fellow (2022-2023). Meta student researcher (2023-) . ☕️ 🐕 🏃♀️🧗♀️🍳Sam Bowman @sleepinyourhat
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8K Followers 560 Following PhD student @berkeley_ai @berkeleynlp working on interpretability and signed languages. Former @msftresearch @deepmind @carnegiemellon @polytechnique. 🇫🇷🇯🇵Christopher Manning @chrmanning
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3K Followers 848 Following Teaching AI policy at Columbia. Previously Director of AI for NYC. PhD from Stanford AI Lab. https://t.co/IWui2szqUuImitation learning works™ – but you need good data 🥹 How to get high-quality visuotactile demos from a bimanual robot with multifingered hands, and learn smooth policies? Check our new work “Learning Visuotactile Skills with Two Multifingered Hands”! 🙌 toruowo.github.io/hato/
Some personal updates: I joined OpenAI a few months ago, working on all things robustness/safety/privacy. Also, we are working to publish more of our safety work. See my first project here below, where we make initial progress on prompt injections and other attacks!
Introducing the Instruction Hierarchy, our latest safety research to advance robustness for prompt injections and other ways of tricking LLMs into executing unsafe actions. More details: arxiv.org/abs/2404.13208
What do you see in these images? These are called hybrid images, originally proposed by Aude Oliva et al. They change appearance depending on size or viewing distance, and are just one kind of perceptual illusion that our method, Factorized Diffusion, can make.
wow this must feel good when training (from Megalodon arxiv.org/abs/2404.08801 )
How can we leverage VLMs & 3D generative models to reconstruct hand-object interactions at scale?🤔 Introducing MCC-Hand-Object (MCC-HO) & Retrieval-Augmented Reconstruction (RAR)! Project & code: janehwu.github.io/mcc-ho Work w/ @geopavlakos @georgiagkioxari @JitendraMalikCV
New paper from @berkeley_ai on Autonomous Evaluation and Refinement of Digital Agents! We show that VLM/LLM-based evaluators can significantly improve the performance of agents for web browsing and device control, advancing sotas by 29% to 75%. arxiv.org/abs/2404.06474 [🧵]
SWE-agent is our new system for autonomously solving issues in GitHub repos. It gets similar accuracy to Devin on SWE-bench, takes 93 seconds on avg + it's open source! We designed a new agent-computer interface to make it easy for GPT-4 to edit+run code github.com/princeton-nlp/…
I wrote about what to do when you lose all hope during your phd in my blog: huiwenn.github.io/feynman
We found a bug that inflated performance. We withdrew the paper from CVPR and apologize for any confusion and inconvenience caused.
🔥New Paper Accepted at #CVPR2024 🔥 Using both text-to-image and image-to-text generative AI to synthesize multimodal training data. On the task of Multimodal Relation Extraction, our model trained on synthetic images and real text outperforms real data. arxiv.org/abs/2312.03025
LLMs can use complex instructions - why can’t retrieval models? We build FollowIR, a training/test set of real-world human retrieval instructions. Our FollowIR-7B is the best IR model for instruct-following, even beating @cohere @OpenAI retrievers 🤯 📝 arxiv.org/abs/2403.15246
Had fun visiting the MIT math department yesterday. A highlight was seeing fluid mechanics demos of hydrodynamic analogs of quantum and gravitational dynamics from John Bush’s lab (check out the video which is an analog of wave-particle duality) thales.mit.edu/bush/
Wanna know gpt-3.5-turbo's embed size? We find a way to extract info from LLM APIs and estimate gpt-3.5-turbo’s embed size to be 4096. With the same trick we also develop 25x faster logprob extraction, audits for LLM APIs, and more! 📄 arxiv.org/abs/2403.09539 Here’s how 1/🧵
Do brain representations of language depend on whether the inputs are pixels or sounds? Our @CommsBio paper studies this question from the perspective of language timescales. We find that representations are highly similar between modalities! rdcu.be/dACh5 1/8
🗣️ “Next-token predictors can’t plan!” ⚔️ “False! Every distribution is expressible as product of next-token probabilities!” 🗣️ In work w/ @GregorBachmann1 , we carefully flesh out this emerging, fragmented debate & articulate a key new failure. 🔴 arxiv.org/abs/2403.06963
From your cell phone to your TV, images and videos are now captured in 4K resolution or better. Vision methods, however, opt to downsize or crop them, losing information. We introduce xT, our framework to model large images end-to-end on contemporary GPUs! ai-climate.berkeley.edu/xt-website/
Achieving bimanual dexterity with RL + Sim2Real! toruowo.github.io/bimanual-twist/ TLDR - We train two robot hands to twist bottle lids using deep RL followed by sim-to-real. A single policy trained with simple simulated bottles can generalize to drastically different real-world objects.
Excited to release Based, an architecture that combines two✌️ simple, familiar, attention-like primitives – short (size-64) sliding window attention and softmax-approximating linear attention – to enable high quality and efficient inference! 💨 🚀 joint w/ @EyubogluSabri,…
Our robot now can learn to walk by simply predicting what happens next. This allows us to utilize huge amount of data without action labels (imagine learning to walk by watching a video of human walking). Check our website! …anoid-next-token-prediction.github.io
Humanoid Locomotion as Next Token Prediction We cast real-world humanoid control as a next token prediction problem, akin to predicting the next word in language. Our model is a causal transformer trained via autoregressive prediction of sensorimotor trajectories. To account for
we cast real-world humanoid control as next token prediction; our approach enables joint training with youtube videos and walks in sf
I'm so excited that our multimodal work from @berkeley_ai got accepted to #CVPR24! We present Compositional Chain-of-Thought, a zero-shot CoT approach that utilizes scene graph representations in order to extract compositional knowledge from an LMM. arxiv.org/abs/2311.17076